def blur_face(frameCount):
# grab global references to the video stream, output frame, and
# lock variables
global vs, outputFrame, lock
# loop over the frames from the video stream
while True:
# grab the frame from the threaded video stream and resize it
# to have a maximum width of 400 pixels
frame = vs.read()
frame = imutils.resize(frame, width=400)
# grab the dimensions of the frame and then construct a blob
# from it
(h, w) = frame.shape[:2]
blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300),
(104.0, 177.0, 123.0))
# pass the blob through the network and obtain the face detections
net.setInput(blob)
detections = net.forward()
# loop over the detections
for i in range(0, detections.shape[2]):
# extract the confidence (i.e., probability) associated with
# the detection
confidence = detections[0, 0, i, 2]
# filter out weak detections by ensuring the confidence is
# greater than the minimum confidence
if confidence > args["confidence"]:
# compute the (x, y)-coordinates of the bounding box for
# the object
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
# extract the face ROI
face = frame[startY:endY, startX:endX]
# check to see if we are applying the "simple" face
# blurring method
if args["method"] == "simple":
face = anonymize_face_simple(face, factor=3.0)
# otherwise, we must be applying the "pixelated" face
# anonymization method
else:
face = anonymize_face_pixelate(face, blocks=args["blocks"])
# store the blurred face in the output image
frame[startY:endY, startX:endX] = face
# show the output frame
# cv2.imshow("Frame", frame)
# key = cv2.waitKey(1) & 0xFF
with lock:
outputFrame = frame.copy()
def predict(self, payload):
# load the input image from disk, clone it, and grab the image spatial
# dimensions
img = imread(io.BytesIO(base64.b64decode(
payload["base64"]))) # numpy array (width, hight, 3)
image = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
orig = image.copy()
(h, w) = image.shape[:2]
# construct a blob from the image
blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300),
(104.0, 177.0, 123.0))
# pass the blob through the network and obtain the face detections
print("[INFO] computing face detections...")
self.net.setInput(blob)
detections = self.net.forward()
# loop over the detections
for i in range(0, detections.shape[2]):
# extract the confidence (i.e., probability) associated with the
# detection
confidence = detections[0, 0, i, 2]
# filter out weak detections by ensuring the confidence is greater
# than the minimum confidence
if confidence > args["confidence"]:
# compute the (x, y)-coordinates of the bounding box for the
# object
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
# extract the face ROI
face = image[startY:endY, startX:endX]
# check to see if we are applying the "simple" face blurring
# method
if args["method"] == "simple":
face = anonymize_face_simple(face, factor=3.0)
# otherwise, we must be applying the "pixelated" face
# anonymization method
else:
face = anonymize_face_pixelate(face, blocks=args["blocks"])
# store the blurred face in the output image
image[startY:endY, startX:endX] = face
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
img = Image.fromarray(image)
im_file = io.BytesIO()
img.save(im_file, format="PNG")
im_bytes = base64.b64encode(im_file.getvalue()).decode("utf-8")
return im_bytes
def blur(img_name, load_folder, save_folder):
load_path = os.path.join(load_folder, img_name)
print(load_path)
save_path = os.path.join(save_folder, img_name)
print("[INFO] loading face detector model...")
prototxtPath = "face_detector/deploy.prototxt"
weightsPath = "face_detector/res10_300x300_ssd_iter_140000.caffemodel"
net = cv2.dnn.readNet(prototxtPath, weightsPath)
# load the input image from disk, clone it, and grab the image spatial
# dimensions
image = cv2.imread(load_path)
orig = image.copy()
(h, w) = image.shape[:2]
# construct a blob from the image
blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300), (104.0, 177.0, 123.0))
# pass the blob through the network and obtain the face detections
print("[INFO] computing face detections...")
net.setInput(blob)
detections = net.forward()
# loop over the detections
for i in range(0, detections.shape[2]):
# extract the confidence (i.e., probability) associated with the
# detection
confidence = detections[0, 0, i, 2]
# filter out weak detections by ensuring the confidence is greater
# than the minimum confidence
if confidence > 0.5:
# compute the (x, y)-coordinates of the bounding box for the
# object
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
# extract the face ROI
face = image[startY:endY, startX:endX]
face = anonymize_face_pixelate(face, blocks=10)
# store the blurred face in the output image
image[startY:endY, startX:endX] = face
return save_path
def getFirstWidthHeight(cap, net):
ret, frame = cap.read()
if ret == False:
#if done
return -1, -1, -1, 1 #return nonreal width height image and code 1
image = frame
(h, w) = image.shape[:2]
# construct a blob from the image
blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300), (104.0, 177.0, 123.0))
# pass the blob through the network and obtain the face detections
net.setInput(blob)
detections = net.forward()
# loop over the detections
for i in range(0, detections.shape[2]):
# extract the confidence (i.e., probability) associated with the
# detection
confidence = detections[0, 0, i, 2]
# filter out weak detections by ensuring the confidence is greater
# than the minimum confidence
if confidence > 0.5:
# compute the (x, y)-coordinates of the bounding box for the
# object
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
# extract the face ROI
face = image[startY:endY, startX:endX]
face = anonymize_face_pixelate(face, blocks=20)
# store the blurred face in the output image
image[startY:endY, startX:endX] = face
# display the original image and the output image with the blurred
# face(s) side by side
# output = np.hstack([orig, image])
# cv2.imshow("Output", output)
# cv2.imshow("Output",image)
# cv2.waitKey(0)
return w, h, image, 0
def censura(imgpath):
# VARIABLES
face = "face_detector" # Métode de detecció facial
method = "pixelated" # Tipus de censura: "simple" o "pixelated"
blocks = 20 # Número de blocs en el mode pixelated
preconfidence = "0.5" # Confidencialitat (Entre 0 i 1)
# SCRIPT DE RECONEIXEMENT FACIAL
prototxtPath = os.path.sep.join([face, "deploy.prototxt"])
weightsPath = os.path.sep.join([face,"res10_300x300_ssd_iter_140000.caffemodel"])
net = cv2.dnn.readNet(prototxtPath, weightsPath)
image = cv2.imread(imgpath)
orig = image.copy()
(h, w) = image.shape[:2]
blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300),(104.0, 177.0, 123.0))
net.setInput(blob)
detections = net.forward()
for i in range(0, detections.shape[2]):
confidence = detections[0, 0, i, 2]
if confidence > float(preconfidence):
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
# EXTREU LA CARA (ROI)
face = image[startY:endY, startX:endX]
# APICA LA CENSURA FACIAL
if method == "simple":
face = anonymize_face_simple(face, factor=3.0)
else:
face = anonymize_face_pixelate(face,
blocks=blocks)
# ENGANXA LA CARA CENSURADA A LA IMATGE
image[startY:endY, startX:endX] = face
# GUARDAR
cv2.imwrite(imgpath, image)
# LOG
print("Nova imatge censurada!")
confidence = detections[0, 0, i, 2]
if confidence > args["confidence"]:
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
face = frame[startY:endY, startX:endX]
if args["method"] == "simple":
face = anonymize_face_simple(face, factor=3.0)
else:
face = anonymize_face_pixelate(face,
blocks=args["blocks"])
frame[startY:endY, startX:endX] = face
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
break
cv2.destroyAllWindows()
vs.stop()