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Xu-jianwen 2021-08-11 11:45:40 +08:00
parent e7ceee5e2f
commit ba136ad1d3
1 changed files with 10 additions and 15 deletions
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video_face_rec.py
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@ -14,7 +14,7 @@ from deepface.commons import functions, realtime, distance as dst
from deepface.detectors import FaceDetector
def analysis(db_path, model_name = 'VGG-Face', detector_backend = 'opencv', distance_metric = 'cosine', source = 0, time_threshold = 5, frame_threshold = 5, smallest_faces=30):
def analysis(db_path, model_name = 'VGG-Face', detector_backend = 'opencv', distance_metric = 'cosine', source = 0, smallest_faces=30):
face_detector = FaceDetector.build_model(detector_backend)
print("Detector backend is ", detector_backend)
@ -68,31 +68,28 @@ def analysis(db_path, model_name = 'VGG-Face', detector_backend = 'opencv', dist
while True:
frame_count += 1
ret, img = cap.read()
if img is None:
break
raw_img = img.copy()
resolution = img.shape; resolution_x = img.shape[1]; resolution_y = img.shape[0]
faces = []
if frame_count % 10 == 0:
faces = FaceDetector.detect_faces(face_detector, detector_backend, img, align = False)
detected_faces = []
face_index = 0
for face, (x, y, w, h) in faces:
# if w > smallest_faces:
if w > smallest_faces:
cv2.rectangle(img, (x,y), (x+w,y+h), (0,255,0), 1) #draw rectangle to main image
cv2.rectangle(img, (x,y), (x+w,y+h), (0,255,0), 1) #draw rectangle to main image
detected_face = img[int(y):int(y+h), int(x):int(x+w)] #crop detected face
detected_face = img[int(y):int(y+h), int(x):int(x+w)] #crop detected face
detected_faces.append((x,y,w,h))
# else:
# cv2.imshow('img', raw_img)
# faces = np.stack(detected_face)
detected_faces.append((x,y,w,h))
base_img = raw_img.copy()
freeze_img = raw_img.copy()
for detected_face in detected_faces:
x = detected_face[0]; y = detected_face[1]
w = detected_face[2]; h = detected_face[3]
@ -125,8 +122,6 @@ def analysis(db_path, model_name = 'VGG-Face', detector_backend = 'opencv', dist
employee_name = candidate['employee']
best_distance = candidate['distance']
#print(candidate[['employee', 'distance']].values)
# if best_distance <= threshold:
if best_distance <= 0.25:
label = employee_name.split("/")[-1].replace(".jpg", "")
@ -147,7 +142,7 @@ def analysis(db_path, model_name = 'VGG-Face', detector_backend = 'opencv', dist
print(end_time - start_time)
if __name__ == '__main__':
analysis("D:/face/320_no_mask/", model_name = 'VGG-Face', distance_metric='cosine', frame_threshold=1,
analysis("D:/face/320_no_mask/", model_name = 'VGG-Face', detector_backend = 'ssd',
# source='rtsp://admin:123456@192.168.123.235:554/stream1',
source="C:/Users/DELL/Desktop/face_det/yg.mp4",
detector_backend = 'ssd', time_threshold=1, smallest_faces=20)
distance_metric='cosine', smallest_faces=20)