def process_img(path, new_path):
dirs = os.listdir(path)
for pic_dir in dirs:
print(pic_dir)
dir_path = os.path.join(path, pic_dir)
pics = os.listdir(dir_path)
for pic in pics:
pic_path = os.path.join(dir_path, pic)
image = face_recognition_models.load_image_file(pic_path)
face_locations = face_recognition_models.face_locations(image)
if len(face_locations) == 0:
continue
img = Image.open(pic_path)
new_pic_path = os.path.join(new_path, pic_dir)
if not os.path.exists(new_pic_path):
os.makedirs(new_pic_path)
if len(img.split()) == 4:
# 利用split和merge将通道从四个转换为三个
r, g, b, a = img.split()
toimg = Image.merge("RGB", (r, g, b))
toimg.save(new_pic_path + '\\' + pic)
else:
try:
img.save(new_pic_path + '\\' + pic)
except:
continue
print('Finish......!')
def main():
encoding = "D:\Projects\computer vision\FaceRecognition\data\encoding1.pickle"
data = pickle.loads(open(encoding, "rb").read())
print(data)
cap = cv2.VideoCapture(0)
if cap.isOpened :
ret, frame = cap.read()
else:
ret = False
while(ret):
ret, frame = cap.read()
rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
rgb = imutils.resize(frame, width=400)
r = frame.shape[1] / float(rgb.shape[1])
boxes = face_recognition_models.face_locations(rgb, model= "hog")
encodings = face_recognition_models.face_encodings(rgb, boxes)
names = []
for encoding in encodings:
matches = face_recognition_models.compare_faces(np.array(encoding),np.array(data["encodings"]))
name = "Unknown"
if True in matches:
matchedIdxs = [i for (i, b) in enumerate(matches) if b]
counts = {}
for i in matchedIdxs:
name = data["names"][i]
counts[name] = counts.get(name, 0) + 1
name = max(counts, key=counts.get)
names.append(name)
for ((top, right, bottom, left), name) in zip(boxes, names):
top = int(top * r)
right = int(right * r)
bottom = int(bottom * r)
left = int(left * r)
cv2.rectangle(frame, (left, top), (right, bottom),(0, 255, 0), 2)
y = top - 15 if top - 15 > 15 else top + 15
cv2.putText(frame, name, (left, y), cv2.FONT_HERSHEY_SIMPLEX,0.75, (0, 255, 0), 2)
cv2.imshow("Frame", frame)
if cv2.waitKey(1) == 27:
break
cv2.destroyAllWindows()
cap.release()
cam.release()
cv2.destroyAllWindows()'''
while True:
# Grab a single frame of video
ret, frame = cam.read()
# Resize frame of video to 1/4 size for faster face recognition processing
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_small_frame = small_frame[:, :, ::-1]
# Only process every other frame of video to save time
if process_this_frame:
# Find all the faces and face encodings in the current frame of video
face_locations = face_recognition_models.face_locations(
rgb_small_frame)
face_encodings = face_recognition_models.face_encodings(
rgb_small_frame, face_locations)
face_names = []
for face_encoding in face_encodings:
# See if the face is a match for the known face(s)
matches = face_recognition_models.compare_faces(
known_face_encodings, face_encoding)
name = "Unknown"
# If a match was found in known_face_encodings, just use the first one.
if True in matches:
first_match_index = matches.index(True)
name = known_face_names[first_match_index]
knownNames = []
# loop over the image paths
for (i, imagePath) in enumerate(imagePaths):
# extract the person name from the image path
print("[INFO] processing image {}/{}".format(i + 1, len(imagePaths)))
name = imagePath.split(os.path.sep)[-2]
# load the input image and convert it from RGB (OpenCV ordering)
# to dlib ordering (RGB)
image = cv2.imread(imagePath)
rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# detect the (x, y)-coordinates of the bounding boxes
# corresponding to each face in the input image
boxes = face_recognition.face_locations(rgb,
model=args["detection_method"])
# compute the facial embedding for the face
encodings = face_recognition.face_encodings(rgb, boxes)
# loop over the encodings
for encoding in encodings:
# add each encoding + name to our set of known names and
# encodings
knownEncodings.append(encoding)
knownNames.append(name)
# dump the facial encodings + names to disk
print("[INFO] serializing encodings...")
data = {"encodings": knownEncodings, "names": knownNames}
f = open(args["encodings"], "wb")
f.write(pickle.dumps(data))
knows_faces = []
knows_names = []
for name in os.listdir(KNOWN_FACES_DIR):
for filename in os.listdir(f"{KNOWN_FACES_DIR}/{name}"):
image = face_recognition.load_image_file(
f"{KNOWN_FACES_DIR}/{name}/{filename}")
encoding = face_recognition.face_encodings(image)[0]
knows_faces.append(encoding)
knows_names.append(name)
print("Prosseing Unknow Face -----")
for filename in os.listdir(UNKNOWN_FACES_DIR):
print(filename)
image = face_recognition.load_image_file(f"{UNKNOWN_FACES_DIR}/{filename}")
locations = face_recognition.face_locations(image, model=MODEL)
encodings = face_recognition.face_encodings(image, locations)
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
for face_encoding, face_location in zip(encodings, locations):
results = face_recognition.compare_face(knows_faces, face_encoding,
locations, TOLEREANCE)
match = None
if True in results:
match = knows_names[results.index(True)]
print(f"Match Face :{match}")
top_left = (face_location[3], face_location[0])
bottom_right = (face_location[1], face_location[2])
color = [0, 255, 0]
cv2.rectangle(image, top_left, bottom_right, color, FACE_THICKNESS)