def __init__(self, config_file = 'config.cfg', model = 'hg_refined_tiny_200', yoloModel = 'YOLO_small.ckpt', disable_yolo = True):
""" Initilize the Predictor
Args:
config_file : *.cfg file with model's parameters
model : *.index file's name. (weights to load)
yoloModel : *.ckpt file (YOLO weights to load)
"""
t = time()
params = process_config(config_file)
self.predict = PredictProcessor(params)
self.predict.color_palette()
self.predict.LINKS_JOINTS()
self.predict.model_init()
self.predict.load_model(load = model)
if disable_yolo:
pass
else:
self.predict.yolo_init()
self.predict.restore_yolo(load = yoloModel)
self.predict._create_prediction_tensor()
if True:
pass
else:
self.filter = VideoFilters()
print('Done: ', time() - t, ' sec.')
def __init__(self, config_file = 'config.cfg', model = 'hg_refined_tiny_200', yoloModel = 'YOLO_small.ckpt'):
""" Initilize the Predictor
Args:
config_file : *.cfg file with model's parameters
model : *.index file's name. (weights to load)
yoloModel : *.ckpt file (YOLO weights to load)
"""
t = time()
params = process_config(config_file)
self.predict = PredictProcessor(params)
self.predict.color_palette()
self.predict.LINKS_JOINTS()
self.predict.model_init()
self.predict.load_model(load = model)
self.predict.yolo_init()
self.predict.restore_yolo(load = yoloModel)
self.predict._create_prediction_tensor()
self.filter = VideoFilters()
print('Done: ', time() - t, ' sec.')
def show_prections(img, predictions, name):
for index, coord in enumerate(predictions):
keypt = (int(coord[1]), int(coord[0]))
text_loc = (keypt[0] + 7, keypt[1] + 7)
cv2.circle(img, keypt, 3, Palette[index], -1)
cv2.putText(img, str(index), text_loc, cv2.FONT_HERSHEY_DUPLEX, 0.5,
Palette[index], 1, cv2.LINE_AA)
cv2.imwrite(os.path.join(params['test_result_directory'], name), img)
if __name__ == '__main__':
print('-- Parsing Config File')
params = process_config('./config.cfg')
model = Inference(model=params['pretrained_model'])
bbox1 = [692, 1250, 1246, 1934]
bbox2 = [76, 810, 626, 1330]
bbox3 = [998, 888, 1602, 1400]
bbox4 = [670, 440, 1156, 856]
bounding_box = {
'cam_00_10_30': bbox1,
'cam_01_10_30': bbox2,
'cam_02_10_30': bbox3,
'cam_03_10_30': bbox4
}
img_paths = os.listdir(params['test_img_directory'])
for img_path in img_paths:
img = cv2.imread(os.path.join(params['test_img_directory'], img_path))
bbox = bounding_box[img_path[0:12]]
y = int(box[2])
w = int(box[3] / 2)
h = int(box[4] / 2)
prob = box[5]
bbox = np.asarray((max(0,x-w), max(0, y-h), min(shapeOd[1]-1, x+w), min(shapeOd[0]-1, y+h)))
cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 255, 0), 2)
cv2.rectangle(frame, (bbox[0], bbox[1] - 20),(bbox[2], bbox[1]), (125, 125, 125), -1)
cv2.putText(frame, class_name + ' : %.2f' % prob, (bbox[0] + 5, bbox[1] - 7), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1)
fps = 1/(time() - t)
cv2.putText(frame, str(fps)[:4] + ' fps' + ' PinS:' + str(len(result)), (20, 20), 2, 1, (0,0,0), thickness = 2)
cv2.imshow('Camera', frame)
ret, frame = cap.read()
if cv2.waitKey(1) == 27:
print('Stream Ended')
cv2.destroyAllWindows()
cap.release()
cv2.destroyAllWindows()
cap.release()
if __name__ == '__main__':
t = time()
params = process_config('configTiny.cfg')
predict = PredictProcessor(params)
predict.color_palette()
predict.LINKS_JOINTS()
predict.model_init()
predict.load_model(load = 'hg_refined_tiny_200')
predict.yolo_init()
predict.restore_yolo(load = 'YOLO_small.ckpt')
predict._create_prediction_tensor()
print('Done: ', time() - t, ' sec.')
