def main():
# Loading the cascade xml file
parser = argparse.ArgumentParser()
parser.add_argument("-f",
"--face",
required=True,
help="path to where the face cascade resides")
parser.add_argument("-v",
"--video",
action='store_true',
help="detection from WebCam")
parser.add_argument("-i",
"--image",
help="path to where the image resides")
args = vars(parser.parse_args())
img = cv2.imread(args["image"], 1)
if (args["video"]):
# cap points to the WebCam
cap = cv2.VideoCapture(0)
while True:
# Getting images from WebCam
ret, img = cap.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# using FaceDetector class to detecte the face
faceDetector = FaceDetector(args['face'])
faces = faceDetector.detect(gray)
# draw a box around faces
for (x, y, w, h) in faces:
img = cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0),
2)
# draw image
cv2.imshow('img', img)
if cv2.waitKey(1) == ord('q'):
cv2.destroyAllWindows()
break
cap.release()
elif img != None:
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# using FaceDetector class to detecte the face
faceDetector = FaceDetector(args['face'])
faces = faceDetector.detect(gray)
# draw a box around faces
for (x, y, w, h) in faces:
img = cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
# draw image
cv2.imshow('img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
else:
print("Please enter the correct arguments")
def main():
# Instantiate Classes
detector = FaceDetector(FACE_CLASSIFIER_PATH, EYE_CLASSIFIER_PATH)
model = FaceModel()
display = Display()
capture = Capture()
oldTime = time.time()
i = 0
frames_num = 0
delta_sum = 0
while True:
# escape key for exit, in linux display is not working without that
k = cv2.waitKey(30) & 0xff
if k == 27:
return
# Calculate time difference (dt), update oldTime variable
newTime = time.time()
dt = newTime - oldTime
oldTime = newTime
frames_num += 1
delta_sum += dt
if frames_num % 100 == 0:
print "delta:", delta_sum / float(
frames_num), "frames pr sec:", 1 / float(
delta_sum / float(frames_num))
frames_num = 0
delta_sum = 0
# Grab Frames
frames = capture.read()
# Detect face 20% of the time, eyes 100% of the time
if i % 5 is 0:
rects = detector.detect(frames)
else:
rects = detector.detect(frames, model.getPreviousFaceRects())
i += 1
# Add detected rectangles to model
model.add(rects)
display.renderScene(frames['display'], model, rects)
display.renderEyes(frames['color'], model)
def main(): # Instantiate Classes detector = FaceDetector(FACE_CLASSIFIER_PATH, EYE_CLASSIFIER_PATH); model = FaceModel(); display = Display(); capture = Capture(); oldTime = time.time(); i = 0; frames_num=0 delta_sum = 0 while True: # escape key for exit, in linux display is not working without that k = cv2.waitKey(30) & 0xff if k == 27: return # Calculate time difference (dt), update oldTime variable newTime = time.time(); dt = newTime - oldTime; oldTime = newTime; frames_num += 1 delta_sum += dt if frames_num % 100 == 0: print "delta:",delta_sum/float(frames_num),"frames pr sec:",1/float(delta_sum/float(frames_num)) frames_num=0 delta_sum = 0 # Grab Frames frames = capture.read(); # Detect face 20% of the time, eyes 100% of the time if i % 5 is 0: rects = detector.detect(frames); else: rects = detector.detect(frames,model.getPreviousFaceRects()); i += 1; # Add detected rectangles to model model.add(rects); display.renderScene(frames['display'],model,rects); display.renderEyes(frames['color'],model);
def detect_faces(filename):
try:
image = cv2.imread(os.path.join(app.config['UPLOAD_FOLDER'], filename))
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# find faces in the image
fd = FaceDetector()
faceRects = fd.detect(gray, scaleFactor = 1.1, minNeighbors = 5,
minSize = (30, 30))
faces_number = len(faceRects)
# loop over the faces and draw a rectangle around each
for (x, y, w, h) in faceRects:
image = cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.imwrite((os.path.join(app.config['UPLOAD_FOLDER'], filename)), image)
return faces_number
except Exception:
faces_number = 0
return faces_number
def VJFindFace(frame):
#Include the global variables inside the scope of the function
global RATIO, orig
#list to store the corner coordinates of the faces found.Initially empty
allRoiPts = []
#generate a copy of the original frame
orig = frame.copy()
#resize the original image. Set the aspect RATIO
dim = (frame.shape[1]/RATIO, frame.shape[0]/RATIO);
# perform the actual resizing of the image and show it
resized = cv2.resize(frame, dim, interpolation = cv2.INTER_AREA)
#convert the frame to gray scale
gray = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)
# find faces in the gray scale frame of the video using Haar feature based trained classifier
fd = FaceDetector('{0}/haarcascade_frontalface_default.xml'.format(OPENCV_HOME))
faceRects = fd.detect(gray, scaleFactor = 1.1, minNeighbors = 4, minSize = (10, 10))
print "\n### Number of Faces: {0}\n".format(np.shape(faceRects)[0])
# loop over the faces and draw a rectangle around each
for (x, y, w, h) in faceRects:
#This step is extremely crucial. Here we are trying to decrease the size of the bounding box
#for the face detected area. The primary reason for this being the box identified by VJ contains
#a part of background. Hence when we find the mean of this box at the time of tracking, the resulting
#bounding box was much larger than the actual face region. Thus to eliminate the effect of background
#changing the mean we decrease the window size, as the color of interest will be available in this area
#for tracking in the upcoming frames.
x = RATIO*(x+10)
y = RATIO*(y+10)
w = RATIO*(w-15)
h = RATIO*(h-15)
#Uncomment line 70, 76 and 77 to view the boxes around faces found using viola jones. Note that these
#boxes will appear to be shifted and smaller size due to the opeRATIOn performed above
#cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 255), 2)
#Assign top left and bottom right pixel values each time Viola-Johnes is run
#Append all the points detected for the face in the list
allRoiPts.append((x, y, x+w, y+h))
#show the detected faces
cv2.imshow("Faces", frame)
cv2.waitKey(1)
return allRoiPts
if not camera.isOpened():
raise ValueError("camera not found!")
else:
camera = cv2.VideoCapture(args["video"])
while True:
# return false if no frame has been grabbed.
(grabbed, frame) = camera.read()
# if video is available then break if not grabbed frame.
if args.get("video") and not grabbed:
break
frame = imutils.resize(frame, width = 300)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faceRects = fd.detect(gray, scaleFactor=1.1)
frameClone = frame.copy()
for (fX, fY, fW, fH) in faceRects:
cv2.rectangle(frameClone, (fX, fY), (fX + fW, fY + fH),
(0, 255, 0), 2)
cv2.imshow("Face", frameClone)
# 按q键退出,ord()返回字符的ascii码
if cv2.waitKey(30) == ord("q"):
break
camera.release()
cv2.destroyAllWindows()
elif right < left:
angle = 15 - abs(right - left)
direction = 2
return direction, angle
cap = cv2.VideoCapture(1)
facedetector = FaceDetector()
while 1:
ret, img = cap.read()
if not ret:
print("usb-camera error, get into normal mode without faceID.")
break
faces = facedetector.detect(img)
valid = facedetector.verifyfaces(faces)
if valid:
print("FaceID matched sucessfully,unlock the car.")
break
else:
print("No matched face.")
cap.release()
del facedetector
car = Car()
detector = Detector()
cap = cv2.VideoCapture(0)
while 1:
ret, img = cap.read()
from mergerect import mergeRects
if __name__ == '__main__':
video_capture = cv2.VideoCapture(0)
faceDetector = FaceDetector()
while True:
ret, frame = video_capture.read()
frame = cv2.resize(frame, (0, 0), fx=0.4, fy=0.4)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces, totalTiles = faceDetector.detect(gray,
min_size=0.0,
max_size=0.3,
step=0.9,
detectPad=(2, 2),
verbose=True,
getTotalTiles=True)
faces = mergeRects(faces, overlap_rate=0.82, min_overlap_cnt=4)
print("----faces detected----")
for x, y, w, h in faces:
print(x, y, w, h)
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
frame = cv2.resize(frame, (0, 0), fx=1.0 / 0.4, fy=1.0 / 0.4)
cv2.imshow('Video', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
(grabbed, frame) = camera.read()
# read() returns a boolean value of success and the frame
# if nothing is returned don't keep going
if arguments.get("video") and not grabbed:
break
# resize the frame to 300 pixels in width, for performance
frame = imutils.resize(frame, width=300)
# convert the frame to gray scale for processing
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# run the frame through the facial detector in the same fashion as
# still pictures from picture_detection.py
face_rectangles = detector.detect(gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=config.WINDOW_SIZE)
# Make a copy of the the frame... just in case
frame_clone = frame.copy()
# Loop over the bounding boxes and draw rectangles
for (fX, fY, fW, fH) in face_rectangles:
cv2.rectangle(frame_clone, (fX, fY), (fX + fW, fY + fH),
config.GREEN_BOX, config.LINE_THICKNESS)
# Show off our work to the world
cv2.imshow("Face", frame_clone)
# Provide a way to stop the display
if cv2.waitKey(1) & 0xFF == ord("q"):
class VideoCamera(object):
def __init__(self):
self.video = cv2.VideoCapture(0)
self.model = load_model('../face_mask.model')
self.face_detector = FaceDetector(
'./haarcascade_frontalface_default.xml')
def __del__(self):
self.video.release()
def _resize(self, image, width=None, height=None, inter=cv2.INTER_AREA):
dim = None
(h, w) = image.shape[:2]
if width is None and height is None:
return image
if width is None:
# calculate the ratio of the height and construct the
# dimensions
r = height / float(h)
dim = (int(w * r), height)
else:
# calculate the ratio of the width and construct the
# dimensions
r = width / float(w)
dim = (width, int(h * r))
resized = cv2.resize(image, dim, interpolation=inter)
return resized
def get_frame(self):
(ret, frame) = self.video.read()
frame = self._resize(frame, width=450)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
frame = np.dstack([frame, frame, frame])
face_rects = self.face_detector.detect(frame,
scaleFactor=1.2,
minNeighbors=3,
minSize=(40, 40))
for (x, y, w, h) in face_rects:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
face_image = frame[y:y + h, x:x + w]
face_image = cv2.cvtColor(face_image, cv2.COLOR_BGR2RGB)
face_image = cv2.resize(face_image, (224, 224))
face_image = face_image / 255.0
face_image = img_to_array(face_image)
face_image = np.expand_dims(face_image, axis=0)
(mask, without_mask) = self.model.predict(face_image)[0]
if mask > without_mask:
print("Mask! :", mask)
color = (0, 255, 0)
cv2.putText(frame, "Mask : {}%".format(mask), (x, y - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
else:
color = (0, 0, 255)
cv2.putText(frame, "No mask : {}%".format(without_mask),
(x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.45, color,
2)
cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
print("without mask ! : ", without_mask)
ret, jpeg = cv2.imencode('.jpg', frame)
return jpeg.tobytes()
for im in args["images"]:
if any(x == os.path.splitext(im)[1][1:] for x in ('jpg','jpeg','gif','png','bmp')):
# Read image
image = cv2.imread(im)
# Grayscale image
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
# Create facedetector object
if args["faces"] is None:
fd = FaceDetector("cascades/haarcascade_frontalface_alt.xml")
else:
fd = FaceDetector(args["faces"])
# Optimize image size
image = imutils.optimize(image,lowerLim,upperLim)
# If size is 300 use custom parameter values, else use default (have to make it dynamic)
if image.shape[0]==lowerLim or image.shape[1]==lowerLim:
faceRects = fd.detect(image,scaleFactor=1.04,minNeighbors=3,minSize=(2,2))
else:
faceRects = fd.detect(image)
for (i,rect) in enumerate(faceRects):
(x,y,w,h) = rect
#cv2.rectangle(image,(x,y),(x+w,y+h),(0,0,255),1)
face = imutils.resize(image[y:y+h,x:x+w],width=100)
faces.append(face)
t=time.time()
folder = "faces/"+os.path.dirname(im)[7:]
try:
os.mkdir(folder)
except OSError:
pass
cv2.imwrite(folder+"/"+str(t)+".jpg",face)
time.sleep(0.025)
ap = argparse.ArgumentParser()
ap.add_argument("-i",
"--image",
required=True,
help="path to where the image file reside")
args = vars(ap.parse_args())
image = cv2.imread(args["image"])
(h, w) = image.shape[:2]
if h > 1080:
image = cv2.resize(image, (600, 800))
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
fd = FaceDetector("./haarcascade_frontalface_default.xml")
faceRects = fd.detect(gray, scaleFactor=1.2, minNeighbors=3, minSize=(40, 40))
print(f'I found {len(faceRects)} face(s)')
model = load_model('./face_mask.model')
face_images = []
for (x, y, w, h) in faceRects:
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
face_image = image[y:y + h, x:x + w]
face_image = cv2.cvtColor(face_image, cv2.COLOR_BGR2RGB)
face_image = cv2.resize(face_image, (224, 224))
face_image = face_image / 255.0
"--face",
required=True,
help="path to where the face cascade resides")
parser.add_argument("-i",
"--image",
required=True,
help="path to where the image file resides")
args = vars(parser.parse_args())
# get the image to use and convert it to Grayscale for processing
image = cv2.imread(args["image"])
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# setup our Classifier processing
detector = FaceDetector(args["face"])
face_rectangles = detector.detect(gray_image,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30))
# Print out the number of faces in the image that were found
print("I found {} face(s)".format(len(face_rectangles)))
# Loop through the faces and compute the rectangles to be drawn
for (x, y, w, h) in face_rectangles:
cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 2)
# Show the image and wait to close the window until a key is pressed
cv2.imshow("Faces", image)
cv2.waitKey(0)
from facedetector import FaceDetector
import argparse
import cv2
ap = argparse.ArgumentParser()
ap.add_argument("-f",
"--face",
required=True,
help="path to where the face cascade resides")
ap.add_argument("-i", "--image", required=True, help="Path to the image")
args = vars(ap.parse_args())
image = cv2.imread(args["image"])
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# Load the face cascade into the face detector
face_detector = FaceDetector(args["face"])
# Detect all the faces on the image
face_rectangles = face_detector.detect(gray)
print "I found %d face(s)" % len(face_rectangles)
# Draw rectangles around the faces found on the image
for (x, y, w, h) in face_rectangles:
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.imshow("Faces", image)
cv2.waitKey(0)
if not args.get('video', False):
camera = cv2.VideoCapture(0)
else:
camera = cv2.VideoCapture(args['video'])
while True:
(grabbed, frame) = camera.read()
if args.get('video') and not grabbed:
break
frame = imutils.resize(frame, width = 1300)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faceRects = fd.detect(gray, scaleFactor=1.2, minNeighbours=5,minSize=(30,30))
frameClone = frame.copy()
for (fX, fY, fW,fH) in faceRects:
cv2.rectangle(frameClone, (fX, fY), (fX+fW, fY+fH),
(0, 255, 0), 2)
cv2.imshow('Face', frameClone)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
camera.release()
cv2.destroyAllWindows()
while True:
(grabbed, frame) = camera.read()
if args.get("video") and not grabbed:
break
img_height, img_width, depth = frame.shape
scale = w / img_width
h = img_height * scale
frame = cv2.resize(frame, (0, 0), fx=scale, fy=scale)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Face detection function is here
faceRects = fd.detect(gray)
frameClone = frame.copy()
if faceRects is not None:
for (fX, fY, fW, fH) in faceRects:
cv2.rectangle(frameClone, (fX, fY), (fX + fW, fY + fH),
(0, 255, 0), 2)
for client in clients:
data = {'X': int(fX), 'Y': int(fY)}
json_data = json.dumps(data, cls=IntegerEncoder)
client.sendMessage(json_data)
cv2.imshow("Face", cv2.flip(frameClone, 1))
if cv2.waitKey(1) & 0xFF == ord('q'):
face_detector = FaceDetector(arguments['face'])
if not arguments.get('video', False):
camera = cv2.VideoCapture(0)
else:
camera = cv2.VideoCapture(arguments['video'])
while True:
(grabbed, frame) = camera.read()
if arguments.get('video') and not grabbed:
break
frame = resize(frame, width=300)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
face_rectangles = face_detector.detect(gray, scale_factor=1.1, min_neighbors=5, min_size=(30, 30))
frame_clone = frame.copy()
green = (0, 255, 0)
for (f_x, f_y, f_w, f_h) in face_rectangles:
cv2.rectangle(frame_clone, (f_x, f_y), (f_x + f_w, f_y + f_h), green, 2)
cv2.imshow('Face', frame_clone)
if cv2.waitKey(1) & 0xFF == ord('q'): # Did the user press the q key?
break
camera.release()
cv2.destroyAllWindows()
from facedetector import FaceDetector
import argparse
import cv2
ap = argparse.ArgumentParser()
ap.add_argument("-f", "--face", required=True, help="path to the face cascade")
ap.add_argument("-i", "--image", required=True, help="path to the image")
args = vars(ap.parse_args())
image = cv2.imread(args["image"])
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
fd = FaceDetector(args["face"])
faceRects = fd.detect(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
print("I found {} faces(s)".format(len(faceRects)))
for (x, y, w, h) in faceRects:
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.imshow("Faces", image)
cv2.waitKey(0)
import cv2
from facedetector import FaceDetector
image = cv2.imread("goat1.jpg", 1)
cv2.imshow("hi", image)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
p = FaceDetector(faceCascadePath="haarcascade_frontalface_default.xml")
face = p.detect(image=gray, scaleFactor=1.2, minNeighbors=5, minSize=(30, 30))
print("I found {} face(s)".format(len(face)))
for (x, y, w, h) in face:
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.imshow("Faces", image)
k = cv2.waitKey(0)
if k == 27:
cv2.destroyAllWindows()