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core.py
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core.py
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from __future__ import print_function
import os
import argparse
import torch.backends.cudnn as cudnn
from data import cfg_mnet, cfg_re50
from layers.functions.prior_box import PriorBox
from utils.nms.py_cpu_nms import py_cpu_nms
from models.retinaface import RetinaFace
from utils.box_utils import decode, decode_landm
import time
import datetime
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import cv2
import torch
import gspread as gs
from oauth2client.service_account import ServiceAccountCredentials
import matplotlib.pyplot as plt
import multiprocessing as mp
from multiprocessing.pool import ThreadPool as Pool
# from pathos.multiprocessing import ProcessingPool as Pool
import requests
import platform
import secrets
class API:
"""Class for connecting sending raw data into google sheets and to miem nvr"""
def __init__(self, email_to_share):
self.scope = ['https://spreadsheets.google.com/feeds',
'https://www.googleapis.com/auth/drive']
self.credentials = ServiceAccountCredentials.from_json_keyfile_name(secrets.credentials_file,
self.scope)
self.client = gs.authorize(self.credentials)
# self.sheet_name = name
self.email_to_share = email_to_share
self.sheet_shared = False
self.nvr_key = {"key": secrets.server_key}
self.nvr_url = secrets.server_url
def write_table(self, filename, table):
"""writes data into sheet via its name. if sheet with that name does not exist, func will create new one
WARNING!!! If no email-to-share is not given, sheet will be barely reachable
filename - string, name of the sheet how it will be displayed on google disk
table - list of lists, data to be inserted"""
try:
sheet = self.client.open(filename)
except gs.exceptions.SpreadsheetNotFound:
sheet = self.client.create(filename)
if not self.sheet_shared:
self.sheet_shared = True
if isinstance(self.email_to_share, list):
for email in self.email_to_share:
sheet.share(email, perm_type='user', role='writer')
else:
sheet.share(self.email_to_share, perm_type='user', role='writer')
sheet = sheet.sheet1
if len(table) != 0:
cell_list = sheet.range(1, 1, len(table), 8)
for i in range(len(cell_list) // 8):
for j in range(8):
cell_list[i*8 + j].value = table[i][j]
sheet.update_cells(cell_list)
def send_to_nvr(self, filename):
""""function to send to miem nvr
filename - string, mp4 video with result"""
file = open(filename, 'rb')
files = {'file': file}
res = requests.post(self.nvr_url, files=files, headers=self.nvr_key)
os.remove(filename)
return res.status_code
class Classifier(nn.Module):
""" class for classifier based on pytorch """
def __init__(self):
super(Classifier, self).__init__()
# device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
self.conv1 = nn.Conv2d(1, 6, 5) # # # TODO<<"^~^">>TODO
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 24, 5)
self.fc1 = nn.Linear(24 * 9 * 9, 486)
self.fc2 = nn.Linear(486, 84)
self.fc3 = nn.Linear(84, 7)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 24 * 9 * 9)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
def analyse(obj, img_raw):
return obj.analyse(img_raw)
class Emanalisis():
""" main class for usage in emotion recognition
input mode - int, determines where class takes its data.
0 - from default webcam of device
1 - from ip camera
2 - from video
output mode - int, determines how output would look.
0 - classical opencv display, augments original video
1 - makes separate graph of emotions count with matplotlib. if record_video is True, will record only graph
2 - graph on black background with all info. Needed for nvr
record_video - bool, if True, will record output on mp4.
email_to_share - list of strings/string, email(s) to share sheet. If there is none, sheets will be barely reachable
channel - int/string, sourse for input data. If input_mode is 0, it should be 0, if input_mode is 1, it'd be ip
address of camera, else it is name of mp4 video file
on_gpu - bool, if true, will use gpu for detection and classification. NEVER USE IF THERE IS NO GPU DEVICE.
display - bool, if true, will show output on screen.
only_headcount - bool, if true, will disable classification and graph drawing
send_to_nvr - bool, if true, will send recorded video into miem nvr"""
def __init__(self, input_mode = 0, output_mode = 0, record_video = False,
email_to_share = None, channel = 0, on_gpu = False,
display = False, only_headcount = False, send_to_nvr = False, parallel=False):
self.save_into_sheet = True
self.on_gpu = on_gpu
self.send_to_nvr = send_to_nvr
if email_to_share == None:
self.save_into_sheet = False
if self.save_into_sheet or self.send_to_nvr:
self.api = API(email_to_share)
uri = 'rtsp://' + secrets.ip_camera_login + ':' + secrets.ip_camera_password + \
'@{}:554/cam/realmonitor?channel=1&subtype=0&unicast=true&proto=Onvif'
self.input_mode = input_mode
self.output_mode = output_mode # 0 - pretty display, 1 - separate graph, 2 - graph with black background
self.record_video = record_video
self.display = display
self.only_headcount = only_headcount
if input_mode == 0:
self.channel = 0 # webcam
elif input_mode == 1: # ip camera
self.channel = uri.format(channel)
self.ip = channel
elif input_mode == 2: # video
self.channel = channel
if parallel and not on_gpu:
self.parallel = True
else:
self.parallel = False
# from classifier by Sizykh Ivan
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
self.class_labels = ['ANGRY', 'DISGUST', 'FEAR', 'HAPPY', 'SAD', 'SURPRISE', 'NEUTRAL']
# PATH = "./check_points_4/net_714.pth"
PATH = "./net_714.pth"
if self.on_gpu:
self.classifier = Classifier().to(self.device)
self.classifier.load_state_dict(torch.load(PATH))
else:
self.classifier = Classifier()
self.classifier.load_state_dict(torch.load(PATH, map_location={'cuda:0': 'cpu'}))
# from detector by Belyakova Katerina
self.parser = argparse.ArgumentParser(description='Retinaface')
self.parser.add_argument('-m', '--trained_model', default='./weights/Resnet50_Final.pth',
type=str, help='Trained state_dict file path to open')
self.parser.add_argument('--network', default='resnet50', help='Backbone network mobile0.25 or resnet50')
self.parser.add_argument('--cpu', action="store_true", default=False, help='Use cpu inference')
self.parser.add_argument('--confidence_threshold', default=0.02, type=float, help='confidence_threshold')
self.parser.add_argument('--top_k', default=5000, type=int, help='top_k')
self.parser.add_argument('--nms_threshold', default=0.4, type=float, help='nms_threshold')
self.parser.add_argument('--keep_top_k', default=750, type=int, help='keep_top_k')
self.parser.add_argument('-s', '--save_image', action="store_true", default=True, help='show detection results')
self.parser.add_argument('--vis_thres', default=0.6, type=float, help='visualization_threshold')
self.parser.add_argument('-v', '--video', default='vid.mp4', type=str)
self.parser_args = self.parser.parse_args()
self.resize = 1
"""sets parameters for RetinaFace, prerun() is used once while first usege of run()"""
torch.set_grad_enabled(False)
cfg = None
if self.parser_args.network == "mobile0.25":
cfg = cfg_mnet
elif self.parser_args.network == "resnet50":
cfg = cfg_re50
# net and model
detector = RetinaFace(cfg=cfg, phase='test')
detector = self.load_model(model=detector, pretrained_path=self.parser_args.trained_model,
load_to_cpu=self.parser_args.cpu)
detector.eval()
print('Finished loading model!')
print(detector)
if self.on_gpu:
cudnn.benchmark = True
self.detector = detector.to(self.device)
else:
self.detector = detector
self.cfg = cfg
# let those be, might be used for further improvements
def load_model(self, model, pretrained_path, load_to_cpu):
"""load model of RetinaFace for face detection"""
def remove_prefix(state_dict, prefix):
''' Old style model is stored with all names of parameters sharing common prefix 'module.' '''
print('remove prefix \'{}\''.format(prefix))
f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x
return {f(key): value for key, value in state_dict.items()}
def check_keys(model, pretrained_state_dict):
ckpt_keys = set(pretrained_state_dict.keys())
model_keys = set(model.state_dict().keys())
used_pretrained_keys = model_keys & ckpt_keys
unused_pretrained_keys = ckpt_keys - model_keys
missing_keys = model_keys - ckpt_keys
print('Missing keys:{}'.format(len(missing_keys)))
print('Unused checkpoint keys:{}'.format(len(unused_pretrained_keys)))
print('Used keys:{}'.format(len(used_pretrained_keys)))
assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint'
return True
print('Loading pretrained model from {}'.format(pretrained_path))
if load_to_cpu:
pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage)
else:
if self.on_gpu:
device = torch.cuda.current_device()
pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, location: storage.cuda(device))
else:
pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, location: storage)
if "state_dict" in pretrained_dict.keys():
pretrained_dict = remove_prefix(pretrained_dict['state_dict'], 'module.')
else:
pretrained_dict = remove_prefix(pretrained_dict, 'module.')
check_keys(model, pretrained_dict)
model.load_state_dict(pretrained_dict, strict=False)
return model
def load_video(self, video, num_fps):
"""load video for analysis.
video - string, name of the file
num_fps - int/float, which fps output will be, mainly used for lowering amount of frames taken to analyse"""
fps = 1 / num_fps
cap = cv2.VideoCapture(video)
# cap = cv2.VideoCapture(0)
ret, frame = cap.read()
t = time.time()
ret = True
# os.chdir(r"frames")
out_arr = []
while ret:
ret, frame = cap.read()
if time.time() - t >= fps:
t = time.time()
out_arr.append(frame)
# cv2.imwrite("frame " + str(count_frames) + ".jpg", frame)
return np.asarray(out_arr)
def make_video(self, filename, frames, num_fps):
"""function that creates new video file
filename - string, name of file WITHOUT '.mp4'
frames - list of lists, frames in BRG format
num_fps - int/float, fps of said video"""
mode = ""
if self.input_mode == 0:
mode = "wc_"
elif self.input_mode == 1:
mode = self.ip
elif self.input_mode == 2:
mode = str(self.channel) # datetime.datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
string = filename + '.mp4'
writer = cv2.VideoWriter(
string,
cv2.VideoWriter_fourcc(*'MP4V'), # codec
num_fps, # fps
(frames[0].shape[1], frames[0].shape[0])) # width, height
for frame in (frames):
writer.write(frame)
writer.release()
cv2.destroyAllWindows()
if platform.system() != "Windows":
d = datetime.datetime.strptime(filename, "%Y-%m-%d_%H-%M")
new_string = d.strftime("%Y-%m-%d_%H:%M") + ".mp4"
os.rename(string, new_string)
return new_string
return string
def detect_faces(self, img_raw):
img = np.float32(img_raw)
im_height, im_width, _ = img.shape
scale = torch.Tensor([img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
img -= (104, 117, 123)
img = img.transpose(2, 0, 1)
img = torch.from_numpy(img).unsqueeze(0)
if self.on_gpu:
img = img.to(self.device)
scale = scale.to(self.device)
# graph = 0
tic = time.time()
loc, conf, landms = self.detector(img) # forward pass
print('net forward time: {:.4f}'.format(time.time() - tic))
priorbox = PriorBox(self.cfg, image_size=(im_height, im_width))
priors = priorbox.forward()
if self.on_gpu:
priors = priors.to(self.device)
prior_data = priors.data
boxes = decode(loc.data.squeeze(0), prior_data, self.cfg['variance'])
boxes = boxes * scale / self.resize
boxes = boxes.cpu().numpy()
scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
landms = decode_landm(landms.data.squeeze(0), prior_data, self.cfg['variance'])
scale1 = torch.Tensor([img.shape[3], img.shape[2], img.shape[3], img.shape[2],
img.shape[3], img.shape[2], img.shape[3], img.shape[2],
img.shape[3], img.shape[2]])
if self.on_gpu:
scale1 = scale1.to(self.device)
landms = landms * scale1 / self.resize
landms = landms.cpu().numpy()
# ignore low scores
inds = np.where(scores > self.parser_args.confidence_threshold)[0]
boxes = boxes[inds]
landms = landms[inds]
scores = scores[inds]
# keep top-K before NMS
order = scores.argsort()[::-1][:self.parser_args.top_k]
boxes = boxes[order]
landms = landms[order]
scores = scores[order]
# do NMS
dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
keep = py_cpu_nms(dets, self.parser_args.nms_threshold)
dets = dets[keep, :]
landms = landms[keep]
# keep top-K faster NMS
dets = dets[:self.parser_args.keep_top_k, :]
landms = landms[:self.parser_args.keep_top_k, :]
dets = np.concatenate((dets, landms), axis=1)
return dets
def classify_face(self, crop_img):
dim = (48, 48)
resized = cv2.resize(crop_img, dim)
gray_res = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)
roi = gray_res
roi = np.array(roi)
roi = 2 * (roi.astype("float") / 255.0) - 1
roi = np.expand_dims(roi, axis=2)
roi = np.expand_dims(roi, axis=0)
roi = np.expand_dims(roi, axis=0)
roi = torch.from_numpy(roi)
roi = roi.float()
roi = roi.squeeze(dim=4)
# make a prediction on the ROI, then lookup the class
tic = time.time()
if self.on_gpu:
preds = self.classifier(roi.to(self.device))[0]
else:
preds = self.classifier(roi)[0]
print(str(time.time() - tic) + " to classify")
return preds
def analyse(self, img_raw):
dets = self.detect_faces(img_raw)
display_img = np.copy(img_raw)
head_count = 0
emotions_count = np.zeros(7)
local_table = []
labels = []
# show image
for b in dets:
if b[4] < self.parser_args.vis_thres:
continue
text = "{:.4f}".format(b[4])
b = list(map(int, b))
crop_img = img_raw[b[1]:b[3], b[0]:b[2]]
if crop_img.sum() != 0:
head_count = head_count + 1
if not self.only_headcount:
preds = self.classify_face(crop_img)
label = self.class_labels[preds.argmax()]
emotions_count[preds.argmax()] += 1
preds = preds.tolist()
emotions_count = emotions_count
labels.append(label)
local_table.append(preds)
return dets[:4], local_table, labels, head_count, emotions_count
def augment_frame(self, array):
if len(array) == 13:
img_raw, dets, local_table, labels, emotions_count, head_count, table, i, \
emotions_lapse, stop_time, fps_factor, figure, graphes = array
else:
img_raw, dets, local_table, labels, emotions_count, head_count, table, i, \
emotions_lapse, stop_time, fps_factor = array
# dets = self.detect_faces(img_raw)
display_img = np.copy(img_raw)
# head_count = 0
# emotions_count = np.zeros(7)
# show image
for j in range(len(local_table)):
# if b[4] < self.parser_args.vis_thres:
# continue
# text = "{:.4f}".format(b[4])
# b = list(map(int, b))
# crop_img = img_raw[b[1]:b[3], b[0]:b[2]]
# dim = (48, 48)
# if crop_img.sum() != 0:
# head_count = head_count + 1
if not self.only_headcount:
# preds = self.classify_face(crop_img, dim)
label = labels[j]
b = list(map(int, dets[j]))
# preds = local_table[j]
# i is timestamp for temporal
# table.append([i, preds[0], preds[1], preds[2], preds[3], preds[4], preds[5], preds[6]])
if self.output_mode != 2:
cv2.rectangle(display_img, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 2)
cx = b[0]
cy = b[1] + 12
if not self.only_headcount:
cv2.putText(display_img, label, (cx, cy), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 3)
if self.output_mode == 2:
display_img = np.zeros_like(img_raw)
display_img = np.resize(display_img, (400, 1920, 3))
# emotions_lapse.append(emotions_count.tolist())
if not self.only_headcount:
ntable = np.asarray(table)
shift = 0
nemotions_lapse = np.asarray(emotions_lapse) * display_img.shape[0] / (2 * head_count)
x = range(1, len(emotions_lapse) + 1)
x = np.asarray(x)
def softmax(x):
return np.exp(x) / sum(np.exp(x))
ntable = softmax(ntable)
unchanged_angry_scores = np.asarray(emotions_lapse)[:, 0]
unchanged_disgust_scores = np.asarray(emotions_lapse)[:, 1]
unchanged_fear_scores = np.asarray(emotions_lapse)[:, 2]
unchanged_happy_scores = np.asarray(emotions_lapse)[:, 3]
unchanged_sad_scores = np.asarray(emotions_lapse)[:, 4]
unchanged_surprise_scores = np.asarray(emotions_lapse)[:, 5]
unchanged_neutral_scores = np.asarray(emotions_lapse)[:, 6]
# attention_coef = np.max(np.max(np.flip(emotions_lapse)[0,:],axis=0)) / head_count
attention_coef = (np.max(emotions_count)) / head_count
# attention_coef = np.mean(emotions_count) / np.max(emotions_count)
if self.output_mode == 0 or self.output_mode == 2:
if not self.only_headcount:
if self.output_mode == 0:
ntable = ntable * 50
# nemotions_lapse = nemotions_lapse * 5
else:
ntable = ntable * 300
# nemotions_lapse = nemotions_lapse * 30
if stop_time == -1:
scale = (display_img.shape[1] - 30) / (30 * 60 * 25 / fps_factor)
else:
scale = (display_img.shape[1] - 30) / (stop_time * 25 / fps_factor)
x = x * scale + 15
shift = display_img.shape[0] / 2 - 10
angry_scores = shift + nemotions_lapse[:, 0]
disgust_scores = shift + nemotions_lapse[:, 1]
fear_scores = shift + nemotions_lapse[:, 2]
happy_scores = shift - nemotions_lapse[:, 3]
sad_scores = shift + nemotions_lapse[:, 4]
surprise_scores = shift - nemotions_lapse[:, 5]
neutral_scores = shift - nemotions_lapse[:, 6]
# possitive_scores = shift - nemotions_lapse[:,3] - nemotions_lapse[:,5] - \
# nemotions_lapse[:,6]
# negative_scores = shift + nemotions_lapse[:, 0] + nemotions_lapse[:, 1] + \
# nemotions_lapse[:, 2] + nemotions_lapse[:, 4]
# possitive_sum = unchanged_happy_scores + unchanged_surprise_scores + \
# unchanged_neutral_scores
# negative_sum = unchanged_angry_scores + unchanged_disgust_scores + unchanged_fear_scores \
# + unchanged_sad_scores
plot = np.vstack((x, angry_scores)).astype(np.int32).T
cv2.polylines(display_img, [plot], isClosed=False, thickness=2, color=(0, 0, 255))
cord = (plot[len(plot) - 1][0], plot[len(plot) - 1][1])
cv2.putText(display_img,
self.class_labels[0] + " " + str(int(np.flip(unchanged_angry_scores)[0])),
cord, cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 1)
plot = np.vstack((x, disgust_scores)).astype(np.int32).T
cv2.polylines(display_img, [plot], isClosed=False, thickness=2, color=(0, 255, 0))
cord = (plot[len(plot) - 1][0], plot[len(plot) - 1][1])
cv2.putText(display_img,
self.class_labels[1] + " " + str(int(np.flip(unchanged_disgust_scores)[0]))
, cord, cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 1)
plot = np.vstack((x, fear_scores)).astype(np.int32).T
cv2.polylines(display_img, [plot], isClosed=False, thickness=2, color=(255, 255, 255))
cord = (plot[len(plot) - 1][0], plot[len(plot) - 1][1])
cv2.putText(display_img,
self.class_labels[2] + " " + str(int(np.flip(unchanged_fear_scores)[0]))
, cord, cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 1)
plot = np.vstack((x, happy_scores)).astype(np.int32).T
cv2.polylines(display_img, [plot], isClosed=False, thickness=2, color=(0, 255, 255))
cord = (plot[len(plot) - 1][0], plot[len(plot) - 1][1])
cv2.putText(display_img,
self.class_labels[3] + " " + str(int(np.flip(unchanged_happy_scores)[0]))
, cord, cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 255), 1)
plot = np.vstack((x, sad_scores)).astype(np.int32).T
cv2.polylines(display_img, [plot], isClosed=False, thickness=2, color=(153, 153, 255))
cord = (plot[len(plot) - 1][0], plot[len(plot) - 1][1])
cv2.putText(display_img,
self.class_labels[4] + " " + str(int(np.flip(unchanged_sad_scores)[0]))
, cord, cv2.FONT_HERSHEY_SIMPLEX, 0.6, (153, 153, 255), 1)
plot = np.vstack((x, surprise_scores)).astype(np.int32).T
cv2.polylines(display_img, [plot], isClosed=False, thickness=2, color=(153, 0, 76))
cord = (plot[len(plot) - 1][0], plot[len(plot) - 1][1])
cv2.putText(display_img,
self.class_labels[5] + " " + str(int(np.flip(unchanged_surprise_scores)[0]))
, cord, cv2.FONT_HERSHEY_SIMPLEX, 0.6, (153, 0, 76), 1)
plot = np.vstack((x, neutral_scores)).astype(np.int32).T
cv2.polylines(display_img, [plot], isClosed=False, thickness=2, color=(96, 96, 96))
cord = (plot[len(plot) - 1][0], plot[len(plot) - 1][1])
cv2.putText(display_img,
self.class_labels[6] + " " + str(int(np.flip(unchanged_neutral_scores)[0]))
, cord, cv2.FONT_HERSHEY_SIMPLEX, 0.6, (96, 96, 96), 1)
cv2.putText(display_img, "Head count: " + str(head_count),
(5, 30), cv2.FONT_HERSHEY_TRIPLEX, 1, (60, 20, 220))
cv2.putText(display_img, "Attention coef: " + str(round(attention_coef, 2)),
(285, 30), cv2.FONT_HERSHEY_TRIPLEX, 1, (60, 20, 220))
# plot = np.vstack((x, table))
# dots on facial features
# cv2.circle(img_raw, (b[5], b[6]), 1, (0, 0, 255), 4)
# cv2.circle(img_raw, (b[7], b[8]), 1, (0, 255, 255), 4)
# cv2.circle(img_raw, (b[9], b[10]), 1, (255, 0, 255), 4)
# cv2.circle(img_raw, (b[11], b[12]), 1, (0, 255, 0), 4)
# cv2.circle(img_raw, (b[13], b[14]), 1, (255, 0, 0), 4)
if self.output_mode == 1:
graphes[0].set_xdata(x[x.shape[0] - 100:x.shape[0] - 1])
graphes[0].set_ydata(unchanged_angry_scores[x.shape[0] - 100:x.shape[0] - 1])
graphes[1].set_xdata(x[x.shape[0] - 100:x.shape[0] - 1])
graphes[1].set_ydata(unchanged_disgust_scores[x.shape[0] - 100:x.shape[0] - 1])
graphes[2].set_xdata(x[x.shape[0] - 100:x.shape[0] - 1])
graphes[2].set_ydata(unchanged_fear_scores[x.shape[0] - 100:x.shape[0] - 1])
graphes[3].set_xdata(x[x.shape[0] - 100:x.shape[0] - 1])
graphes[3].set_ydata(unchanged_happy_scores[x.shape[0] - 100:x.shape[0] - 1])
graphes[4].set_xdata(x[x.shape[0] - 100:x.shape[0] - 1])
graphes[4].set_ydata(unchanged_sad_scores[x.shape[0] - 100:x.shape[0] - 1])
graphes[5].set_xdata(x[x.shape[0] - 100:x.shape[0] - 1])
graphes[5].set_ydata(unchanged_surprise_scores[x.shape[0] - 100:x.shape[0] - 1])
graphes[6].set_xdata(x[x.shape[0] - 100:x.shape[0] - 1])
graphes[6].set_ydata(unchanged_neutral_scores[x.shape[0] - 100:x.shape[0] - 1])
figure.canvas.draw()
figure.canvas.flush_events()
axa = plt.gca()
axa.relim()
axa.autoscale_view(True, True, True)
if self.record_video:
figure.tight_layout(pad=0)
axa.margins(0)
plot = np.frombuffer(figure.canvas.tostring_rgb(), dtype=np.uint8)
plot = plot.reshape(figure.canvas.get_width_height()[::-1] + (3,))
return plot
return display_img
def run(self, filename, fps_factor=1, stop_time=-1):
"""main function that does all the work.
filename - string, name of video file and google sheet.
fps_factor - int, determines how often frame is taken from input. It takes every Nth frame from source
stop_time - int, timer until it stops recording. -1 is used to work indefinitely(or until Enter key is pressed)
"""
# to load RetinaFace model
# if self.detector is None or self.cfg is None:
# self.prerun()
table = []
if self.record_video:
frames = []
cap = cv2.VideoCapture(self.channel)#self.uri.format(ip))
i = 0
emotions_lapse = []
start_time = time.time()
if self.output_mode == 1:
plt.ion()
figure = plt.figure()
ax = figure.add_subplot(111)
angry_graph, = ax.plot(0, 0, 'r-', label=self.class_labels[0])
disgust_graph, = ax.plot(0,0, 'g-', label=self.class_labels[1])
fear_graph, = ax.plot(0,0, 'k-', label=self.class_labels[2])
happy_graph, = ax.plot(0,0, 'y-', label=self.class_labels[3])
sad_graph, = ax.plot(0,0, 'c-', label=self.class_labels[4])
surprise_graph, = ax.plot(0,0,'m-', label=self.class_labels[5])
neutral_graph, = ax.plot(0,0,'b-', label=self.class_labels[6])
ax.legend()
graphes = [angry_graph, disgust_graph, fear_graph, happy_graph, sad_graph, surprise_graph, neutral_graph]
if not self.parallel:
while True:
ret, img_raw = cap.read()
# try:
if i % fps_factor == 0:
dets, local_table, labels, head_count, emotions_count = self.analyse(img_raw)
emotions_lapse.append(emotions_count)
for pred in local_table:
table.append([i, pred[0], pred[1], pred[2], pred[3], pred[4], pred[5], pred[6]])
if self.output_mode == 1:
frame = self.augment_frame([img_raw, dets, local_table, labels, emotions_count, head_count, table,
i, emotions_lapse, stop_time, fps_factor, figure, graphes])
else:
frame = self.augment_frame([img_raw, dets, local_table, labels, emotions_count, head_count, table,
i, emotions_lapse, stop_time, fps_factor])
if self.record_video:
frames.append(frame)
if self.display:
if frame.shape[1] >= 1000:
percent = 50
width = int(frame.shape[1] * percent / 100)
height = int(frame.shape[0] * percent / 100)
new_shape = (width, height)
frame = cv2.resize(frame, new_shape, interpolation=cv2.INTER_AREA)
cv2.imshow('Face Detector', frame)
# except:
# cap = cv2.VideoCapture(self.channel)
# if time.time() - start_time >= stop_time:
# break
# continue
if cv2.waitKey(1) == 13 or time.time() - start_time >= stop_time:
break
i += 1
else:
# pass
cpus = 2
while True:
pool = Pool(processes=cpus)
raw_images = []
j = 0
while j in range(cpus):
if i % fps_factor:
ret, img_raw = cap.read()
raw_images.append(img_raw)
j += 1
i += 1
results = pool.map(self.analyse, raw_images)
for l in range(len(results)):
dets, local_table, labels, head_count, emotions_count = results[l]
img_raw = raw_images[l]
emotions_lapse.append(emotions_count)
for pred in local_table:
table.append([i, pred[0], pred[1], pred[2], pred[3], pred[4], pred[5], pred[6]])
if self.output_mode == 1:
frame = self.augment_frame([img_raw, dets, local_table, labels, emotions_count, head_count, table,
i - cpus + l, emotions_lapse, stop_time, fps_factor, figure, graphes])
else:
frame = self.augment_frame([img_raw, dets, local_table, labels, emotions_count, head_count, table,
i - cpus + l, emotions_lapse, stop_time, fps_factor])
if self.record_video:
frames.append(frame)
if self.display:
if frame.shape[1] >= 1000:
percent = 50
width = int(frame.shape[1] * percent / 100)
height = int(frame.shape[0] * percent / 100)
new_shape = (width, height)
frame = cv2.resize(frame, new_shape, interpolation=cv2.INTER_AREA)
cv2.imshow('Face Detector', frame)
if cv2.waitKey(1) == 13 or time.time() - start_time >= stop_time:
break
fps = cap.get(cv2.CAP_PROP_FPS)
cap.release()
cv2.destroyAllWindows()
plt.close()
if self.save_into_sheet:
self.api.write_table(filename, table)
if self.record_video:
if self.send_to_nvr:
fps = len(frames) / 1800
video = self.make_video(filename, frames, fps)
self.api.send_to_nvr(video)
else:
video = self.make_video(filename, frames, fps / fps_factor)