video tools

sgoldenlab · May 8, 2024 · 2ac5308 · 2ac5308
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Showing 11 changed files with 343 additions and 202 deletions.
diff --git a/docs/_static/img/change_single_video_fps.mp4 b/docs/_static/img/change_single_video_fps.mp4
diff --git a/docs/conf.py b/docs/conf.py
@@ -27,7 +27,8 @@
               #'sphinx_autodoc_typehints',
               'sphinx_togglebutton',
               'nbsphinx',
-              'sphinx.ext.intersphinx']
+              'sphinx.ext.intersphinx',
+              'sphinxcontrib.video']
 intersphinx_mapping = {
   'python': ('https://docs.python.org/3', None),
   }

diff --git a/docs/environment.yml b/docs/environment.yml
@@ -11,6 +11,7 @@ dependencies:
   - pip:
       - pip==21.2.2
       - sphinx-autodoc-typehints
+      - sphinxcontrib-video
       - sphinx-togglebutton==0.3.2
       - sphinx_rtd_theme
       - simba-uw-tf-dev

diff --git a/simba/SimBA.py b/simba/SimBA.py
@@ -132,13 +132,12 @@
     InitiateClipMultipleVideosByFrameNumbersPopUp,
     InitiateClipMultipleVideosByTimestampsPopUp, InteractiveClahePopUp,
     MergeFrames2VideoPopUp, MultiCropPopUp, MultiShortenPopUp,
-    SuperImposeFrameCountPopUp, VideoRotatorPopUp, VideoTemporalJoinPopUp)
+    SuperImposeFrameCountPopUp, VideoRotatorPopUp, VideoTemporalJoinPopUp, DownsampleMultipleVideosPopUp, DownsampleSingleVideoPopUp)
 from simba.ui.pop_ups.visualize_pose_in_dir_pop_up import \
     VisualizePoseInFolderPopUp
 from simba.ui.tkinter_functions import DropDownMenu, Entry_Box, FileSelect
 from simba.ui.video_info_ui import VideoInfoTable
-from simba.utils.checks import (check_ffmpeg_available,
-                                check_file_exist_and_readable, check_int)
+from simba.utils.checks import (check_ffmpeg_available, check_file_exist_and_readable, check_int)
 from simba.utils.custom_feature_extractor import CustomFeatureExtractor
 from simba.utils.enums import OS, Defaults, Formats, Paths, TagNames
 from simba.utils.errors import InvalidInputError
@@ -1613,12 +1612,7 @@ def __init__(self):
         )
 
         menu.add_cascade(label="Tools", menu=video_process_menu)
-        video_process_menu.add_cascade(
-            label="Change fps...",
-            compound="left",
-            image=self.menu_icons["fps"]["img"],
-            menu=fps_menu,
-        )
+        video_process_menu.add_cascade(label="Change fps...", compound="left", image=self.menu_icons["fps"]["img"], menu=fps_menu)
 
         clip_video_menu = Menu(menu)
         clip_video_menu.add_command(label="Clip single video", command=ClipVideoPopUp)
@@ -1647,36 +1641,11 @@ def __init__(self):
         )
 
         crop_video_menu = Menu(menu)
-        crop_video_menu.add_command(
-            label="Crop videos",
-            compound="left",
-            image=self.menu_icons["crop"]["img"],
-            command=CropVideoPopUp,
-        )
-        crop_video_menu.add_command(
-            label="Crop videos (circles)",
-            compound="left",
-            image=self.menu_icons["circle"]["img"],
-            command=CropVideoCirclesPopUp,
-        )
-        crop_video_menu.add_command(
-            label="Crop videos (polygons)",
-            compound="left",
-            image=self.menu_icons["polygon"]["img"],
-            command=CropVideoPolygonsPopUp,
-        )
-        crop_video_menu.add_command(
-            label="Multi-crop",
-            compound="left",
-            image=self.menu_icons["crop"]["img"],
-            command=MultiCropPopUp,
-        )
-        video_process_menu.add_cascade(
-            label="Crop videos...",
-            compound="left",
-            image=self.menu_icons["crop"]["img"],
-            menu=crop_video_menu,
-        )
+        crop_video_menu.add_command(label="Crop videos", compound="left", image=self.menu_icons["crop"]["img"], command=CropVideoPopUp)
+        crop_video_menu.add_command(label="Crop videos (circles)", compound="left", image=self.menu_icons["circle"]["img"], command=CropVideoCirclesPopUp)
+        crop_video_menu.add_command(label="Crop videos (polygons)", compound="left", image=self.menu_icons["polygon"]["img"], command=CropVideoPolygonsPopUp)
+        crop_video_menu.add_command(label="Multi-crop", compound="left", image=self.menu_icons["crop"]["img"], command=MultiCropPopUp)
+        video_process_menu.add_cascade(label="Crop videos...", compound="left", image=self.menu_icons["crop"]["img"], menu=crop_video_menu)
 
         format_menu = Menu(video_process_menu)
         format_menu.add_command(label="Change image file formats", command=ChangeImageFormatPopUp)
@@ -1725,12 +1694,16 @@ def __init__(self):
             command=MakePathPlotPopUp,
         )
 
-        video_process_menu.add_command(
-            label="Down-sample videos",
-            compound="left",
-            image=self.menu_icons["sample"]["img"],
-            command=DownsampleVideoPopUp,
-        )
+        downsample_video_menu = Menu(video_process_menu)
+        downsample_video_menu.add_command(label="Down-sample single video", command=DownsampleSingleVideoPopUp)
+        downsample_video_menu.add_command(label="Down-sample multiple videos", command=DownsampleMultipleVideosPopUp)
+        video_process_menu.add_cascade(label="Down-sample video...", compound="left", image=self.menu_icons["sample"]["img"], menu=downsample_video_menu)
+
+
+        #video_process_menu.add_command(label="Down-sample videos",compound="left",image=self.menu_icons["sample"]["img"],command=DownsampleVideoPopUp)
+
+
+
         video_process_menu.add_cascade(
             label="Drop body-parts from tracking data",
             compound="left",

diff --git a/simba/plotting/ROI_feature_visualizer_mp.py b/simba/plotting/ROI_feature_visualizer_mp.py
@@ -64,34 +64,10 @@ def __insert_texts(shape_info: dict, img: np.ndarray):
             for cnt, animal_data in bp_lk.items():
                 animal, animal_bp, _ = animal_data
                 animal_name = f"{animal} {animal_bp}"
-                cv2.putText(
-                    img,
-                    text_locations[animal_name][shape_name]["in_zone_text"],
-                    text_locations[animal_name][shape_name]["in_zone_text_loc"],
-                    font,
-                    font_size,
-                    shape_color,
-                    1,
-                )
-                cv2.putText(
-                    img,
-                    text_locations[animal_name][shape_name]["distance_text"],
-                    text_locations[animal_name][shape_name]["distance_text_loc"],
-                    font,
-                    font_size,
-                    shape_color,
-                    1,
-                )
+                cv2.putText(img, text_locations[animal_name][shape_name]["in_zone_text"], text_locations[animal_name][shape_name]["in_zone_text_loc"], font, font_size, shape_color, 1)
+                cv2.putText(img, text_locations[animal_name][shape_name]["distance_text"], text_locations[animal_name][shape_name]["distance_text_loc"], font, font_size, shape_color, 1)
                 if directing_data is not None and style_attr[DIRECTIONALITY]:
-                    cv2.putText(
-                        img,
-                        text_locations[animal][shape_name]["directing_text"],
-                        text_locations[animal][shape_name]["directing_text_loc"],
-                        font,
-                        font_size,
-                        shape_color,
-                        1,
-                    )
+                    cv2.putText(img, text_locations[animal][shape_name]["directing_text"], text_locations[animal][shape_name]["directing_text_loc"], font, font_size, shape_color, 1)
         return img
 
     fourcc = cv2.VideoWriter_fourcc(*Formats.MP4_CODEC.value)
@@ -101,7 +77,7 @@ def __insert_texts(shape_info: dict, img: np.ndarray):
     save_path = os.path.join(save_temp_dir, f"{group_cnt}.mp4")
     writer = cv2.VideoWriter(save_path, fourcc, video_meta_data["fps"], (video_meta_data["width"] * 2, video_meta_data["height"]))
     cap = cv2.VideoCapture(video_path)
-    cap.set(1, start_frm)
+    cap.set(1, current_frm)
     while current_frm <= end_frm:
         ret, img = cap.read()
         if ret:
@@ -141,9 +117,9 @@ def __insert_texts(shape_info: dict, img: np.ndarray):
                                                                   color=shape_info[shape_name]["Color BGR"],
                                                                   thickness=shape_info[shape_name]["Thickness"],
                                                                   style=style_attr[DIRECTIONALITY_STYLE])
-            current_frm += 1
             writer.write(np.uint8(img))
             print(f"Multiprocessing frame: {current_frm} / {video_meta_data['frame_count']}  on core {group_cnt}...")
+            current_frm += 1
         else:
             break
     writer.release()
@@ -191,7 +167,8 @@ def __init__(self,
         if platform.system() == "Darwin":
             multiprocessing.set_start_method("spawn", force=True)
         check_int(name=f"{self.__class__.__name__} core_cnt",value=core_cnt,min_value=-1,max_value=find_core_cnt()[0])
-        if core_cnt == -1: core_cnt = find_core_cnt()[0]
+        if core_cnt == -1:
+            core_cnt = find_core_cnt()[0]
         check_file_exist_and_readable(file_path=video_path)
         ConfigReader.__init__(self, config_path=config_path)
         PlottingMixin.__init__(self)
@@ -206,18 +183,14 @@ def __init__(self,
             data=self.roi_dict, video_name=self.video_name
         )
         self.core_cnt, self.style_attr = core_cnt, style_attr
-        self.save_path = os.path.join(
-            self.roi_features_save_dir, f"{self.video_name}.mp4"
-        )
+        self.save_path = os.path.join(self.roi_features_save_dir, f"{self.video_name}.mp4")
         if not os.path.exists(self.roi_features_save_dir):
             os.makedirs(self.roi_features_save_dir)
         self.save_temp_dir = os.path.join(self.roi_features_save_dir, "temp")
         if os.path.exists(self.save_temp_dir):
             remove_a_folder(folder_dir=self.save_temp_dir)
         os.makedirs(self.save_temp_dir)
-        self.data_path = os.path.join(
-            self.outlier_corrected_dir, f"{self.video_name}.{self.file_type}"
-        )
+        self.data_path = os.path.join(self.outlier_corrected_dir, f"{self.video_name}.{self.file_type}")
         if not os.path.isfile(self.data_path):
             raise NoFilesFoundError(
                 msg=f"SIMBA ERROR: Could not find the file at path {self.data_path}. Make sure the data file exist to create ROI visualizations",
@@ -379,7 +352,7 @@ def __get_border_img_size(self, video_path: Union[str, os.PathLike]):
     def run(self):
         self.img_w_border_h, self.img_w_border_w = self.__get_border_img_size(video_path=self.video_path)
         self.__calc_text_locs()
-        frm_lst = np.arange(0, len(self.data_df)+1)
+        frm_lst = np.arange(0, len(self.data_df) + 1)
         frm_lst = np.array_split(frm_lst, self.core_cnt)
         frame_range = []
         for i in range(len(frm_lst)): frame_range.append((i, frm_lst[i]))
@@ -405,7 +378,7 @@ def run(self):
                                           roi_features_df=self.roi_features_df,
                                           animal_bps=self.animal_bp_dict)
             for cnt, result in enumerate(pool.imap(constants, frame_range, chunksize=self.multiprocess_chunksize)):
-                print(f"Batch core {result+1}/{self.core_cnt} complete...")
+               print(f"Batch core {result+1}/{self.core_cnt} complete...")
             print(f"Joining {self.video_name} multi-processed video...")
             concatenate_videos_in_folder(in_folder=self.save_temp_dir, save_path=self.save_path, video_format="mp4", remove_splits=True)
             self.timer.stop_timer()

diff --git a/simba/roi_tools/ROI_feature_analyzer.py b/simba/roi_tools/ROI_feature_analyzer.py
@@ -31,7 +31,6 @@ class ROIFeatureCreator(ConfigReader, FeatureExtractionMixin):
     .. note::
         `ROI tutorials <https://github.com/sgoldenlab/simba/blob/master/docs/ROI_tutorial_new.md>`__.
 
-
     :param Union[str, os.PathLike] config_path: Path to SimBA project config file in Configparser format.
     :param List[str] body_parts: List of the body-parts to use as proxy for animal location(s).
     :param Optional[Union[str, os.PathLike]] data_path: Path to folder or file holding the data used to calculate ROI aggregate statistics. If None, then defaults to the `project_folder/csv/outlier_corrected_movement_location` directory of the SimBA project. Default: None.