import cv2
import numpy as np
from recognition.FaceRecognition import FaceRecognition
from detection.FaceDetector import FaceDetector
face_detector = FaceDetector()
face_recognition = FaceRecognition()
image_files = ['./media/1.jpg', './media/2.jpg']
for input_str in image_files:
img = cv2.imread(input_str)
boxes, scores = face_detector.detect(img)
face_boxes = boxes[np.argwhere(scores > 0.5).squeeze()]
print('Number of face in image:', len(face_boxes))
for box in face_boxes:
cropped_face = img[box[0]:box[2], box[1]:box[3], :]
cropped_face = cv2.resize(cropped_face, (160, 160),
interpolation=cv2.INTER_AREA)
print('Face descriptor:')
print(face_recognition.recognize(cropped_face), '\n')
cv2.imshow('image', cropped_face)
cv2.waitKey(0)
cv2.destroyAllWindows()
face_detector = FaceDetector()
face_recognition = FaceRecognition()
face_classfier = FaceClassifier('./classifier/trained_classifier.pkl')
video_capture = cv2.VideoCapture(0)
print('Start Recognition!')
prevTime = 0
while True:
ret, frame = video_capture.read()
frame = cv2.resize(frame, (0, 0), fx=1, fy=1) # resize frame (optional)
#time.sleep(1)
curTime = time.time() # calc fps
find_results = []
frame = frame[:, :, 0:3]
boxes, scores = face_detector.detect(frame)
face_boxes = boxes[np.argwhere(scores>0.3).reshape(-1)]
face_scores = scores[np.argwhere(scores>0.3).reshape(-1)]
print('Detected_FaceNum: %d' % len(face_boxes))
if len(face_boxes) > 0:
for i in range(len(face_boxes)):
box = face_boxes[i]
cropped_face = frame[box[0]:box[2], box[1]:box[3], :]
cropped_face = cv2.resize(cropped_face, (160, 160), interpolation=cv2.INTER_AREA)
feature = face_recognition.recognize(cropped_face)
name = face_classfier.classify(feature)
cv2.rectangle(frame, (box[1], box[0]), (box[3], box[2]), (0, 255, 0), 2)
# plot result idx under box
def main(args):
face_detector = FaceDetector()
face_recognition = FaceRecognition(args.model)
face_classfier = FaceClassifier(args.classifier_filename)
video_capture = cv2.VideoCapture(args.video_input)
output_file = './media/result/' + os.path.basename(
args.video_input) + '_result.avi'
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter(
output_file, fourcc, 24.0,
(int(video_capture.get(3)), int(video_capture.get(4))))
print('Start Recognition!')
prevTime = 0
while video_capture.isOpened():
ret, frame = video_capture.read()
curTime = time.time() # calc fps
find_results = []
frame = frame[:, :, 0:3]
boxes, scores = face_detector.detect(frame)
face_boxes = boxes[np.argwhere(scores > 0.3).reshape(-1)]
face_scores = scores[np.argwhere(scores > 0.3).reshape(-1)]
print('Detected_FaceNum: %d' % len(face_boxes))
if len(face_boxes) > 0:
for i in range(len(face_boxes)):
box = face_boxes[i]
cropped_face = frame[box[0]:box[2], box[1]:box[3], :]
cropped_face = cv2.resize(cropped_face, (160, 160),
interpolation=cv2.INTER_AREA)
feature = face_recognition.recognize(cropped_face)
name = face_classfier.classify(feature)
cv2.rectangle(frame, (box[1], box[0]), (box[3], box[2]),
(0, 255, 0), 2)
# plot result idx under box
text_x = box[1]
text_y = box[2] + 20
cv2.putText(frame,
name, (text_x, text_y),
cv2.FONT_HERSHEY_COMPLEX_SMALL,
1, (0, 0, 255),
thickness=1,
lineType=2)
else:
print('Unable to align')
sec = curTime - prevTime
prevTime = curTime
fps = 1 / (sec)
str = 'FPS: %2.3f' % fps
text_fps_x = len(frame[0]) - 150
text_fps_y = 20
cv2.putText(frame,
str, (text_fps_x, text_fps_y),
cv2.FONT_HERSHEY_COMPLEX_SMALL,
1, (0, 0, 0),
thickness=1,
lineType=2)
out.write(frame)
video_capture.release()
out.release()
cv2.destroyAllWindows()