diff --git a/.gitignore b/.gitignore index 97db4436..d2b7f1e3 100644 --- a/.gitignore +++ b/.gitignore @@ -102,13 +102,13 @@ celerybeat.pid *.sage.py # Environments -.env -.venv -env/ -venv/ +.env* +.venv/ +.venv*/ ENV/ env.bak/ venv.bak/ +bak/ # Spyder project settings .spyderproject diff --git a/README.md b/README.md index 72728cdd..2ddec810 100644 --- a/README.md +++ b/README.md @@ -60,6 +60,14 @@ Test the whole backend pipeline without the GUI using this command ```sh python trapdata/tests/test_pipeline.py +# or +ami test pipeline +``` + +Run all other tests with: + +```sh +ami test all ``` ## GUI Usage @@ -149,19 +157,58 @@ A script is available in the repo source to run the commands above. -## KG Notes for adding new models +## Adding new models -- To add new models, save the pt and json files to: - ``` - ~/Library/Application Support/trapdata/models - ``` - or wherever you set the appropriate dir in settings. - The json file is simply a dict of species name and index. +1) Create a new inference class in `trapdata/ml/models/classification.py` or `trapdata/ml/models/localization.py`. All models inherit from `InferenceBaseClass`, but there are more specific classes for classification and localization and different architectures. Choose the appropriate class to inherit from. It's best to copy an existing inference class that is similar to the new model you are adding. + +2) Upload your model weights and category map to a cloud storage service and make sure the file is publicly accessible via a URL. The weights will be downloaded the first time the model is run. Alternatively, you can manually add the model weights to the configured `USER_DATA_PATH` directory under the subdir `USER_DATA_PATH/models/` (on macOS this is `~/Library/Application Support/trapdata/models`). However the model will not be available to other users unless they also manually add the model weights. The category map json file is simply a dict of species names and their indexes in your model's last layer. See the existing category maps for examples. + +3) Select your model in the GUI settings or set the `SPECIES_CLASSIFICATION_MODEL` setting. If the model inherits from `SpeciesClassifier` class, it will automatically become one of the valid choices. - Then you need to create a class in `trapdata/ml/models/classification.py` or `trapdata/ml/models/localization.py` and add the model details. +## Clearing the cache & starting fresh -- To clear the cache: +Remove the index of images, all detections and classifications by removing the database file. This will not remove the images themselves, only the metadata about them. The database is located in the user data directory. +On macOS: ``` - rm ~/Library/Application\ Support/trapdata/trapdata.db - ``` \ No newline at end of file +rm ~/Library/Application\ Support/trapdata/trapdata.db +``` + +On Linux: +``` +rm ~/.config/trapdata/trapdata.db +``` + +On Windows: +``` +del %AppData%\trapdata\trapdata.db +``` + +## Running the web API + +The model inference pipeline can be run as a web API using FastAPI. This is what the Antenna platform uses to process images. + +To run the API, use the following command: + +```sh +ami api +``` + +View the interactive API docs at http://localhost:2000/ + + +## Web UI demo (Gradio) + +A simple web UI is also available to test the inference pipeline. This is a quick way to test models on a remote server via a web browser. + +```sh +ami gradio +``` + +Open http://localhost:7861/ + +Use ngrok to temporarily expose localhost to the internet: + +```sh +ngrok http 7861 +``` diff --git a/gradio.conf b/gradio.conf new file mode 100644 index 00000000..f2550357 --- /dev/null +++ b/gradio.conf @@ -0,0 +1,11 @@ +[program:gradio] +directory=/home/ubuntu/ami-data-companion +command=/home/ubuntu/ami-data-companion/.venv/bin/ami gradio +autostart=true +autorestart=true +# stopsignal=KILL +stopasgroup=true +killasgroup=true +stderr_logfile=/var/log/gradio.err.log +stdout_logfile=/var/log/gradio.out.log +# process_name=%(program_name)s_%(process_num)02d diff --git a/poetry.lock b/poetry.lock index e23ec7cc..7e32b9bb 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,14 +1,25 @@ # This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. +[[package]] +name = "aiofiles" +version = "23.2.1" +description = "File support for 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b/pyproject.toml @@ -13,12 +13,11 @@ requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" [tool.poetry.dependencies] -python = "^3.9" +python = "^3.10" pillow = "^9.5.0" python-dateutil = "^2.8.2" python-dotenv = "^1.0.0" -pydantic = "^1.10.7" -typer = "^0.7.0" +pydantic = "^2.5.0" rich = "^13.3.3" pandas = "^1.5.3" sqlalchemy = ">2.0" @@ -27,8 +26,8 @@ alembic = "^1.10.2" psycopg2-binary = { version = "^2.9.5", optional = true } sentry-sdk = "^1.18.0" imagesize = "^1.4.1" -torch = "^2.0.0" -torchvision = "^0.15.1" +torch = "^2.1.0" +torchvision = "^0.16.0" timm = "^0.6.13" structlog = "^22.3.0" kivy = { extras = ["base"], version = "^2.3.0" } @@ -45,6 +44,14 @@ ipython = "^8.11.0" pytest-cov = "^4.0.0" pytest-asyncio = "^0.21.0" pytest = "*" +numpy = "^1.26.2" +pip = "^23.3.1" +pydantic-settings = "^2.1.0" +boto3 = "^1.33.0" +botocore = "^1.33.0" +mypy-boto3-s3 = "^1.29.7" +typer = "^0.12.3" +gradio = "^4.41.0" [tool.pytest.ini_options] diff --git a/scripts/start_db_container.sh b/scripts/start_db_container.sh index 307e5acb..abc8586a 100755 --- a/scripts/start_db_container.sh +++ b/scripts/start_db_container.sh @@ -4,7 +4,7 @@ set -o errexit set -o nounset CONTAINER_NAME=ami-db -HOST_PORT=5432 +HOST_PORT=5433 POSTGRES_VERSION=14 POSTGRES_DB=ami @@ -12,5 +12,5 @@ docker run -d -i --name $CONTAINER_NAME -v "$(pwd)/db_data":/var/lib/postgresql/ docker logs ami-db --tail 100 -echo 'Database started, Connection string: "postgresql://postgres@localhost:5432/ami"' +echo "Database started, Connection string: \"postgresql://postgres@localhost:${HOST_PORT}/${POSTGRES_DB}\"" echo "Stop (and destroy) database with 'docker stop $CONTAINER_NAME' && docker remove $CONTAINER_NAME" diff --git a/trapdata/__init__.py b/trapdata/__init__.py index b820b00a..af703e1e 100644 --- a/trapdata/__init__.py +++ b/trapdata/__init__.py @@ -1,17 +1,18 @@ import sentry_sdk +from .common import constants, utils +from .common.logs import logger +from .db.models.detections import DetectedObject +from .db.models.events import MonitoringSession +from .db.models.images import TrapImage + sentry_sdk.init( dsn="https://d2f65f945fe343669bbd3be5116d5922@o4503927026876416.ingest.sentry.io/4503927029497856", traces_sample_rate=1.0, ) -# + # import multiprocessing -from .common import constants, utils -from .common.logs import logger -from .db.models.detections import DetectedObject -from .db.models.events import MonitoringSession -from .db.models.images import TrapImage __all__ = [ logger, diff --git a/trapdata/api/__init__.py b/trapdata/api/__init__.py new file mode 100644 index 00000000..25f8e659 --- /dev/null +++ b/trapdata/api/__init__.py @@ -0,0 +1,3 @@ +from trapdata.settings import read_settings + +settings = read_settings() diff --git a/trapdata/api/api.py b/trapdata/api/api.py new file mode 100644 index 00000000..7c0f8100 --- /dev/null +++ b/trapdata/api/api.py @@ -0,0 +1,194 @@ +""" +Fast API interface for processing images through the localization and classification pipelines. +""" + +import enum +import time + +import fastapi +import pydantic +from rich import print + +from ..common.logs import logger # noqa: F401 +from . import settings +from .models.classification import ( + APIMothClassifier, + MothClassifierBinary, + MothClassifierGlobal, + MothClassifierPanama, + MothClassifierPanama2024, + MothClassifierQuebecVermont, + MothClassifierTuringAnguilla, + MothClassifierTuringCostaRica, + MothClassifierUKDenmark, +) +from .models.localization import APIMothDetector +from .schemas import Detection, SourceImage + +app = fastapi.FastAPI() + + +class SourceImageRequest(pydantic.BaseModel): + model_config = pydantic.ConfigDict(extra="ignore") + + # @TODO bring over new SourceImage & b64 validation from the lepsAI repo + id: str + url: str + # b64: str | None = None + + +class SourceImageResponse(pydantic.BaseModel): + model_config = pydantic.ConfigDict(extra="ignore") + + id: str + url: str + + +PIPELINE_CHOICES = { + "panama_moths_2023": MothClassifierPanama, + "panama_moths_2024": MothClassifierPanama2024, + "quebec_vermont_moths_2023": MothClassifierQuebecVermont, + "uk_denmark_moths_2023": MothClassifierUKDenmark, + "costa_rica_moths_turing_2024": MothClassifierTuringCostaRica, + "anguilla_moths_turing_2024": MothClassifierTuringAnguilla, + "global_moths_2024": MothClassifierGlobal, +} +_pipeline_choices = dict(zip(PIPELINE_CHOICES.keys(), list(PIPELINE_CHOICES.keys()))) + + +PipelineChoice = enum.Enum("PipelineChoice", _pipeline_choices) + + +class PipelineRequest(pydantic.BaseModel): + pipeline: PipelineChoice + source_images: list[SourceImageRequest] + + +class PipelineResponse(pydantic.BaseModel): + pipeline: PipelineChoice + total_time: float + source_images: list[SourceImageResponse] + detections: list[Detection] + + +@app.get("/") +async def root(): + return fastapi.responses.RedirectResponse("/docs") + + +@app.post("/pipeline/process") +@app.post("/pipeline/process/") +async def process(data: PipelineRequest) -> PipelineResponse: + # Ensure that the source images are unique, filter out duplicates + source_images_index = { + source_image.id: source_image for source_image in data.source_images + } + incoming_source_images = list(source_images_index.values()) + if len(incoming_source_images) != len(data.source_images): + logger.warning( + f"Removed {len(data.source_images) - len(incoming_source_images)} duplicate source images" + ) + + source_image_results = [ + SourceImageResponse(**image.model_dump()) for image in incoming_source_images + ] + source_images = [ + SourceImage(**image.model_dump()) for image in incoming_source_images + ] + + start_time = time.time() + detector = APIMothDetector( + source_images=source_images, + batch_size=settings.localization_batch_size, + num_workers=settings.num_workers, + # single=True if len(source_images) == 1 else False, + single=True, # @TODO solve issues with reading images in multiprocessing + ) + detector_results = detector.run() + num_pre_filter = len(detector_results) + + filter = MothClassifierBinary( + source_images=source_images, + detections=detector_results, + batch_size=settings.classification_batch_size, + num_workers=settings.num_workers, + # single=True if len(detector_results) == 1 else False, + single=True, # @TODO solve issues with reading images in multiprocessing + filter_results=False, # Only save results with the positive_binary_label, @TODO make this configurable from request + ) + filter.run() + # all_binary_classifications = filter.results + + # Compare num detections with num moth detections + num_post_filter = len(filter.results) + logger.info( + f"Binary classifier returned {num_post_filter} out of {num_pre_filter} detections" + ) + + # Filter results based on positive_binary_label + moth_detections = [] + non_moth_detections = [] + for detection in filter.results: + for classification in detection.classifications: + if classification.classification == filter.positive_binary_label: + moth_detections.append(detection) + elif classification.classification == filter.negative_binary_label: + non_moth_detections.append(detection) + break + + logger.info( + f"Sending {len(moth_detections)} out of {num_pre_filter} detections to the classifier" + ) + + Classifier = PIPELINE_CHOICES[data.pipeline.value] + classifier: APIMothClassifier = Classifier( + source_images=source_images, + detections=moth_detections, + batch_size=settings.classification_batch_size, + num_workers=settings.num_workers, + # single=True if len(filtered_detections) == 1 else False, + single=True, # @TODO solve issues with reading images in multiprocessing + ) + classifier.run() + end_time = time.time() + seconds_elapsed = float(end_time - start_time) + + # Return all detections, including those that were not classified as moths + all_detections = classifier.results + non_moth_detections + + logger.info( + f"Processed {len(source_images)} images in {seconds_elapsed:.2f} seconds" + ) + logger.info(f"Returning {len(all_detections)} detections") + print(all_detections) + + # If the number of detections is greater than 100, its suspicious. Log it. + if len(all_detections) > 100: + logger.warning( + f"Detected {len(all_detections)} detections. This is suspicious and may contain duplicates." + ) + + response = PipelineResponse( + pipeline=data.pipeline, + source_images=source_image_results, + detections=all_detections, + total_time=seconds_elapsed, + ) + return response + + +# Future methods + +# batch processing +# async def process_batch(data: PipelineRequest) -> PipelineResponse: +# pass + +# render image crops and bboxes on top of the original image +# async def render(data: PipelineRequest) -> PipelineResponse: +# pass + + +if __name__ == "__main__": + import uvicorn + + uvicorn.run(app, host="0.0.0.0", port=2000) diff --git a/trapdata/api/datasets.py b/trapdata/api/datasets.py new file mode 100644 index 00000000..9493e313 --- /dev/null +++ b/trapdata/api/datasets.py @@ -0,0 +1,89 @@ +import typing + +import torch +import torch.utils.data +import torchvision + +from trapdata.common.logs import logger + +from .schemas import Detection, SourceImage + + +class LocalizationImageDataset(torch.utils.data.Dataset): + def __init__( + self, + source_images: typing.Iterable[SourceImage], + image_transforms: torchvision.transforms.Compose, + batch_size: int = 1, + ): + super().__init__() + self.source_images: list[SourceImage] = list(source_images) + self.image_transforms: torchvision.transforms.Compose = image_transforms + self.batch_size: int = batch_size + + def __len__(self): + return len(list(self.source_images)) + + def __getitem__(self, idx): + worker_info = torch.utils.data.get_worker_info() + logger.info(f"Using worker: {worker_info}") + + source_image: SourceImage = self.source_images[idx] + image_data = source_image.open() + if not image_data: + return None + + image_data = self.image_transforms(image_data) + + # ids_batch = torch.utils.data.default_collate([source_image.id]) + # image_batch = torch.utils.data.default_collate([image_data]) + + # logger.info(f"Batch data: {ids_batch}, {image_batch}") + + # return (ids_batch, image_batch) + return source_image.id, image_data + + +class ClassificationImageDataset(torch.utils.data.Dataset): + def __init__( + self, + source_images: typing.Iterable[SourceImage], + detections: typing.Iterable[Detection], + image_transforms: torchvision.transforms.Compose, + batch_size: int = 1, + ): + super().__init__() + self.detections = list(detections) + self.image_transforms: torchvision.transforms.Compose = image_transforms + self.batch_size: int = batch_size + self.source_images: dict[str, SourceImage] = { + img.id: img for img in source_images + } + + def __len__(self): + # Append all detections to a single list + return len(self.detections) + + def __getitem__(self, idx): + worker_info = torch.utils.data.get_worker_info() + logger.info(f"Using worker: {worker_info}") + + detection_idx = idx + detection: Detection = self.detections[idx] + source_image = self.source_images[detection.source_image_id] + image_data = source_image.open() + if not image_data: + return None + bbox = detection.bbox + coords = bbox.x1, bbox.y1, bbox.x2, bbox.y2 + assert all(coord is not None for coord in coords) + image_data = image_data.crop(coords) # type: ignore + image_data = self.image_transforms(image_data) + + # ids_batch = torch.utils.data.default_collate([source_image.id]) + # image_batch = torch.utils.data.default_collate([image_data]) + + # logger.info(f"Batch data: {ids_batch}, {image_batch}") + + # return (ids_batch, image_batch) + return (source_image.id, detection_idx), image_data diff --git a/trapdata/api/demo.py b/trapdata/api/demo.py new file mode 100644 index 00000000..d87e9967 --- /dev/null +++ b/trapdata/api/demo.py @@ -0,0 +1,171 @@ +import typing + +import gradio as gr +import PIL.Image +import PIL.ImageDraw +from rich import print + +from .models.classification import ( + APIMothClassifier, + MothClassifierBinary, + MothClassifierGlobal, + MothClassifierPanama, + MothClassifierPanama2024, + MothClassifierQuebecVermont, + MothClassifierTuringAnguilla, + MothClassifierTuringCostaRica, + MothClassifierUKDenmark, +) +from .models.localization import APIMothDetector +from .schemas import SourceImage +from .tests import get_test_images + + +def predict(*img_paths, Classifier: typing.Type[APIMothClassifier]): + # img_paths = img_paths * 4 # Test batch size + source_images = [ + SourceImage(id=str(i), filepath=img_path) + for i, img_path in enumerate(img_paths) + ] + # Print size of each source image + for source_image in source_images: + source_image.open(raise_exception=True) + assert source_image._pil is not None + print(source_image, source_image._pil.size) + + if len(source_images) == 1: + detector = APIMothDetector( + source_images=source_images, + single=True, + ) + else: + detector = APIMothDetector( + source_images=source_images, + single=False, + ) + detector.run() + all_detections = detector.results + + # Filter out non-moths + filter = MothClassifierBinary( + source_images=source_images, + detections=all_detections, + filter_results=True, + batch_size=20, + ) + filter.run() + filtered_detections = filter.results + + classifier = Classifier( + source_images=source_images, + detections=filtered_detections, + batch_size=20, + ) + classifier.run() + + # ASSUME SINGLE IMAGE + assert len(source_images) == 1 + source_image = source_images[0] + source_image.open(raise_exception=True) + assert source_image._pil is not None + + # Draw bounding boxes on image + print("Drawing bounding boxes on source image") + annotated_image = source_image._pil.copy() + canvas = PIL.ImageDraw.Draw(annotated_image) + colors = { + MothClassifierBinary.positive_binary_label: "green", + MothClassifierBinary.negative_binary_label: "red", + } + default_color = "black" + for pred in filtered_detections: + # Draw rectangle on PIL Image + assert pred.bbox is not None + coords = ( + pred.bbox.x1, + pred.bbox.y1, + pred.bbox.x2, + pred.bbox.y2, + ) + # First classification is the binary one + binary_label = pred.classifications[0].labels[0] + color = colors.get(binary_label, default_color) + canvas.rectangle(coords, outline=color, width=8) + + # Create PIL Images: + print("Rendering crops") + crops = [] + for pred in classifier.results: + bbox = pred.bbox + assert bbox is not None + coords = (round(bbox.x1), round(bbox.y1), round(bbox.x2), round(bbox.y2)) + assert source_image._pil is not None + crop = source_image._pil.crop(coords) + # Last classification is the most specific one + labels = pred.classifications[-1].labels + scores = pred.classifications[-1].scores + top_label_with_score = f"{labels[0]} ({scores[0]:.2f})" + crops.append((crop, top_label_with_score)) + + print("Returning results") + return annotated_image, crops + + +from functools import partial + + +def make_interface( + Classifier: typing.Type[APIMothClassifier], + example_images_subdirs: typing.Iterable[str] = (), +): + return gr.Interface( + title=Classifier.name, + description=Classifier.description, + fn=partial(predict, Classifier=Classifier), # type: ignore + inputs=[gr.Image(type="filepath", label="Source Image")], + outputs=[ + gr.Image(label="Annotated Source Image"), + gr.Gallery(label="Classified Crops"), + ], + examples=[ + img.filepath for img in get_test_images(subdirs=example_images_subdirs) + ], + ) + + +app = gr.TabbedInterface( + [ + make_interface(MothClassifierPanama, ["panama"]), + make_interface(MothClassifierPanama2024, ["panama"]), + make_interface(MothClassifierUKDenmark, ["denmark"]), + make_interface(MothClassifierQuebecVermont, ["vermont"]), + make_interface(MothClassifierTuringCostaRica, ["panama"]), + make_interface(MothClassifierTuringAnguilla, ["panama"]), + make_interface( + MothClassifierGlobal, + [ + "vermont", + "panama", + "denmark", + ], + ), + ], + [ + "Panama 2023", + "Panama 2024", + "UK/Denmark", + "Quebec/Vermont", + "Costa Rica", + "Anguilla", + "Global", + ], +) + +if __name__ == "__main__": + app.launch( + debug=True, + show_error=True, + show_api=False, + server_name="0.0.0.0", + server_port=7861, + ) diff --git a/trapdata/api/models/__init__.py b/trapdata/api/models/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/trapdata/api/models/base.py b/trapdata/api/models/base.py new file mode 100644 index 00000000..d2cc9dfa --- /dev/null +++ b/trapdata/api/models/base.py @@ -0,0 +1,11 @@ +class APIInferenceBaseClass: + """ + Override methods and properties in the InferenceBaseClass that + are needed or not needed for the API version. + """ + + def __init__(self, *args, **kwargs): + # Don't need to set these for API version + kwargs["db_path"] = None + kwargs["image_base_path"] = None + super().__init__(*args, **kwargs) diff --git a/trapdata/api/models/classification.py b/trapdata/api/models/classification.py new file mode 100644 index 00000000..89a9f1db --- /dev/null +++ b/trapdata/api/models/classification.py @@ -0,0 +1,192 @@ +import datetime +import typing + +import numpy as np +import torch + +from trapdata.common.logs import logger +from trapdata.ml.models.classification import ( + GlobalMothSpeciesClassifier, + InferenceBaseClass, + MothNonMothClassifier, + PanamaMothSpeciesClassifier2024, + PanamaMothSpeciesClassifierMixedResolution2023, + QuebecVermontMothSpeciesClassifier2024, + TuringAnguillaSpeciesClassifier, + TuringCostaRicaSpeciesClassifier, + UKDenmarkMothSpeciesClassifier2024, +) + +from ..datasets import ClassificationImageDataset +from ..schemas import Classification, Detection, SourceImage +from .base import APIInferenceBaseClass + + +class APIMothClassifier( + APIInferenceBaseClass, + InferenceBaseClass, +): + def __init__( + self, + source_images: typing.Iterable[SourceImage], + detections: typing.Iterable[Detection], + *args, + **kwargs, + ): + self.source_images = source_images + self.detections = list(detections) + self.results: list[Detection] = [] + super().__init__(*args, **kwargs) + logger.info( + f"Initialized {self.__class__.__name__} with {len(self.detections)} detections" + ) + + def get_dataset(self): + return ClassificationImageDataset( + source_images=self.source_images, + detections=self.detections, + image_transforms=self.get_transforms(), + batch_size=self.batch_size, + ) + + def post_process_batch(self, output, top_n=3): + predictions = torch.nn.functional.softmax(output, dim=1) + predictions = predictions.cpu().numpy() + + # Ensure that top_n is not greater than the number of categories + # (e.g. binary classification will have only 2 categories) + top_n = min(top_n, predictions.shape[1]) + + top_n_indices = np.argpartition(predictions, -top_n, axis=1)[:, -top_n:] + top_n_scores = predictions[ + np.arange(predictions.shape[0])[:, None], top_n_indices + ] + top_n_labels = np.array( + [[self.category_map[i] for i in row] for row in top_n_indices] + ) + + result = [ + list(zip(labels, scores)) + for labels, scores in zip(top_n_labels, top_n_scores) + ] + result = [sorted(items, key=lambda x: x[1], reverse=True) for items in result] + logger.debug(f"Post-processing result batch: {result}") + return result + + def save_results( + self, metadata, batch_output, seconds_per_item, *args, **kwargs + ) -> list[Detection]: + image_ids = metadata[0] + detection_idxes = metadata[1] + for image_id, detection_idx, predictions in zip( + image_ids, detection_idxes, batch_output + ): + detection = self.detections[detection_idx] + assert detection.source_image_id == image_id + classification = Classification( + classification=predictions[0][0], + labels=[label for (label, _) in list(predictions)], + scores=[score for (_, score) in list(predictions)], + inference_time=seconds_per_item, + algorithm=self.name, + timestamp=datetime.datetime.now(), + ) + self.update_classification(detection, classification) + + self.results = self.detections + logger.info(f"Saving {len(self.results)} detections with classifications") + return self.results + + def update_classification( + self, detection: Detection, new_classification: Classification + ) -> None: + # Remove all existing classifications from this algorithm + detection.classifications = [ + c for c in detection.classifications if c.algorithm != self.name + ] + # Add the new classification for this algorithm + detection.classifications.append(new_classification) + logger.debug( + f"Updated classification for detection {detection.bbox}. Total classifications: {len(detection.classifications)}" + ) + + def run(self) -> list[Detection]: + logger.info( + f"Starting {self.__class__.__name__} run with {len(self.results)} detections" + ) + super().run() + logger.info( + f"Finished {self.__class__.__name__} run. Processed {len(self.results)} detections" + ) + return self.results + + +class MothClassifierBinary(APIMothClassifier, MothNonMothClassifier): + def __init__(self, *args, **kwargs): + self.filter_results = kwargs.get("filter_results", True) + super().__init__(*args, **kwargs) + + def save_results( + self, metadata, batch_output, seconds_per_item, *args, **kwargs + ) -> list[Detection]: + """ + Override the base class method to save only the results that have the + label we are interested in. + """ + logger.info(f"Saving {len(batch_output)} detections with classifications") + image_ids = metadata[0] + detection_idxes = metadata[1] + for image_id, detection_idx, predictions in zip( + image_ids, detection_idxes, batch_output + ): + detection = self.detections[detection_idx] + assert detection.source_image_id == image_id + classification = Classification( + classification=predictions[0][0], + labels=[label for (label, _) in list(predictions)], + scores=[score for (_, score) in list(predictions)], + inference_time=seconds_per_item, + algorithm=self.name, + timestamp=datetime.datetime.now(), + # Specific to binary classification / the filter model + terminal=False, + ) + self.update_classification(detection, classification) + + self.results = self.detections + logger.info(f"Saving {len(self.results)} detections with classifications") + return self.results + + +class MothClassifierPanama( + APIMothClassifier, PanamaMothSpeciesClassifierMixedResolution2023 +): + pass + + +class MothClassifierPanama2024(APIMothClassifier, PanamaMothSpeciesClassifier2024): + pass + + +class MothClassifierUKDenmark(APIMothClassifier, UKDenmarkMothSpeciesClassifier2024): + pass + + +class MothClassifierQuebecVermont( + APIMothClassifier, QuebecVermontMothSpeciesClassifier2024 +): + pass + + +class MothClassifierTuringCostaRica( + APIMothClassifier, TuringCostaRicaSpeciesClassifier +): + pass + + +class MothClassifierTuringAnguilla(APIMothClassifier, TuringAnguillaSpeciesClassifier): + pass + + +class MothClassifierGlobal(APIMothClassifier, GlobalMothSpeciesClassifier): + pass diff --git a/trapdata/api/models/localization.py b/trapdata/api/models/localization.py new file mode 100644 index 00000000..9fa8d35f --- /dev/null +++ b/trapdata/api/models/localization.py @@ -0,0 +1,59 @@ +import concurrent.futures +import datetime +import typing + +from trapdata.ml.models.localization import MothObjectDetector_FasterRCNN_2023 + +from ..datasets import LocalizationImageDataset +from ..schemas import BoundingBox, Detection, SourceImage +from .base import APIInferenceBaseClass + + +class APIMothDetector(APIInferenceBaseClass, MothObjectDetector_FasterRCNN_2023): + def __init__(self, source_images: typing.Iterable[SourceImage], *args, **kwargs): + self.source_images = source_images + self.results: list[Detection] = [] + super().__init__(*args, **kwargs) + + def get_dataset(self): + return LocalizationImageDataset( + self.source_images, self.get_transforms(), batch_size=self.batch_size + ) + + def get_source_image(self, source_image_id: int) -> SourceImage: + for source_image in self.source_images: + if source_image.id == source_image_id: + return source_image + raise ValueError(f"Source image with id {source_image_id} not found") + + def save_results(self, item_ids, batch_output, seconds_per_item, *args, **kwargs): + detections: list[Detection] = [] + + def save_detection(image_id, coords): + bbox = BoundingBox(x1=coords[0], y1=coords[1], x2=coords[2], y2=coords[3]) + detection = Detection( + source_image_id=image_id, + bbox=bbox, + inference_time=seconds_per_item, + algorithm=self.name, + timestamp=datetime.datetime.now(), + crop_image_url=None, + ) + return detection + + with concurrent.futures.ThreadPoolExecutor() as executor: + futures = [] + for image_id, image_output in zip(item_ids, batch_output): + for coords in image_output: + future = executor.submit(save_detection, image_id, coords) + futures.append(future) + + for future in concurrent.futures.as_completed(futures): + detection = future.result() + detections.append(detection) + + self.results += detections + + def run(self) -> list[Detection]: + super().run() + return self.results diff --git a/trapdata/api/queries.py b/trapdata/api/queries.py new file mode 100644 index 00000000..02d567ab --- /dev/null +++ b/trapdata/api/queries.py @@ -0,0 +1,102 @@ +from trapdata import logger + +from . import settings +from .auth import get_session +from .schemas import SourceImage + +TEMPORARY_DEPLOYMENT_ID = 9 +TEMPORARY_EVENT_ID = 34 +TEMPORARY_COLLECTION_ID = 4 + + +def fetch_source_image_data(id: int): + path = f"captures/{id}/" + url = settings.api_base_url + path + resp = get_session().get(url) + resp.raise_for_status() + data = resp.json() + return SourceImage(**data) + + +# @TODO If this is being initiated from a POST request with the source image info, then we can +# return the results in the same response. At least single images. +def save_detected_objects( + source_image_ids: list[int], detected_objects_data: list[dict], *args, **kwargs +): + logger.info(f"Saving {len(source_image_ids)} detected objects via API") + print(f"Saving {len(source_image_ids)} detected objects via API") + responses = {} + path = "detections/" + + for source_image_id, detected_objects in zip( + source_image_ids, detected_objects_data + ): + for detected_object in detected_objects: + data = {} + data["bbox"] = detected_object["bbox"] + # data["source_image_id"] = source_image_id + data["source_image"] = ( + settings.api_base_url + f"captures/{source_image_id}/" + ) + data[ + "detection_algorithm_id" + ] = 2 # https://api.dev.insectai.org/api/v2/ml/algorithms/2/ + resp = get_session().post(settings.api_base_url + path, json=data) + resp.raise_for_status() + data = resp.json() + logger.info( + f"Saved detected object {data['details']} with width {data['width']} and height {data['height']}" + ) + responses[source_image_id] = data + return responses + + +def get_next_source_images(num: int, *args, **kwargs) -> list[SourceImage]: + path = "captures/" + args = { + "limit": num, + # "deployment": TEMPORARY_DEPLOYMENT_ID, + # "event": TEMPORARY_EVENT_ID, + "collections": TEMPORARY_COLLECTION_ID, + "has_detections": False, + "order": "?", + } # last_processed__isnull=True + url = settings.api_base_url + path + resp = get_session().get(url, params=args) + resp.raise_for_status() + data = resp.json() + source_images = [SourceImage(**item) for item in data["results"]] + return source_images + + +def get_source_image_count(*args, **kwargs) -> int: + path = "captures/" + args = { + # "deployment": TEMPORARY_DEPLOYMENT_ID, + # "event": TEMPORARY_EVENT_ID, + "collections": TEMPORARY_COLLECTION_ID, + "limit": 1, + "has_detections": False, + } + url = settings.api_base_url + path + resp = get_session().get(url, params=args) + print(resp.url) + resp.raise_for_status() + data = resp.json() + count = data["count"] + logger.info(f"Images remaining to process: {count}") + return count + + +def get_totals(project_id: int, *args, **kwargs): + path = "status/summary" + params = {"project": project_id} + url = settings.api_base_url + path + resp = get_session().get(url, params=params) + resp.raise_for_status() + data = resp.json() + return data + + +class QueueManager: + pass diff --git a/trapdata/api/schemas.py b/trapdata/api/schemas.py new file mode 100644 index 00000000..fb176d7c --- /dev/null +++ b/trapdata/api/schemas.py @@ -0,0 +1,86 @@ +# Can these be imported from the OpenAPI spec yaml? +import datetime +import pathlib + +import PIL.Image +import pydantic + +from trapdata.common.logs import logger +from trapdata.ml.utils import get_image + + +class BoundingBox(pydantic.BaseModel): + x1: float + y1: float + x2: float + y2: float + + @classmethod + def from_coords(cls, coords: list[float]): + return cls(x1=coords[0], y1=coords[1], x2=coords[2], y2=coords[3]) + + def to_string(self): + return f"{self.x1},{self.y1},{self.x2},{self.y2}" + + def to_path(self): + return "-".join([str(int(x)) for x in [self.x1, self.y1, self.x2, self.y2]]) + + def to_tuple(self): + return (self.x1, self.y1, self.x2, self.y2) + + +class SourceImage(pydantic.BaseModel): + model_config = pydantic.ConfigDict(extra="ignore", arbitrary_types_allowed=True) + + id: str + url: str | None = None + b64: str | None = None + filepath: str | pathlib.Path | None = None + _pil: PIL.Image.Image | None = None + width: int | None = None + height: int | None = None + timestamp: datetime.datetime | None = None + + # Validate that there is at least one of the following fields + @pydantic.model_validator(mode="after") + def validate_source(self): + if not any([self.url, self.b64, self.filepath, self._pil]): + raise ValueError( + "At least one of the following fields must be provided: url, b64, filepath, pil" + ) + return self + + def open(self, raise_exception=False) -> PIL.Image.Image | None: + if not self._pil: + logger.warn(f"Opening image {self.id} for the first time") + self._pil = get_image( + url=self.url, + b64=self.b64, + filepath=self.filepath, + raise_exception=raise_exception, + ) + else: + logger.info(f"Using already loaded image {self.id}") + if self._pil: + self.width, self.height = self._pil.size + return self._pil + + +class Classification(pydantic.BaseModel): + classification: str + labels: list[str] = [] + scores: list[float] = [] + inference_time: float | None = None + algorithm: str | None = None + terminal: bool = True + timestamp: datetime.datetime + + +class Detection(pydantic.BaseModel): + source_image_id: str + bbox: BoundingBox + inference_time: float | None = None + algorithm: str | None = None + timestamp: datetime.datetime + crop_image_url: str | None = None + classifications: list[Classification] = [] diff --git a/trapdata/api/service.conf b/trapdata/api/service.conf new file mode 100644 index 00000000..745b39c1 --- /dev/null +++ b/trapdata/api/service.conf @@ -0,0 +1,14 @@ +[program:ami] +directory=/home/debian/ami-data-companion +command=/home/debian/miniconda3/bin/ami api +autostart=true +autorestart=true +# stopsignal=KILL +stopasgroup=true +killasgroup=true +stderr_logfile=/var/log/ami.err.log +stdout_logfile=/var/log/ami.out.log +# process_name=%(program_name)s_%(process_num)02d +environment=HOME="/home/debian",USER="debian" +user=debian + diff --git a/trapdata/api/test_api.py b/trapdata/api/test_api.py new file mode 100644 index 00000000..610f82f7 --- /dev/null +++ b/trapdata/api/test_api.py @@ -0,0 +1,281 @@ +import collections +import datetime +import logging +import pathlib +import tempfile +import typing +import unittest +from unittest import TestCase + +import PIL.Image +import pytest + +from trapdata.common.filemanagement import find_images +from trapdata.tests import TEST_IMAGES_BASE_PATH + +from .models.classification import MothClassifierBinary, MothClassifierQuebecVermont +from .models.localization import APIMothDetector +from .schemas import BoundingBox, Detection, SourceImage + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +TEST_IMAGE_SIZE = (100, 100) +TEST_BASE64_IMAGES = { + # 10x10 pixel images + "RED": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mP8z8BQz0AEYBxVSF+FABJADveWkH6oAAAAAElFTkSuQmCC", + "GREEN": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mNk+M9Qz0AEYBxVSF+FAAhKDveksOjmAAAAAElFTkSuQmCC", + "BLUE": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mNkYPhfz0AEYBxVSF+FAP5FDvcfRYWgAAAAAElFTkSuQmCC", + "BROWSER_STRING": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mNkYPhfz0AEYBxVSF+FAP5FDvcfRYWgAAAAAElFTkSuQmCC=", # noqa +} + + +def get_empty_test_images(read: bool = False): + assert len(TEST_BASE64_IMAGES) > 0, "No empty test images found" + images = [SourceImage(id=name, b64=b64) for name, b64 in TEST_BASE64_IMAGES.items()] + if read: + for image in images: + image.open(raise_exception=True) + return list(images) + + +def get_empty_detections(): + # Return one large detection for each image + # @TODO Also test zero sized box = [0, 0, 0, 0] + return [ + Detection( + source_image_id=img.id, + bbox=BoundingBox.from_coords([0, 0, img.width, img.height]), # type: ignore + algorithm="Full width and height", + timestamp=datetime.datetime.now(), + ) + for img in get_empty_test_images(read=True) + ] + + +def make_image(): + # Create a fake test image and save to temporary filepath + + img = PIL.Image.new("RGB", TEST_IMAGE_SIZE, color="red") # type: ignore + with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as f: + img.save(f.name) + return f.name + + +def get_test_images( + subdirs: typing.Iterable[str] = ("vermont", "panama"), limit: int = 6 +) -> list[SourceImage]: + return [ + SourceImage(id=str(img["path"].name), filepath=img["path"]) + for subdir in subdirs + for img in find_images(pathlib.Path(TEST_IMAGES_BASE_PATH) / subdir) + ][:limit] + + +def check_for_duplicate_classifications(results: list[Detection]): + """ + Ensure that there is only one classification per classifier, per bounding box + """ + result_counts = collections.defaultdict(int) + for result in results: + for classification in result.classifications: + bbox = result.bbox.to_tuple() + unique_result = tuple(list(bbox) + [classification.algorithm]) + result_counts[unique_result] += 1 + + duplicates = {k: v for k, v in result_counts.items() if v > 1} + assert not duplicates, f"Duplicate detections found: {duplicates}" + + +class TestLocalization(TestCase): + def test_localization_zero(self): + detector = APIMothDetector(source_images=get_empty_test_images()) + detector.run() + results = detector.results + self.assertEqual(len(results), 0) + + def test_localization(self): + test_images = get_test_images() + detector = APIMothDetector( + source_images=test_images, + ) + detector.run() + results = detector.results + + # Results are not grouped by image, all detections are in one list + results_image_ids = {det.source_image_id for det in results} + all_test_image_ids = {img.id for img in test_images} + + # If an image has no detections, it is not included in the results + # Check that results_image_ids is a subset of all_test_image_ids + assert set(results_image_ids).issubset(all_test_image_ids) + + # @TODO ensure bounding boxes are correct + + for detection in detector.results: + assert isinstance( + detection, Detection + ), "Detection result is not a Detection object" + self.assertIn(detection.source_image_id, all_test_image_ids) + + +class TestClassification(TestCase): + def get_detections(self, test_images: list[SourceImage]) -> list[Detection]: + # @TODO Reuse the results from the localization test. Or provide serialized results. + detector = APIMothDetector( + source_images=test_images, + ) + detector.run() + return detector.results + + def filter_detections( + self, + test_images: list[SourceImage], + detections: list[Detection], + ) -> list[Detection]: + # Filter detections based on results of the binary classifier + classifier = MothClassifierBinary( + source_images=test_images, + detections=detections, + filter_results=True, + ) + classifier.run() + filtered_detections = classifier.results + self.assertLessEqual(len(filtered_detections), len(detections)) + return filtered_detections + + def test_classification_zero(self): + classifier = MothClassifierQuebecVermont( + source_images=get_empty_test_images(), + detections=get_empty_detections(), + ) + classifier.run() + results = classifier.results + print(results) + self.assertGreater(len(results), 0) + # Assert that all results have very low scores + for result in results: + for classification in result.classifications: + self.assertLessEqual(classification.scores[0], 0.4) + + @pytest.mark.skip( + reason="The new binary classifier is classifying empty images as moths" + ) + def test_binary_classification_zero(self): + # @TODO + # This is classifying empty images as moths! + + classifier = MothClassifierBinary( + source_images=get_empty_test_images(), + detections=get_empty_detections(), + ) + classifier.run() + results = classifier.results + self.assertGreater(len(results), 0) + # Assert that all results are predicted negative and have very high scores + for result in results: + for classification in result.classifications: + self.assertEqual( + classification.classification, + MothClassifierBinary.negative_binary_label, + ) + self.assertLessEqual(classification.scores[0], 0.9) + + def test_binary_classification(self): + test_images = get_test_images() + detections = self.get_detections(test_images) + classifier = MothClassifierBinary( + source_images=test_images, + detections=detections, + ) + classifier.run() + results = classifier.results + + check_for_duplicate_classifications(results) + + self.assertEqual(len(results), len(detections)) + for result in results: + for classification in result.classifications: + self.assertIn( + classification.classification, + ( + MothClassifierBinary.positive_binary_label, + MothClassifierBinary.negative_binary_label, + ), + ) + # @TODO ensure classification results are correct + + def test_classification(self): + test_images = get_test_images() + detections = self.get_detections(test_images) + detections = self.filter_detections(test_images, detections) + classifier = MothClassifierQuebecVermont( + source_images=test_images, + detections=detections, + ) + classifier.run() + results = classifier.results + # image_lookup = {img.id: img for img in test_images} + self.assertEqual(len(results), len(detections)) + + check_for_duplicate_classifications(results) + + # Assert that each result has at least one classification + for result in results: + self.assertGreater( + len(result.classifications), 0, f"{result} has no classifications" + ) + + # @TODO ensure classification results are correct + + +class TestSourceImageSchema(TestCase): + def setUp(self) -> None: + super().setUp() + self.test_image = make_image() + + def test_filepath(self): + filepath = self.test_image + source_image = SourceImage(id="1", filepath=filepath) + self.assertEqual(source_image.filepath, filepath) + img = source_image.open() + self.assertIsNotNone(img) + assert img is not None # For type hinting + self.assertEqual(img.size, TEST_IMAGE_SIZE) + img.close() + + def test_url(self): + # Don't trust placeholder image services + url = "https://upload.wikimedia.org/wikipedia/en/thumb/8/80/Wikipedia-logo-v2.svg/103px-Wikipedia-logo-v2.svg.png" + source_image = SourceImage(id="1", url=url) + self.assertEqual(source_image.url, url) + img = source_image.open() + self.assertIsNotNone(img) + assert img is not None + self.assertEqual(img.size, (103, 94)) + img.close() + + def test_bad_base64(self): + base64_string = "happy birthday" + source_image = SourceImage(id="1", b64=base64_string) + from binascii import Error + + with self.assertRaises(Error): + source_image.open(raise_exception=True) + + def _test_base64(self, base64_string): + source_image = SourceImage(id="1", b64=base64_string) + img = source_image.open(raise_exception=True) + self.assertIsNotNone(img) + assert img is not None + self.assertEqual(img.size, (10, 10)) + img.close() + + def test_base64_images(self): + for image in TEST_BASE64_IMAGES.values(): + self._test_base64(image) + + +if __name__ == "__main__": + unittest.main() diff --git a/trapdata/api/utils.py b/trapdata/api/utils.py new file mode 100644 index 00000000..2c2678a4 --- /dev/null +++ b/trapdata/api/utils.py @@ -0,0 +1,35 @@ +import pathlib +import time + +import PIL.Image + +from ..common.utils import slugify +from .schemas import BoundingBox, SourceImage + + +def render_crop(source_image: SourceImage, bbox: BoundingBox | None) -> PIL.Image.Image: + """ """ + source_image.open(raise_exception=True) + assert source_image._pil is not None + if not bbox: + # Use full image + return source_image._pil + coords = (bbox.x1, bbox.y1, bbox.x2, bbox.y2) + return source_image._pil.crop(coords) # type: ignore + + +def render_crops( + source_image: SourceImage, bboxes: list[BoundingBox] +) -> list[PIL.Image.Image]: + """ + Is there an efficient way to crop multiple boxes from a single image using numpy? + """ + raise NotImplementedError + + +def get_crop_fname(source_image: SourceImage, bbox: BoundingBox) -> str: + assert source_image.url, "Auto naming only works for images with a URL" + source_name = slugify(pathlib.Path(source_image.url).stem) + bbox_name = bbox.to_path() + timestamp = int(time.time()) # @TODO use pipeline name/version instead + return f"{source_name}/{bbox_name}-{timestamp}.jpg" diff --git a/trapdata/cli/base.py b/trapdata/cli/base.py index b124b86f..616c645d 100644 --- a/trapdata/cli/base.py +++ b/trapdata/cli/base.py @@ -63,5 +63,38 @@ def run_pipeline(): ) +@cli.command("api-pipeline") +def run_api_pipeline(source_image_ids: list[int]): + """ + Process all images via the AMI platform API. + """ + from trapdata.api.pipeline import start_pipeline as start_api_pipeline + + start_api_pipeline( + source_image_ids=source_image_ids, + settings=settings, + ) + + +@cli.command("gradio") +def run_gradio(): + """ + Run the gradio interface. + """ + from trapdata.api.demo import app + + app.queue().launch(show_api=False, server_name="0.0.0.0", server_port=7861) + + +@cli.command("api") +def run_api(): + """ + Run the API. + """ + import uvicorn + + uvicorn.run("trapdata.api.api:app", host="0.0.0.0", port=2000, reload=True) + + if __name__ == "__main__": cli() diff --git a/trapdata/cli/test.py b/trapdata/cli/test.py index d63f2841..e2c33b17 100644 --- a/trapdata/cli/test.py +++ b/trapdata/cli/test.py @@ -1,10 +1,11 @@ import datetime +import subprocess import sys -import pytest import typer from rich import print from sqlalchemy import select + from trapdata.cli import settings from trapdata.db.base import check_db, get_session_class from trapdata.db.models import MonitoringSession @@ -20,7 +21,9 @@ def all(): Run pytest tests. Would like this to be the default command. """ # return_code = pytest.main(["--doctest-modules", "-v", "."]) - return_code = pytest.main(["-v", "."]) + # return_code = pytest.main(["-v", "."]) + + return_code = subprocess.call(["pytest", "-v", "."]) sys.exit(return_code) diff --git a/trapdata/db/models/detections.py b/trapdata/db/models/detections.py index 9ed55330..c195da66 100644 --- a/trapdata/db/models/detections.py +++ b/trapdata/db/models/detections.py @@ -4,7 +4,7 @@ import PIL.Image import sqlalchemy as sa -from pydantic import BaseModel +from pydantic import BaseModel, ConfigDict from sqlalchemy import orm from trapdata import constants, db @@ -28,6 +28,9 @@ class DetectionListItem(BaseModel): in_queue: bool notes: Optional[str] + # PyDantic complains because we have an attribute called `model_name` + model_config = ConfigDict(protected_namespaces=[]) # type:ignore + class DetectionDetail(DetectionListItem): deployment: Optional[str] @@ -507,9 +510,7 @@ def get_species_for_image(db_path, image_id): def num_species_for_event( db_path, monitoring_session, classification_threshold: float = 0.6 ) -> int: - query = sa.select( - sa.func.count(DetectedObject.specific_label.distinct()), - ).where( + query = sa.select(sa.func.count(DetectedObject.specific_label.distinct()),).where( (DetectedObject.specific_label_score >= classification_threshold) & (DetectedObject.monitoring_session == monitoring_session) ) @@ -521,9 +522,7 @@ def num_species_for_event( def num_occurrences_for_event( db_path, monitoring_session, classification_threshold: float = 0.6 ) -> int: - query = sa.select( - sa.func.count(DetectedObject.sequence_id.distinct()), - ).where( + query = sa.select(sa.func.count(DetectedObject.sequence_id.distinct()),).where( (DetectedObject.specific_label_score >= classification_threshold) & (DetectedObject.monitoring_session == monitoring_session) ) diff --git a/trapdata/ml/models/base.py b/trapdata/ml/models/base.py index 40b45ad5..c43ce6b8 100644 --- a/trapdata/ml/models/base.py +++ b/trapdata/ml/models/base.py @@ -1,6 +1,7 @@ import json from typing import Union +import numpy as np import sqlalchemy import torch import torch.utils.data @@ -125,7 +126,9 @@ def get_key(cls): def get_weights(self, weights_path): if weights_path: return get_or_download_file( - weights_path, self.user_data_path, prefix="models" + weights_path, + self.user_data_path or torch.hub.get_dir(), + prefix="models", ) else: logger.warn(f"No weights specified for model {self.name}") @@ -133,7 +136,9 @@ def get_weights(self, weights_path): def get_labels(self, labels_path): if labels_path: local_path = get_or_download_file( - labels_path, self.user_data_path, prefix="models" + labels_path, + self.user_data_path or torch.hub.get_dir(), + prefix="models", ) with open(local_path) as f: @@ -145,12 +150,24 @@ def get_labels(self, labels_path): Taxon IDs are helpful for looking up additional information about the species such as the genus and family. """ + import concurrent.futures + from trapdata.ml.utils import replace_gbif_id_with_name - string_labels = {} - for label, index in labels.items(): - string_label = replace_gbif_id_with_name(label) - string_labels[string_label] = index + def fetch_gbif_ids(labels): + string_labels = {} + with concurrent.futures.ThreadPoolExecutor() as executor: + futures = [] + for label, _index in labels.items(): + future = executor.submit(replace_gbif_id_with_name, label) + futures.append(future) + for future, (_label, index) in zip(futures, labels.items()): + string_label = future.result() + string_labels[string_label] = index + + return string_labels + + string_labels = fetch_gbif_ids(labels) logger.info(f"Replacing GBIF IDs with names in {local_path}") # Backup the original file @@ -230,15 +247,27 @@ def get_dataloader(self): logger.info( f"Preparing dataloader with batch size of {self.batch_size} and {self.num_workers} workers." ) - self.dataloader = torch.utils.data.DataLoader( - self.dataset, - num_workers=0 if self.single else self.num_workers, - persistent_workers=False if self.single else True, - shuffle=False, - pin_memory=False if self.single else True, # @TODO review this - batch_size=None, # Recommended setting for streaming datasets - batch_sampler=None, # Recommended setting for streaming datasets - ) + dataloader_args = { + "num_workers": 0 if self.single else self.num_workers, + "persistent_workers": False if self.single else True, + "shuffle": False, + "pin_memory": False if self.single else True, # @TODO review this + } + if isinstance(self.dataset, torch.utils.data.IterableDataset): + # Batch size and sample should be None for streaming datasets + dataloader_args.update( + { + "batch_size": None, + "batch_sampler": None, + } + ) + else: + dataloader_args.update( + { + "batch_size": self.batch_size, + } + ) + self.dataloader = torch.utils.data.DataLoader(self.dataset, **dataloader_args) return self.dataloader def predict_batch(self, batch): @@ -258,7 +287,9 @@ def post_process_batch(self, batch_output): # for item in batch_output: # yield self.post_process_single(item) - def save_results(self, item_ids, batch_output): + def save_results( + self, item_ids, batch_output, seconds_per_item: float | None = None + ): logger.warn("No save method configured for model. Doing nothing with results") return None @@ -291,9 +322,11 @@ def run(self): ) batch_output = list(self.post_process_batch(batch_output)) - item_ids = item_ids.tolist() - logger.info(f"Saving {len(item_ids)} results") - self.save_results(item_ids, batch_output) + if isinstance(item_ids, (np.ndarray, torch.Tensor)): + item_ids = item_ids.tolist() + logger.info(f"Saving results from {len(item_ids)} items") + + self.save_results(item_ids, batch_output, seconds_per_item=seconds_per_item) logger.info(f"{self.name} Batch -- Done") logger.info(f"{self.name} -- Done") diff --git a/trapdata/ml/models/classification.py b/trapdata/ml/models/classification.py index 2813683e..8735885d 100644 --- a/trapdata/ml/models/classification.py +++ b/trapdata/ml/models/classification.py @@ -109,7 +109,6 @@ def forward(self, x): class Resnet50Classifier_Turing(InferenceBaseClass): # function to run the Turing models - logger.info("KG: Resnet50Classifier_Turing") input_size = 300 def get_model(self): @@ -162,12 +161,25 @@ def get_model(self): model.eval() return model + def _pad_to_square(self): + """Padding transformation to make the image square""" + width, height = self.input_size, self.input_size + if height < width: + return torchvision.transforms.Pad(padding=[0, 0, 0, width - height]) + elif height > width: + return torchvision.transforms.Pad(padding=[0, 0, height - width, 0]) + else: + return torchvision.transforms.Pad(padding=[0, 0, 0, 0]) + def get_transforms(self): mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225] return torchvision.transforms.Compose( [ - torchvision.transforms.Resize((self.input_size, self.input_size)), + self._pad_to_square(), torchvision.transforms.ToTensor(), + torchvision.transforms.Resize( + (self.input_size, self.input_size), antialias=True # type: ignore + ), torchvision.transforms.Normalize(mean, std), ] ) @@ -207,21 +219,24 @@ def get_model(self): class Resnet50TimmClassifier(Resnet50Classifier): def get_model(self): model = timm.create_model( - "resnet50", pretrained=False, num_classes=self.num_classes + "resnet50", + pretrained=False, + num_classes=self.num_classes, ) assert self.weights, f"No weights path configured for {self.name}" checkpoint = torch.load(self.weights, map_location=self.device) state_dict = checkpoint.get("model_state_dict") or checkpoint model.load_state_dict(state_dict) + model.to(self.device) model.eval() return model -class BinaryClassifier(EfficientNetClassifier): +class BinaryClassifier(Resnet50ClassifierLowRes): stage = 2 type = "binary_classification" - positive_binary_label = None - positive_negative_label = None + positive_binary_label: str = constants.POSITIVE_BINARY_LABEL + negative_binary_label: str = constants.NEGATIVE_BINARY_LABEL def get_queue(self) -> DetectedObjectQueue: return DetectedObjectQueue(self.db_path, self.image_base_path) @@ -234,13 +249,13 @@ def get_dataset(self): ) return dataset - def save_results(self, object_ids, batch_output): + def save_results(self, object_ids, batch_output, *args, **kwargs): # Here we are saving the moth/non-moth labels classified_objects_data = [ { "binary_label": str(label), "binary_label_score": float(score), - "in_queue": True if label == constants.POSITIVE_BINARY_LABEL else False, + "in_queue": True if label == self.positive_binary_label else False, "model_name": self.name, } for label, score in batch_output @@ -248,13 +263,28 @@ def save_results(self, object_ids, batch_output): save_classified_objects(self.db_path, object_ids, classified_objects_data) -class MothNonMothClassifier(BinaryClassifier): +class MothNonMothClassifier2022(EfficientNetClassifier, BinaryClassifier): name = "Moth / Non-Moth Classifier" description = "Trained on May 6, 2022" weights_path = "https://object-arbutus.cloud.computecanada.ca/ami-models/moths/classification/moth-nonmoth-effv2b3_20220506_061527_30.pth" labels_path = "https://object-arbutus.cloud.computecanada.ca/ami-models/moths/classification/05-moth-nonmoth_category_map.json" positive_binary_label = "moth" - positive_negative_label = "nonmoth" + negative_binary_label = "nonmoth" + + +class MothNonMothClassifier(BinaryClassifier): + name = "Moth / Non-Moth Classifier" + description = "Trained on April 17, 2024" + weights_path = ( + "https://object-arbutus.cloud.computecanada.ca/ami-models/moths/classification/" + "moth-nonmoth_resnet50_20240417_b4fe3efe.pth" + ) + labels_path = ( + "https://object-arbutus.cloud.computecanada.ca/ami-models/moths/classification/" + "05-moth-nonmoth_category_map.json" + ) + positive_binary_label = "moth" + negative_binary_label = "nonmoth" class SpeciesClassifier(InferenceBaseClass): @@ -272,7 +302,7 @@ def get_dataset(self): ) return dataset - def save_results(self, object_ids, batch_output): + def save_results(self, object_ids, batch_output, *args, **kwargs): # Here we are saving the specific taxon labels classified_objects_data = [ { @@ -316,6 +346,13 @@ class TuringCostaRicaSpeciesClassifier(SpeciesClassifier, Resnet50Classifier_Tur ) +class TuringAnguillaSpeciesClassifier(SpeciesClassifier, Resnet50Classifier_Turing): + name = "Turing Anguilla Species Classifier" + description = "Trained on 28th June 2024 by Turing team using Resnet50 model." + weights_path = "https://object-arbutus.cloud.computecanada.ca/ami-models/moths/classification/turing-anguilla_v01_resnet50_2024-06-28-17-01_state.pt" + labels_path = "https://object-arbutus.cloud.computecanada.ca/ami-models/moths/classification/01_anguilla_data_category_map.json" + + class TuringUKSpeciesClassifier(SpeciesClassifier, Resnet50Classifier_Turing): name = "Turing UK Species Classifier" description = "Trained on 13th May 2024 by Turing team using Resnet50 model." @@ -372,14 +409,93 @@ class PanamaMothSpeciesClassifierMixedResolution2023( lookup_gbif_names = True normalization = imagenet_normalization + description = "Trained on Novempber 11th, 2023 using a corrected species list of 1060 classes." + weights_path = ( + "https://object-arbutus.cloud.computecanada.ca/ami-models/moths/classification/" + "panama_resetnet50_best_5aeb515a.pth" + ) + labels_path = ( + "https://object-arbutus.cloud.computecanada.ca/ami-models/moths/classification/" + "03_moths_centralAmerica_category_map-202311110-with-names.json" + ) + + +class GlobalMothSpeciesClassifier(SpeciesClassifier, Resnet50TimmClassifier): + input_size = 128 + normalization = imagenet_normalization + lookup_gbif_names = False + + name = "Global Species Classifier - Aug 2024" description = ( - "Trained on Noveber 07, 2023 using a corrected species list of 1036 classes." + "Trained on August 28th, 2024 for 29,176 species. " + "https://wandb.ai/moth-ai/global-moth-classifier/runs/h0cuqrbc/overview" + ) + weights_path = ( + "https://object-arbutus.cloud.computecanada.ca/ami-models/moths/classification/" + "global_resnet50_20240828_b06d3b3a.pth" ) + labels_path = ( + "https://object-arbutus.cloud.computecanada.ca/ami-models/moths/classification/" + "global_category_map_with_names_20240828.json" + ) + + +class QuebecVermontMothSpeciesClassifier2024(SpeciesClassifier, Resnet50TimmClassifier): + input_size = 128 + normalization = imagenet_normalization + lookup_gbif_names = False + + name = "Quebec & Vermont Species Classifier - Apr 2024" + description = ( + "Trained on April 17, 2024 for 2,497 species. " + "https://wandb.ai/moth-ai/ami-gbif-fine-grained/runs/1x53zmp2/overview" + ) + weights_path = ( + "https://object-arbutus.cloud.computecanada.ca/ami-models/moths/classification/" + "quebec-vermont_resnet50_baseline_20240417_950de764.pth" + ) + labels_path = ( + "https://object-arbutus.cloud.computecanada.ca/ami-models/moths/classification/" + "01_ami-gbif_fine-grained_ne-america_category_map-with_names.json" + ) + + +class UKDenmarkMothSpeciesClassifier2024(SpeciesClassifier, Resnet50TimmClassifier): + input_size = 128 + normalization = imagenet_normalization + lookup_gbif_names = False + + name = "UK & Denmark Species Classifier - Apr 2024" + description = ( + "Trained on April 17, 2024 for 2,603 species. " + "https://wandb.ai/moth-ai/ami-gbif-fine-grained/runs/x5u7jcbf/overview" + ) + weights_path = ( + "https://object-arbutus.cloud.computecanada.ca/ami-models/moths/classification/" + "uk-denmark_resnet50_baseline_20240417_55250a8b.pth" + ) + labels_path = ( + "https://object-arbutus.cloud.computecanada.ca/ami-models/moths/classification/" + "02_ami-gbif_fine-grained_w-europe_category_map-with_names.json" + ) + + +class PanamaMothSpeciesClassifier2024(SpeciesClassifier, Resnet50TimmClassifier): + input_size = 128 + normalization = imagenet_normalization + lookup_gbif_names = False + + name = "Panama Species Classifier - Apr 2024" + description = ( + "Trained on April 17, 2024 for 636 species. " + "https://wandb.ai/moth-ai/ami-gbif-fine-grained/runs/1sekgkan/overview" + ) + weights_path = ( "https://object-arbutus.cloud.computecanada.ca/ami-models/moths/classification/" - "panama_resnet50_baseline_9270f84a.pth" + "panama_resnet50_baseline_20240417_edbb46dd.pth" ) labels_path = ( "https://object-arbutus.cloud.computecanada.ca/ami-models/moths/classification/" - "03_moths_centralAmerica_category_map-with-names.json" + "03_ami-gbif_fine-grained_c-america_category_map-with_names.json" ) diff --git a/trapdata/ml/models/localization.py b/trapdata/ml/models/localization.py index 2f0dc9d5..bf93cda4 100644 --- a/trapdata/ml/models/localization.py +++ b/trapdata/ml/models/localization.py @@ -131,7 +131,7 @@ def get_dataset(self): ) return dataset - def save_results(self, item_ids, batch_output): + def save_results(self, item_ids, batch_output, *args, **kwargs): # Format data to be saved in DB # Here we are just saving the bboxes of detected objects detected_objects_data = [] @@ -180,7 +180,7 @@ def get_model(self): def post_process_single(self, output): # This model does not use the labels from the object detection model _ = output["labels"] - assert all([label == 1 for label in output["labels"]]) + assert all(label == 1 for label in output["labels"]) # Filter out objects if their score is under score threshold bboxes = output["boxes"][output["scores"] > self.bbox_score_threshold] @@ -221,7 +221,7 @@ def get_model(self): def post_process_single(self, output): # This model does not use the labels from the object detection model _ = output["labels"] - assert all([label == 1 for label in output["labels"]]) + assert all(label == 1 for label in output["labels"]) # Filter out objects if their score is under score threshold bboxes = output["boxes"][output["scores"] > self.bbox_score_threshold] @@ -275,7 +275,7 @@ def get_model(self): def post_process_single(self, output): # This model does not use the labels from the object detection model _ = output["labels"] - assert all([label == 1 for label in output["labels"]]) + assert all(label == 1 for label in output["labels"]) # Filter out objects if their score is under score threshold bboxes = output["boxes"][output["scores"] > self.bbox_score_threshold] diff --git a/trapdata/ml/models/tracking.py b/trapdata/ml/models/tracking.py index 7a71ad84..1a2497e4 100644 --- a/trapdata/ml/models/tracking.py +++ b/trapdata/ml/models/tracking.py @@ -464,7 +464,7 @@ def post_process_batch(self, output) -> np.ndarray: # logger.debug(f"Post-processing features: {output[0]}") return output - def save_results(self, object_ids, batch_output): + def save_results(self, object_ids, batch_output, *args, **kwargs): # Here we are saving the moth/non-moth labels data = [ { diff --git a/trapdata/ml/server.py b/trapdata/ml/server.py deleted file mode 100644 index ad022c6b..00000000 --- a/trapdata/ml/server.py +++ /dev/null @@ -1,55 +0,0 @@ -import multiprocessing -import os -import sys -import time - -from trapdata.db.models.queue import ImageQueue -from trapdata.ml.models.localization import MothObjectDetector_FasterRCNN - - -def watch_queue(db_path, interval=1): - # query queue(s) - # the queue should contain the model name and parameters to be used - # the model name is from the model registry, which is where the weights are specified as well - # the queue entries can have their status updated (waiting, preprocessing, classifying, done) - # the server queries the queue, groups by model, starts predict dataloader with that batch - # if we want to do one model at a time, not sure how to order them - - model = MothObjectDetector_FasterRCNN(db_path) - queue = ImageQueue(db_path) - - while True: - print(f"Images in queue: {queue.queue_count()}") - if queue.queue_count() > 0: - # Pick one of these directions! - # model.run(queue=queue) - queue.process_queue(model) - time.sleep(interval) - - -def watch_queue_in_background(db_path): - # https://docs.python.org/3/library/multiprocessing.html - multiprocessing.set_start_method( - "spawn" - ) # Required for PyTorch, default on Windows - p = multiprocessing.Process( - target=watch_queue, - args=(db_path,), - daemon=True, - ) - p.start() - - while True: - print(p) - if not p.is_alive(): - print("Subprocess exited") - sys.exit(1) - time.sleep(1) - - -if __name__ == "__main__": - # db_path = sys.argv[1] - db_path = os.environ["DATABASE_URL"] - - watch_queue(db_path) - # watch_queue_in_background(db_path) diff --git a/trapdata/ml/utils.py b/trapdata/ml/utils.py index 5a96fccc..3d520677 100644 --- a/trapdata/ml/utils.py +++ b/trapdata/ml/utils.py @@ -1,19 +1,38 @@ +from __future__ import annotations + +import base64 +import binascii import datetime +import io import json import os import pathlib +import re +import tempfile import time import urllib.error -import urllib.request from dataclasses import dataclass -from typing import Optional +from typing import TYPE_CHECKING, Optional +from urllib.parse import urlparse import pandas as pd +import PIL.Image +import PIL.ImageFile +import requests import torch import torchvision +if TYPE_CHECKING: + from _typeshed import SupportsRead + from trapdata import logger +PIL.ImageFile.LOAD_TRUNCATED_IMAGES = True + +# This is polite and required by some hosts +# see: https://foundation.wikimedia.org/wiki/Policy:User-Agent_policy +USER_AGENT = "AntennaInsectDataPlatform/1.0 (https://insectai.org)" + def get_device(device_str=None) -> torch.device: """ @@ -30,18 +49,26 @@ def get_device(device_str=None) -> torch.device: def get_or_download_file( - path, destination_dir=None, prefix=None, suffix=None + path_or_url, destination_dir=None, prefix=None, suffix=None ) -> pathlib.Path: """ - >>> filename, headers = get_weights("https://drive.google.com/file/d/1KdQc56WtnMWX9PUapy6cS0CdjC8VSdVe/view?usp=sharing") - + Fetch a file from a URL or local path. If the path is a URL, download the file. + If the URL has already been downloaded, return the existing local path. + If the path is a local path, return the path. + >>> filepath = get_or_download_file( + "https://example.uk/images/31-20230919033000-snapshot.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=451d406b7eb1113e1bb05c083ce51481%2F20240429%2F") + >>> filepath.name + '31-20230919033000-snapshot.jpg' + >>> filepath = get_or_download_file("/home/user/images/31-20230919033000-snapshot.jpg") + >>> filepath.name + '31-20230919033000-snapshot.jpg' """ - if not path: + if not path_or_url: raise Exception("Specify a URL or path to fetch file from.") - # If path is a local path instead of a URL then urlretrieve will just return that path destination_dir = destination_dir or os.environ.get("LOCAL_WEIGHTS_PATH") - fname = path.rsplit("/", 1)[-1] + fname = pathlib.Path(urlparse(path_or_url).path).name + if destination_dir: destination_dir = pathlib.Path(destination_dir) if prefix: @@ -60,15 +87,94 @@ def get_or_download_file( if local_filepath and local_filepath.exists(): logger.info(f"Using existing {local_filepath}") return local_filepath + else: + logger.info(f"Downloading {path_or_url} to {local_filepath}") + + # Check if the path is a URL + if path_or_url.startswith(("http://", "https://")): + headers = {"User-Agent": USER_AGENT} + response = requests.get(path_or_url, stream=True, headers=headers) + response.raise_for_status() # Raise an exception for HTTP errors + + with open(local_filepath, "wb") as f: + for chunk in response.iter_content(chunk_size=8192): + f.write(chunk) + + logger.info(f"Downloaded to {local_filepath}") + return local_filepath + else: + # If it's a local path, just return it + return pathlib.Path(path_or_url) + + +def decode_base64_string(string) -> io.BytesIO: + image_data = re.sub("^data:image/.+;base64,", "", string) + decoded = base64.b64decode(image_data) + buffer = io.BytesIO(decoded) + buffer.seek(0) + return buffer + +def open_image( + fp: str | bytes | pathlib.Path | SupportsRead[bytes], raise_exception: bool = True +) -> PIL.Image.Image | None: + """ + Wrapper from PIL.Image.open that handles errors and converts to RGB. + """ + img = None + try: + img = PIL.Image.open(fp) + except PIL.UnidentifiedImageError: + logger.warn(f"Unidentified image: {str(fp)[:100]}...") + if raise_exception: + raise + except OSError: + logger.warn(f"Could not open image: {str(fp)[:100]}...") + if raise_exception: + raise else: - logger.info(f"Downloading {path} to {local_filepath}") - resulting_filepath, headers = urllib.request.urlretrieve( - url=path, filename=local_filepath - ) - resulting_filepath = pathlib.Path(resulting_filepath) - logger.info(f"Downloaded to {resulting_filepath}") - return resulting_filepath + # Convert to RGB if necessary + if img.mode != "RGB": + img = img.convert("RGB") + + return img + + +def get_image( + url: str | None = None, + filepath: str | pathlib.Path | None = None, + b64: str | None = None, + raise_exception: bool = True, +) -> PIL.Image.Image | None: + """ + Given a URL, local file path or base64 image, return a PIL image. + """ + + if url: + logger.info(f"Fetching image from URL: {url}") + tempdir = tempfile.TemporaryDirectory(prefix="ami_images") + img_path = get_or_download_file(url, destination_dir=tempdir.name) + return open_image(img_path, raise_exception=raise_exception) + + elif filepath: + logger.info(f"Loading image from local filesystem: {filepath}") + return open_image(filepath, raise_exception=raise_exception) + + elif b64: + logger.info(f"Loading image from base64 string: {b64[:30]}...") + try: + buffer = decode_base64_string(b64) + except binascii.Error as e: + logger.warn(f"Could not decode base64 image: {e}") + if raise_exception: + raise + else: + return None + else: + return open_image(buffer, raise_exception=raise_exception) + + else: + raise Exception("Specify a URL, path or base64 image.") def synchronize_clocks(): @@ -151,6 +257,7 @@ def fetch_gbif_species(gbif_id: int) -> Optional[Taxon]: Look up taxon name from GBIF API. Cache results in user_data_path. """ + logger.info(f"Looking up species name for GBIF id {gbif_id}") base_url = "https://api.gbif.org/v1/species/{gbif_id}" url = base_url.format(gbif_id=gbif_id) diff --git a/trapdata/settings.py b/trapdata/settings.py index 0785d501..1f5ce85f 100644 --- a/trapdata/settings.py +++ b/trapdata/settings.py @@ -5,7 +5,8 @@ from typing import Optional, Union import sqlalchemy -from pydantic import BaseSettings, Field, ValidationError, validator +from pydantic import Field, ValidationError, validator +from pydantic_settings import BaseSettings from rich import print as rprint from trapdata import ml @@ -17,7 +18,7 @@ class Settings(BaseSettings): # Can't use PyDantic DSN validator for database_url if sqlite filepath has spaces, see custom validator below database_url: Union[str, sqlalchemy.engine.URL] = default_database_dsn() user_data_path: pathlib.Path = get_app_dir() - image_base_path: Optional[pathlib.Path] + image_base_path: Optional[pathlib.Path] = None localization_model: ml.models.ObjectDetectorChoice = Field( default=ml.models.DEFAULT_OBJECT_DETECTOR ) diff --git a/trapdata/tests/config/stages.json b/trapdata/tests/config/stages.json new file mode 100644 index 00000000..ad8d3aa0 --- /dev/null +++ b/trapdata/tests/config/stages.json @@ -0,0 +1,50 @@ +[ + { + "key": "localization", + "name": "Object Detection", + "params": [ + { + "name": "Model", + "key": "algorithm", + "category": "algorithm", + "value": "FasterRCNN MobileNet for AMI Moth Traps 2023" + } + ] + }, + { + "key": "binary_classification", + "name": "Binary Classification", + "params": [ + { + "name": "Model", + "key": "algorithm", + "category": "algorithm", + "value": "Moth / Non-Moth Detector 2022" + } + ] + }, + { + "key": "fine_grained_classification", + "name": "Fine-grained Species Classification", + "params": [ + { + "name": "Model", + "key": "algorithm", + "category": "algorithm", + "value": "Default" + } + ] + }, + { + "key": "tracking", + "name": "Occurrence Tracking", + "params": [ + { + "name": "Model", + "key": "algorithm", + "category": "algorithm", + "value": "Default" + } + ] + } +] diff --git a/trapdata/tests/images/costarica/.gitkeep b/trapdata/tests/images/costarica/.gitkeep new file mode 100644 index 00000000..e69de29b diff --git a/trapdata/tests/images/global/.gitkeep b/trapdata/tests/images/global/.gitkeep new file mode 100644 index 00000000..e69de29b diff --git a/trapdata/tests/images/panama/01-20231111015309-snapshot.jpg b/trapdata/tests/images/panama/01-20231111015309-snapshot.jpg new file mode 100644 index 00000000..a8df830e Binary files /dev/null and b/trapdata/tests/images/panama/01-20231111015309-snapshot.jpg differ diff --git a/trapdata/tests/images/panama/01-20231111032659-snapshot.jpg b/trapdata/tests/images/panama/01-20231111032659-snapshot.jpg new file mode 100644 index 00000000..d7921e06 Binary files /dev/null and b/trapdata/tests/images/panama/01-20231111032659-snapshot.jpg differ diff --git a/trapdata/tests/results/sequential.json b/trapdata/tests/results/sequential.json index 8bac3dbe..98304951 100644 --- a/trapdata/tests/results/sequential.json +++ b/trapdata/tests/results/sequential.json @@ -6,10 +6,10 @@ "frame": 0, "image": 1, "id": 1, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/1e1a6bc537bdbaf5c4491936614ca1b9.jpg", + "path": "crops/1e1a6bc537bdbaf5c4491936614ca1b9.jpg", "binary_label": "moth", "specific_label": "Herminia jacchusalis", - "specific_label_score": 0.3590092957019806, + "specific_label_score": 0.36674025654792786, "cost": null } ], @@ -20,59 +20,11 @@ "frame": 0, "image": 1, "id": 10, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/9c3c030cbdad6426503329007c220d01.jpg", + "path": "crops/9c3c030cbdad6426503329007c220d01.jpg", "binary_label": "moth", "specific_label": "Cenopis pettitana", - "specific_label_score": 0.48101720213890076, + "specific_label_score": 0.4551536440849304, "cost": null - }, - { - "event": "2022-07-28", - "sequence": "20220728-SEQ-10", - "frame": 1, - "image": 2, - "id": 23, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/06e3e85bd3121bb32d353d504720c7e0.jpg", - "binary_label": "moth", - "specific_label": "Lomographa vestaliata", - "specific_label_score": 0.1082422137260437, - "cost": 0.33694157610858744 - }, - { - "event": "2022-07-28", - "sequence": "20220728-SEQ-10", - "frame": 2, - "image": 3, - "id": 36, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/5a04ecbab826991f438e7ca4847f2d79.jpg", - "binary_label": "moth", - "specific_label": "Paleacrita vernata", - "specific_label_score": 0.11166275292634964, - "cost": 0.3060874110661605 - }, - { - "event": "2022-07-28", - "sequence": "20220728-SEQ-10", - "frame": 3, - "image": 4, - "id": 48, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/6fb9055b640ed29efeca88fe6dba9026.jpg", - "binary_label": "moth", - "specific_label": "Cenopis pettitana", - "specific_label_score": 0.3255586326122284, - "cost": 0.1531350902213409 - }, - { - "event": "2022-07-28", - "sequence": "20220728-SEQ-10", - "frame": 4, - "image": 5, - "id": 63, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/612e4ffefd65401b8e8b4bc8864d3393.jpg", - "binary_label": "moth", - "specific_label": "Acleris forbesana", - "specific_label_score": 0.30045822262763977, - "cost": 0.5540414948529558 } ], "20220728-SEQ-12": [ @@ -82,10 +34,10 @@ "frame": 0, "image": 1, "id": 12, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/94a04729c28297efc26797dd8f960bf4.jpg", + "path": "crops/94a04729c28297efc26797dd8f960bf4.jpg", "binary_label": "moth", "specific_label": "Sciota virgatella", - "specific_label_score": 0.16646118462085724, + "specific_label_score": 0.1626400649547577, "cost": null }, { @@ -94,11 +46,11 @@ "frame": 1, "image": 2, "id": 25, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/2adf5e391835830bc5c3b168b2a14ae7.jpg", + "path": "crops/2adf5e391835830bc5c3b168b2a14ae7.jpg", "binary_label": "moth", "specific_label": "Blastobasis glandulella", - "specific_label_score": 0.3292843997478485, - "cost": 0.139929701806197 + "specific_label_score": 0.33004164695739746, + "cost": 0.13497154554706778 } ], "20220728-SEQ-13": [ @@ -108,10 +60,10 @@ "frame": 0, "image": 2, "id": 13, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/8f8a83bef8cddff01421ab2b5b7fe43f.jpg", + "path": "crops/8f8a83bef8cddff01421ab2b5b7fe43f.jpg", "binary_label": "moth", "specific_label": "Herminia jacchusalis", - "specific_label_score": 0.8296645283699036, + "specific_label_score": 0.8326359391212463, "cost": null }, { @@ -120,37 +72,11 @@ "frame": 1, "image": 3, "id": 26, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/04466e5cb9fb971e09d5d7929666a2fb.jpg", + "path": "crops/04466e5cb9fb971e09d5d7929666a2fb.jpg", "binary_label": "moth", "specific_label": "Herminia jacchusalis", - "specific_label_score": 0.8486447334289551, - "cost": 0.9821406052135898 - } - ], - "20220728-SEQ-19": [ - { - "event": "2022-07-28", - "sequence": "20220728-SEQ-19", - "frame": 0, - "image": 2, - "id": 19, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/56720869aeef9a7b7c65636690c3a1c4.jpg", - "binary_label": "moth", - "specific_label": "Epiblema otiosana", - "specific_label_score": 0.10388875752687454, - "cost": null - }, - { - "event": "2022-07-28", - "sequence": "20220728-SEQ-19", - "frame": 1, - "image": 3, - "id": 32, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/27f383b75d560abd4d2248173375ce45.jpg", - "binary_label": "moth", - "specific_label": "Epiblema otiosana", - "specific_label_score": 0.4118242859840393, - "cost": 0.47746568310855875 + "specific_label_score": 0.8482295870780945, + "cost": 0.981487038815832 } ], "20220728-SEQ-2": [ @@ -160,10 +86,10 @@ "frame": 0, "image": 1, "id": 2, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/050d2f2270b273a1fded55bc48017e41.jpg", + "path": "crops/050d2f2270b273a1fded55bc48017e41.jpg", "binary_label": "moth", "specific_label": "Renia discoloralis", - "specific_label_score": 0.1003936380147934, + "specific_label_score": 0.09825816750526428, "cost": null }, { @@ -172,11 +98,11 @@ "frame": 1, "image": 2, "id": 14, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/b4574a59c5fe4885bbb53bbc88e73717.jpg", + "path": "crops/b4574a59c5fe4885bbb53bbc88e73717.jpg", "binary_label": "moth", "specific_label": "Renia discoloralis", - "specific_label_score": 0.18321821093559265, - "cost": 0.15039522868278563 + "specific_label_score": 0.18399162590503693, + "cost": 0.14990407857026697 }, { "event": "2022-07-28", @@ -184,11 +110,11 @@ "frame": 2, "image": 3, "id": 27, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/caaed63d0f43489f133f3f2c055e9ac5.jpg", + "path": "crops/caaed63d0f43489f133f3f2c055e9ac5.jpg", "binary_label": "moth", - "specific_label": "Phalaenophana pyramusalis", - "specific_label_score": 0.16425232589244843, - "cost": 0.1416141777643564 + "specific_label": "Renia discoloralis", + "specific_label_score": 0.1651955544948578, + "cost": 0.1416390773232055 }, { "event": "2022-07-28", @@ -196,11 +122,11 @@ "frame": 3, "image": 4, "id": 40, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/25dc045b46478da77bce17f067c84ade.jpg", + "path": "crops/25dc045b46478da77bce17f067c84ade.jpg", "binary_label": "moth", "specific_label": "Phalaenophana pyramusalis", - "specific_label_score": 0.180535227060318, - "cost": 0.050937856300308317 + "specific_label_score": 0.18242935836315155, + "cost": 0.050806535128817366 }, { "event": "2022-07-28", @@ -208,11 +134,11 @@ "frame": 4, "image": 5, "id": 55, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/ffaed774c235e53d1a88f71b46815a79.jpg", + "path": "crops/ffaed774c235e53d1a88f71b46815a79.jpg", "binary_label": "moth", "specific_label": "Phalaenophana pyramusalis", - "specific_label_score": 0.10901834815740585, - "cost": 0.14004164301990088 + "specific_label_score": 0.1101696714758873, + "cost": 0.13936685385203476 } ], "20220728-SEQ-3": [ @@ -222,10 +148,10 @@ "frame": 0, "image": 1, "id": 3, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/a397b241b1e6f0f4c021a71191c4bcc8.jpg", + "path": "crops/a397b241b1e6f0f4c021a71191c4bcc8.jpg", "binary_label": "moth", "specific_label": "Alcis porcelaria", - "specific_label_score": 0.7772248387336731, + "specific_label_score": 0.7742837071418762, "cost": null }, { @@ -234,11 +160,11 @@ "frame": 1, "image": 2, "id": 15, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/d31b94d05b70c2536a3aab333b85fd1a.jpg", + "path": "crops/d31b94d05b70c2536a3aab333b85fd1a.jpg", "binary_label": "moth", "specific_label": "Alcis porcelaria", - "specific_label_score": 0.7708656787872314, - "cost": 0.10908107763303553 + "specific_label_score": 0.7705228328704834, + "cost": 0.10963244718643826 }, { "event": "2022-07-28", @@ -246,11 +172,11 @@ "frame": 2, "image": 3, "id": 28, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/fd59758e8bec3f212aab405114e4b677.jpg", + "path": "crops/fd59758e8bec3f212aab405114e4b677.jpg", "binary_label": "moth", "specific_label": "Alcis porcelaria", - "specific_label_score": 0.7444019317626953, - "cost": 0.11793770704262882 + "specific_label_score": 0.7453959584236145, + "cost": 0.11819957033750247 }, { "event": "2022-07-28", @@ -258,11 +184,11 @@ "frame": 3, "image": 4, "id": 41, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/06daa50d5a8dc37087147f17aef241e6.jpg", + "path": "crops/06daa50d5a8dc37087147f17aef241e6.jpg", "binary_label": "moth", "specific_label": "Alcis porcelaria", - "specific_label_score": 0.8606120944023132, - "cost": 0.0201224386349128 + "specific_label_score": 0.8595471978187561, + "cost": 0.020524657777073563 }, { "event": "2022-07-28", @@ -270,11 +196,11 @@ "frame": 4, "image": 5, "id": 54, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/48fd0a9a60b779184ecbb152276532ba.jpg", + "path": "crops/48fd0a9a60b779184ecbb152276532ba.jpg", "binary_label": "moth", "specific_label": "Alcis porcelaria", - "specific_label_score": 0.7540773153305054, - "cost": 0.7950070255293996 + "specific_label_score": 0.7575086951255798, + "cost": 0.7954841306374566 } ], "20220728-SEQ-34": [ @@ -284,52 +210,50 @@ "frame": 0, "image": 3, "id": 34, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/06103a98c8c1bcfe11567891de35ba73.jpg", + "path": "crops/06103a98c8c1bcfe11567891de35ba73.jpg", "binary_label": "moth", "specific_label": "Platytes vobisne", - "specific_label_score": 0.21381868422031403, + "specific_label_score": 0.21883490681648254, "cost": null } ], - "20220728-SEQ-39": [ + "20220728-SEQ-36": [ { "event": "2022-07-28", - "sequence": "20220728-SEQ-39", + "sequence": "20220728-SEQ-36", "frame": 0, - "image": 4, - "id": 39, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/0b872ceead5109a3d3ef3afbb0f2e2bf.jpg", + "image": 3, + "id": 36, + "path": "crops/5a04ecbab826991f438e7ca4847f2d79.jpg", "binary_label": "moth", - "specific_label": "Herminia jacchusalis", - "specific_label_score": 0.7112213969230652, + "specific_label": "Paleacrita vernata", + "specific_label_score": 0.1074075922369957, "cost": null - } - ], - "20220728-SEQ-45": [ + }, { "event": "2022-07-28", - "sequence": "20220728-SEQ-45", - "frame": 0, + "sequence": "20220728-SEQ-36", + "frame": 1, "image": 4, - "id": 45, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/6d79d24740c4f43fde169d9762216b07.jpg", + "id": 48, + "path": "crops/6fb9055b640ed29efeca88fe6dba9026.jpg", "binary_label": "moth", - "specific_label": "Psychomorpha epimenis", - "specific_label_score": 0.2151375710964203, - "cost": null + "specific_label": "Cenopis pettitana", + "specific_label_score": 0.3148452937602997, + "cost": 0.1494068513805036 } ], - "20220728-SEQ-47": [ + "20220728-SEQ-39": [ { "event": "2022-07-28", - "sequence": "20220728-SEQ-47", + "sequence": "20220728-SEQ-39", "frame": 0, "image": 4, - "id": 47, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/24188d60b41752cf5f456e20f1e8071b.jpg", + "id": 39, + "path": "crops/0b872ceead5109a3d3ef3afbb0f2e2bf.jpg", "binary_label": "moth", - "specific_label": "Phigalia titea", - "specific_label_score": 0.11463999003171921, + "specific_label": "Herminia jacchusalis", + "specific_label_score": 0.7139288187026978, "cost": null } ], @@ -340,10 +264,10 @@ "frame": 0, "image": 1, "id": 5, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/6ab8a9dc4dc1532c5af19df01461b2f2.jpg", + "path": "crops/6ab8a9dc4dc1532c5af19df01461b2f2.jpg", "binary_label": "moth", "specific_label": "Scoparia biplagialis", - "specific_label_score": 0.048303935676813126, + "specific_label_score": 0.04816208407282829, "cost": null }, { @@ -352,11 +276,11 @@ "frame": 1, "image": 2, "id": 16, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/79a4aa5da1e2a02470bb3ec7c282aba6.jpg", + "path": "crops/79a4aa5da1e2a02470bb3ec7c282aba6.jpg", "binary_label": "moth", "specific_label": "Sciota virgatella", - "specific_label_score": 0.040570326149463654, - "cost": 0.09378082078715587 + "specific_label_score": 0.040679384022951126, + "cost": 0.09395580948975955 }, { "event": "2022-07-28", @@ -364,11 +288,11 @@ "frame": 2, "image": 3, "id": 29, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/c261ec825f40ba9208d7267c2d5698b0.jpg", + "path": "crops/c261ec825f40ba9208d7267c2d5698b0.jpg", "binary_label": "moth", "specific_label": "Endothenia hebesana", - "specific_label_score": 0.132541224360466, - "cost": 0.17138480259372366 + "specific_label_score": 0.12980878353118896, + "cost": 0.17010236089277006 }, { "event": "2022-07-28", @@ -376,11 +300,11 @@ "frame": 3, "image": 4, "id": 43, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/3edc2d2288d6b582bb02339a2fefebad.jpg", + "path": "crops/3edc2d2288d6b582bb02339a2fefebad.jpg", "binary_label": "moth", - "specific_label": "Sciota virgatella", - "specific_label_score": 0.024115437641739845, - "cost": 0.10420992078244451 + "specific_label": "Endothenia hebesana", + "specific_label_score": 0.02412785217165947, + "cost": 0.10550720119503376 }, { "event": "2022-07-28", @@ -388,37 +312,11 @@ "frame": 4, "image": 5, "id": 56, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/97e5b560e72068184825655157ca1e01.jpg", + "path": "crops/97e5b560e72068184825655157ca1e01.jpg", "binary_label": "moth", "specific_label": "Acrobasis indigenella", - "specific_label_score": 0.025976473465561867, - "cost": 0.28677334510811137 - } - ], - "20220728-SEQ-51": [ - { - "event": "2022-07-28", - "sequence": "20220728-SEQ-51", - "frame": 0, - "image": 4, - "id": 51, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/64229628766e2d9faa16419db0ec58b5.jpg", - "binary_label": "moth", - "specific_label": "Blastobasis glandulella", - "specific_label_score": 0.44940221309661865, - "cost": null - }, - { - "event": "2022-07-28", - "sequence": "20220728-SEQ-51", - "frame": 1, - "image": 5, - "id": 64, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/4a83f2400b4fcfc9725dcf0c8dfa9aa1.jpg", - "binary_label": "moth", - "specific_label": "Ephestiodes infimella", - "specific_label_score": 0.2463548630475998, - "cost": 0.20962423038802197 + "specific_label_score": 0.026554321870207787, + "cost": 0.28692762777153913 } ], "20220728-SEQ-52": [ @@ -428,10 +326,10 @@ "frame": 0, "image": 4, "id": 52, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/6668ea366a978a829e207c46b54569f2.jpg", + "path": "crops/6668ea366a978a829e207c46b54569f2.jpg", "binary_label": "moth", "specific_label": "Alsophila pometaria", - "specific_label_score": 0.044359490275382996, + "specific_label_score": 0.04102465510368347, "cost": null } ], @@ -442,24 +340,10 @@ "frame": 0, "image": 5, "id": 53, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/617998a4e8778a82c4fff4e2a734badc.jpg", + "path": "crops/617998a4e8778a82c4fff4e2a734badc.jpg", "binary_label": "moth", "specific_label": "Herminia jacchusalis", - "specific_label_score": 0.7014172673225403, - "cost": null - } - ], - "20220728-SEQ-59": [ - { - "event": "2022-07-28", - "sequence": "20220728-SEQ-59", - "frame": 0, - "image": 5, - "id": 59, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/f8cf34b4222265f4f5cf600e3343791d.jpg", - "binary_label": "moth", - "specific_label": "Exaeretia ciniflonella", - "specific_label_score": 0.04361367225646973, + "specific_label_score": 0.701308012008667, "cost": null } ], @@ -470,10 +354,10 @@ "frame": 0, "image": 1, "id": 6, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/42d7083f6f607254c4413471af0cb318.jpg", + "path": "crops/42d7083f6f607254c4413471af0cb318.jpg", "binary_label": "moth", "specific_label": "Pandemis lamprosana", - "specific_label_score": 0.18402302265167236, + "specific_label_score": 0.18234437704086304, "cost": null }, { @@ -482,11 +366,11 @@ "frame": 1, "image": 2, "id": 17, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/aeb2787c663b0fba62424469364c550c.jpg", + "path": "crops/aeb2787c663b0fba62424469364c550c.jpg", "binary_label": "moth", "specific_label": "Pandemis lamprosana", - "specific_label_score": 0.18639925122261047, - "cost": 0.16316379318943114 + "specific_label_score": 0.18779221177101135, + "cost": 0.16293477478873047 }, { "event": "2022-07-28", @@ -494,11 +378,11 @@ "frame": 2, "image": 3, "id": 31, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/e7cf2aecf178331d12d7c8e3d21609d2.jpg", + "path": "crops/e7cf2aecf178331d12d7c8e3d21609d2.jpg", "binary_label": "moth", "specific_label": "Pandemis lamprosana", - "specific_label_score": 0.22766618430614471, - "cost": 0.11152822333897693 + "specific_label_score": 0.22746993601322174, + "cost": 0.11173570877555027 }, { "event": "2022-07-28", @@ -506,11 +390,11 @@ "frame": 3, "image": 4, "id": 44, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/383f0b8d4c51ad7cf46020b8a69d8231.jpg", + "path": "crops/383f0b8d4c51ad7cf46020b8a69d8231.jpg", "binary_label": "moth", "specific_label": "Pandemis lamprosana", - "specific_label_score": 0.10905972868204117, - "cost": 0.1307117404503841 + "specific_label_score": 0.11245781183242798, + "cost": 0.13005166402889598 }, { "event": "2022-07-28", @@ -518,24 +402,24 @@ "frame": 4, "image": 5, "id": 57, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/6dcba1cdd72bf84b73dc9c76a87ed2eb.jpg", + "path": "crops/6dcba1cdd72bf84b73dc9c76a87ed2eb.jpg", "binary_label": "moth", "specific_label": "Pandemis lamprosana", - "specific_label_score": 0.2105257660150528, - "cost": 0.0887736799534202 + "specific_label_score": 0.21134120225906372, + "cost": 0.08849004208516884 } ], - "20220728-SEQ-60": [ + "20220728-SEQ-64": [ { "event": "2022-07-28", - "sequence": "20220728-SEQ-60", + "sequence": "20220728-SEQ-64", "frame": 0, "image": 5, - "id": 60, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/f2d13d975e93631dde15c47f9a0212bc.jpg", + "id": 64, + "path": "crops/4a83f2400b4fcfc9725dcf0c8dfa9aa1.jpg", "binary_label": "moth", - "specific_label": "Psychomorpha epimenis", - "specific_label_score": 0.29859471321105957, + "specific_label": "Ephestiodes infimella", + "specific_label_score": 0.2339533269405365, "cost": null } ], @@ -546,10 +430,10 @@ "frame": 0, "image": 1, "id": 7, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/3db72a01163c2c2bbfa8bfddd13ee28b.jpg", + "path": "crops/3db72a01163c2c2bbfa8bfddd13ee28b.jpg", "binary_label": "moth", "specific_label": "Parapediasia decorellus", - "specific_label_score": 0.059002503752708435, + "specific_label_score": 0.061027005314826965, "cost": null }, { @@ -558,11 +442,11 @@ "frame": 1, "image": 2, "id": 21, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/31786116724214f10a16e1d52cdad7f1.jpg", + "path": "crops/31786116724214f10a16e1d52cdad7f1.jpg", "binary_label": "moth", "specific_label": "Acleris flavivittana", - "specific_label_score": 0.0627722442150116, - "cost": 0.30495344614216496 + "specific_label_score": 0.06507749855518341, + "cost": 0.3045031878885703 }, { "event": "2022-07-28", @@ -570,11 +454,11 @@ "frame": 2, "image": 3, "id": 33, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/d14dc92620227e704f3246a4ebbf6406.jpg", + "path": "crops/d14dc92620227e704f3246a4ebbf6406.jpg", "binary_label": "moth", "specific_label": "Platytes vobisne", - "specific_label_score": 0.12406660616397858, - "cost": 0.2630428577920178 + "specific_label_score": 0.12161475419998169, + "cost": 0.2617039664654565 }, { "event": "2022-07-28", @@ -582,11 +466,11 @@ "frame": 3, "image": 4, "id": 46, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/bc570c303fe214f42b1aecdae0cba390.jpg", + "path": "crops/5ed17aa005cbf61239b07945577e390b.jpg", "binary_label": "moth", "specific_label": "Platytes vobisne", - "specific_label_score": 0.06495240330696106, - "cost": 0.10871116996741088 + "specific_label_score": 0.060081955045461655, + "cost": 0.06600176087540374 }, { "event": "2022-07-28", @@ -594,11 +478,11 @@ "frame": 4, "image": 5, "id": 61, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/24bd1df99cf61e2214e0618916acbd8a.jpg", + "path": "crops/24bd1df99cf61e2214e0618916acbd8a.jpg", "binary_label": "moth", "specific_label": "Platytes vobisne", - "specific_label_score": 0.04522542282938957, - "cost": 0.24548752153884454 + "specific_label_score": 0.04389219731092453, + "cost": 0.20434342177720344 } ], "20220728-SEQ-8": [ @@ -608,10 +492,10 @@ "frame": 0, "image": 1, "id": 8, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/57ba9fe508130cba788cff516e8d69e3.jpg", + "path": "crops/57ba9fe508130cba788cff516e8d69e3.jpg", "binary_label": "moth", "specific_label": "Microcrambus minor", - "specific_label_score": 0.1657266765832901, + "specific_label_score": 0.1680111587047577, "cost": null }, { @@ -620,11 +504,11 @@ "frame": 1, "image": 2, "id": 22, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/64c722d2a3c5ce02e39a7e08a2845a10.jpg", + "path": "crops/64c722d2a3c5ce02e39a7e08a2845a10.jpg", "binary_label": "moth", "specific_label": "Antaeotricha schlaegeri", - "specific_label_score": 0.14964184165000916, - "cost": 0.46193150285828655 + "specific_label_score": 0.1581038385629654, + "cost": 0.4612160146343883 }, { "event": "2022-07-28", @@ -632,11 +516,11 @@ "frame": 2, "image": 3, "id": 38, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/0bb4c3e7e4a9b26fb6ecd984126c026d.jpg", + "path": "crops/dc755b668229ec4e8dee38a83d1d5d8c.jpg", "binary_label": "moth", "specific_label": "Crambus albellus", - "specific_label_score": 0.5847471356391907, - "cost": 0.8770945889496965 + "specific_label_score": 0.5698991417884827, + "cost": 0.8748914180950089 }, { "event": "2022-07-28", @@ -644,25 +528,11 @@ "frame": 3, "image": 4, "id": 50, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/e84d0da41ac73067cc92173bd122e051.jpg", + "path": "crops/e84d0da41ac73067cc92173bd122e051.jpg", "binary_label": "moth", "specific_label": "Crambus albellus", - "specific_label_score": 0.942969560623169, - "cost": 0.1925447533484664 - } - ], - "20220728-SEQ-9": [ - { - "event": "2022-07-28", - "sequence": "20220728-SEQ-9", - "frame": 0, - "image": 1, - "id": 9, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/7f9d3ed01b92984b7c470bb79bf16dae.jpg", - "binary_label": "moth", - "specific_label": "Plodia interpunctella", - "specific_label_score": 0.13182422518730164, - "cost": null + "specific_label_score": 0.9432279467582703, + "cost": 0.1839993937822919 } ] -} \ No newline at end of file +} diff --git a/trapdata/tests/results/vermont.json b/trapdata/tests/results/vermont.json index 6549c3a8..e26409ed 100644 --- a/trapdata/tests/results/vermont.json +++ b/trapdata/tests/results/vermont.json @@ -6,7 +6,7 @@ "frame": 0, "image": 2, "id": 1, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/c770465b18caac250fe1e18281bce99a.jpg", + "path": "crops/c770465b18caac250fe1e18281bce99a.jpg", "binary_label": "moth", "specific_label": "Lambdina fiscellaria", "specific_label_score": 0.25860387086868286, @@ -20,7 +20,7 @@ "frame": 0, "image": 2, "id": 2, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/cbaf562563b6430939f11c3e48c01b0c.jpg", + "path": "crops/cbaf562563b6430939f11c3e48c01b0c.jpg", "binary_label": "moth", "specific_label": "Campaea perlata", "specific_label_score": 0.2461177408695221, @@ -34,7 +34,7 @@ "frame": 0, "image": 2, "id": 3, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/4333c822b450400a5d03e880bfde16cc.jpg", + "path": "crops/4333c822b450400a5d03e880bfde16cc.jpg", "binary_label": "moth", "specific_label": "Cnephasia asseclana", "specific_label_score": 0.3257419168949127, @@ -48,7 +48,7 @@ "frame": 0, "image": 2, "id": 4, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/9d1603576e38431c8739e9c414b1297f.jpg", + "path": "crops/9d1603576e38431c8739e9c414b1297f.jpg", "binary_label": "moth", "specific_label": "Lophocampa caryae", "specific_label_score": 0.7309626340866089, @@ -62,7 +62,7 @@ "frame": 0, "image": 2, "id": 5, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/5de9d9afbbbc24fb7253ab95884cad91.jpg", + "path": "crops/5de9d9afbbbc24fb7253ab95884cad91.jpg", "binary_label": "moth", "specific_label": "Scoparia basalis", "specific_label_score": 0.11617226898670197, @@ -76,7 +76,7 @@ "frame": 0, "image": 2, "id": 6, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/b8cce6c49a1a63635a00d7374cca4986.jpg", + "path": "crops/b8cce6c49a1a63635a00d7374cca4986.jpg", "binary_label": "moth", "specific_label": "Archips packardiana", "specific_label_score": 0.8169540166854858, @@ -90,7 +90,7 @@ "frame": 0, "image": 2, "id": 7, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/505f353352c5efc6f32aee7a838b9ff0.jpg", + "path": "crops/505f353352c5efc6f32aee7a838b9ff0.jpg", "binary_label": "moth", "specific_label": "Lithacodia carneola", "specific_label_score": 0.0930459126830101, @@ -104,11 +104,11 @@ "frame": 0, "image": 2, "id": 8, - "path": "/Users/michael/Projects/AMI/USERDATA/crops/e891687374ad5f78e16b8fa893c9b058.jpg", + "path": "crops/e891687374ad5f78e16b8fa893c9b058.jpg", "binary_label": "moth", "specific_label": "Campaea perlata", "specific_label_score": 0.8783769607543945, "cost": null } ] -} \ No newline at end of file +}