def face_detection():
cam = cv2.VideoCapture(0)
global preFaceCount
while cam.isOpened():
ret, img = cam.read()
img = rescale_frame(img, percent=35)
clone_img = copy.copy(img)
winlist = pcn.detect(img)
drawed_img = pcn.draw(img, winlist)
cv2.imshow('window', img)
if cv2.waitKey(1) & 0xFF == ord('s'):
face = winlist[0]
x1 = face.x
y1 = face.y
x2 = face.width + face.x - 1
y2 = face.width + face.y - 1
aligned_image = clone_img[y1:y2, x1:x2]
retval, buffer = cv2.imencode('.jpg', aligned_image)
jpg_as_text = base64.b64encode(buffer)
data = {"image": jpg_as_text, "user_id": username}
r = requests.post(url="http://68.183.226.196:1210/register",
data=data)
if (r == "OK"):
print("OK")
break
def detect():
data = request.files["image"]
img_str = data.read()
nparr = np.fromstring(img_str, np.uint8)
img = cv2.imdecode(nparr, cv2.IMREAD_ANYCOLOR)
faces = pcn.detect(img)
return json.dumps(list(map(create_response_face, faces)))
def cropface(path):
os.makedirs("crops", exist_ok=True)
os.makedirs('errors', exist_ok=True)
img = cv2.imread(path)
if img is None:
os.rename(path, f"errors/{os.path.basename(path)}")
return
height, width = img.shape[:2]
margin = 5
res_lm = landmark.detect(path)
for det in res_lm:
x1, y1, x2, y2 = det['bbox']
x1 = max(0, x1 - margin)
y1 = max(0, y1 - margin)
x2 = min(width, x2 + margin)
y2 = min(height, y2 + margin)
face = img[y1:y2, x1:x2, :]
cv2.imwrite(f"crops/{uuid1()}.jpg", face)
winlist = pcn.detect(img)
crops = pcn.crop(img, winlist)
for face in crops:
cv2.imwrite(f"crops/{uuid1()}.jpg", face)
def face_detection():
cam = cv2.VideoCapture(0)
global preFaceCount
preFaceCount = 0
while cam.isOpened():
ret, img = cam.read()
img = rescale_frame(img, percent=35)
clone_img = copy.copy(img)
winlist = pcn.detect(img)
curFaceCount = len(winlist)
if (curFaceCount > preFaceCount):
if (len(winlist) > 0):
for win in winlist:
face = winlist[0]
x1 = face.x
y1 = face.y
x2 = face.width + face.x - 1
y2 = face.width + face.y - 1
aligned_image = clone_img[y1:y2, x1:x2]
q.put(aligned_image)
drawed_img = pcn.draw(img, winlist)
cv2.imshow('window', img)
preFaceCount = curFaceCount
if cv2.waitKey(1) & 0xFF == ord('q'):
break
def step_by_step(imgpath):
img = cv2.imread(imgpath)
winlist = pcn.detect(img)
pcn.draw(img, winlist)
cv2.imshow("Image", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
def face_detector():
global fisheye_frame
global detected_frame
global winlist
while True:
if type(fisheye_frame)!=type(None):
winlist = pcn.detect(fisheye_frame)
detected_frame= pcn.draw(fisheye_frame, winlist)
def face_detect(image_path):
"""detect a face from an image,
Args:
image_path: path to the image
Returns:
If exactly one face is found, return a numpy-array-format face. Shape is (128x128x3)
for RGB image and (128x128x1) for signal channels image. Else return None or the first
found face.
"""
image = cv2.imread(image_path)
winlist = pcn.detect(image)
crops = pcn.crop(image, winlist,
size=128) # 128 is the input size of arcface
return crops[0] if crops else None
def detect_face(self):
# print("In detect face")
self.winlist = pcn.detect(self.frame)
self.face_detection_state = 1
import cv2
import pcn
print("pcn imported")
cap = cv2.VideoCapture("/dev/video0")
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 960)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter.fourcc('M', 'J', 'P', 'G'))
if __name__ == '__main__':
# network detection
print("in main")
while cap.isOpened():
ret, img = cap.read()
print("compute face")
winlist = pcn.detect(img)
img = pcn.draw(img, winlist)
cv2.imshow('PCN', img)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
def crop_face(img):
# NOTE: demo purpose: only the first face will be cropped!
winlist = pcn.detect(img)
crops = pcn.crop(img, winlist, 128) # input of net is 1x128x128
face = crops[0]
return face
def calcEmbedsRec(urlNew):
#initialize identified names
recognized_names = []
print('Received url: ', urlNew)
device = torch.device('cuda:0')
print('Running on device: {}'.format(device))
mtcnn = MTCNN(image_size=160,
margin=0,
min_face_size=20,
thresholds=[0.6, 0.7, 0.7],
factor=0.709,
prewhiten=True,
device=device)
#Function takes 2 vectors 'a' and 'b'
#Returns the cosine similarity according to the definition of the dot product
def cos_sim(a, b):
dot_product = np.dot(a, b)
norm_a = np.linalg.norm(a)
norm_b = np.linalg.norm(b)
return dot_product / (norm_a * norm_b)
#cos_sim returns real numbers,where negative numbers have different interpretations.
#So we use this function to return only positive values.
def cos(a, b):
minx = -1
maxx = 1
return (cos_sim(a, b) - minx) / (maxx - minx)
# Define Inception Resnet V1 module (GoogLe Net)
resnet = InceptionResnetV1(pretrained='vggface2').eval().to(device)
# Define a dataset and data loader
dataset = datasets.ImageFolder('student_data/Test')
dataset.idx_to_class = {i: c for c, i in dataset.class_to_idx.items()}
loader = DataLoader(dataset, collate_fn=lambda x: x[0])
#Perfom MTCNN facial detection
#Detects the face present in the image and prints the probablity of face detected in the image.
aligned = []
names = []
for x, y in loader:
x_aligned, prob = mtcnn(x, return_prob=True)
if x_aligned is not None:
print('Face detected with probability: {:8f}'.format(prob))
aligned.append(x_aligned)
names.append(dataset.idx_to_class[y])
# Calculate the 512 face embeddings
aligned = torch.stack(aligned).to(device)
embeddings = resnet(aligned).to(device)
# Print distance matrix for classes.
#The embeddings are plotted in space and cosine distace is measured.
cos_sim = nn.CosineSimilarity(dim=-1, eps=1e-6)
for i in range(0, len(names)):
emb = embeddings[i].unsqueeze(0)
# The cosine similarity between the embeddings is given by 'dist'.
dist = cos(embeddings[0], emb)
dists = [[cos(e1, e2).item() for e2 in embeddings] for e1 in embeddings]
# The print statement below is
#Helpful for analysing the results and for determining the value of threshold.
print(pd.DataFrame(dists, columns=names, index=names))
i = 1
# Haarcascade Classifier is used to detect faces through webcam.
#It is preffered over MTCNN as it is faster. Real time basic applications needs to be fast.
classifier = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
#Takes 2 vectors 'a' and 'b' .
#Returns the cosine similarity according to the definition of the dot product.
def cos_sim(a, b):
dot_product = np.dot(a, b)
norm_a = np.linalg.norm(a)
norm_b = np.linalg.norm(b)
return dot_product / (norm_a * norm_b)
#cos_sim returns real numbers,where negative numbers have different interpretations.
#So we use this function to return only positive values.
def cos(a, b):
minx = -1
maxx = 1
return (cos_sim(a, b) - minx) / (maxx - minx)
#This is the function for doing face recognition.
def verify(embedding, start_rec_time):
for i, k in enumerate(embeddings):
for j, l in enumerate(embedding):
#Computing Cosine distance.
dist = cos(k, l)
#Chosen threshold is 0.85
#Threshold is determined after seeing the table in the previous cell.
if dist > 0.8:
#Name of the person identified is printed on the screen, as well as below the detecetd face (below the rectangular box).
text = names[i]
#textOnImg = text + " - Time Elapsed: " + str(int(time.time() - start_rec_time)) + " s"
cv2.putText(img1, text, (boxes[j][0].astype(int),
boxes[j][3].astype(int) + 17),
cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 255, 0),
2)
#cv2.putText(img1, textOnImg, (20, 20), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (255,0,0), 2)
print(text)
#if text in names:
recognized_names.append(text)
#else:
textOnImg = "Time Elapsed: " + str(
int(time.time() - start_rec_time)) + " s"
cv2.putText(img1, textOnImg, (20, 20),
cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (255, 0, 0), 2)
#Define Inception Resnet V1 module (GoogLe Net)
resnet = InceptionResnetV1(pretrained='vggface2').eval().to(device)
mtcnn = MTCNN(image_size=160,
margin=0,
min_face_size=20,
thresholds=[0.6, 0.7, 0.7],
factor=0.709,
prewhiten=True,
device=device,
keep_all=True)
#Camera is opened. Webcam video streaming starts.
#vs = WebcamVideoStream(src=0).start()
print("Camera on")
cv2.namedWindow("Detected faces")
options = {
"CAP_PROP_FRAME_WIDTH": 640,
"CAP_PROP_FRAME_HEIGHT": 480,
"CAP_PROP_FPS ": 30
}
output_params = {"-fourcc": "MJPG", "-fps": 30}
writer = WriteGear(output_filename='Output.mp4',
compression_mode=False,
logging=True,
**output_params)
#stream = VideoGear(source=0, time_delay=1, logging=True, **options).start()
#url = "http://192.168.43.223:8080/shot.jpg"
url = urlNew
#run face recognition for 1 minute
start_face_rec = time.time()
end_face_rec = time.time() + 60
while (time.time() < end_face_rec):
# frm = stream.read()
# if frm is None:
# break
img_resp = requests.get(url)
img_arr = np.array(bytearray(img_resp.content), dtype=np.uint8)
img = cv2.imdecode(img_arr, -1)
#im= vs.read()
#Flip to act as a mirror
im = cv2.flip(img, 1)
#try:
#The resize function of imutils maintains the aspect ratio
#It provides the keyword arguments width and heightso the image can be resized to the intended width/height
frame = imutils.resize(im, width=400)
#Detecting faces using Haarcascade classifier.
winlist = pcn.detect(frame)
img1 = pcn.draw(frame, winlist)
face = list(map(lambda win: crop_face(img1, win, 160), winlist))
face = [f[0] for f in face]
#cv2.imshow('Live Feed', img1)
cnt = 1
for f in face:
#fc, u = crop_face(img, f)
print('Printing Face no: ', cnt)
cv2.imshow('Detected faces', f)
cnt += 1
#faces = classifier.detectMultiScale(face)
path = "./student_data/Pics/".format(i)
img_name = "image_{}.jpg".format(i)
#The captured image is saved.
cv2.imwrite(os.path.join(path, img_name), f)
imgName = "./student_data/Pics/image_{}.jpg".format(i)
# Get cropped and prewhitened image tensor
img = Image.open(imgName)
i = i + 1
img_cropped = mtcnn(img)
boxes, prob = mtcnn.detect(img)
img_draw = img.copy()
draw = ImageDraw.Draw(img_draw)
#print(boxes)
#Rectangular boxes are drawn on faces present in the image.
#The detected and cropped faces are then saved.
if (boxes is not None):
for i, box in enumerate(boxes):
#draw.rectangle(box.tolist())
extract_face(
img,
box,
save_path='./student_data/Pics/Cropped_Face_{}.jpg'.
format(i))
img_draw.save('./student_data/Pics/Faces_Detected.jpg')
ima = cv2.imread('./student_data/Pics/Faces_Detected.jpg')
#Calculate embeddings of each cropped face.
if (img_cropped is not None):
img_embedding = resnet(img_cropped.cuda()).to(device)
#Call function verify.
#Identify the person with the help of embeddings.
cos_sim = nn.CosineSimilarity(dim=-1, eps=1e-6)
verify(img_embedding, start_face_rec)
#else:
#textForImg = "Time Elapsed: " + str(int(time.time() - start_face_rec)) + " s"
#cv2.putText(frame, textForImg, cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (255,255,255), 2)
#'Detecting..' window opens.
#Rectangular boxes are drawn on detected faces.
#The identified faces have their respective name below the box.
cv2.imshow('Detecting...', img1)
writer.write(img1)
if (not face):
#cv2.imshow(f"Time Elapsed: ${str(int(time.time() - start_face_rec))} s" ,frame)
textForImg = "Time Elapsed: " + str(
int(time.time() - start_face_rec)) + " s"
cv2.putText(img1, textForImg, (40, 40),
cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (255, 0, 0), 2)
#print("no face")
cv2.imshow('Detecting...', img1)
# except:
# #In case 'try' doesn't work, "Get the image embedding" text is printed on the screen.
# #Run first cell
# text="Get the image embeddings"
# print(text)
# break
key = cv2.waitKey(1)
#13 is for 'Enter' key.
#If 'Enter' key is pressed, all the windows are made to close forcefully.
if key == 13:
break
print("calculating a list of all recognized faces...")
rec_names_dict = {i: recognized_names.count(i) for i in recognized_names}
filtered_names = []
for key in rec_names_dict:
if rec_names_dict[key] > 30:
filtered_names.append(key)
print("Total Recognized names: ", rec_names_dict)
print("Filtered names: ", filtered_names)
cv2.destroyAllWindows()
writer.close()
#vs.stop()
#return {i:rec_names_dict[i] for i in filtered_names}
return filtered_names
def main(self):
while self.frame != None:
self.winlist = pcn.detect(self.frame)
print("Found face(s)")
