def crop_face(img): bounding_boxes, landmarks = detect_faces(img) image = show_bboxes(img, bounding_boxes) (x, y, h, w)=bounding_boxes[0][:4] img = img.crop((x, y, h, w)) img=img.resize((224,224)) return img
def get_image_from_camera():
capture = cv2.VideoCapture(0)
while 1:
ret, frame = capture.read()
window_name = "face"
#cv2.imshow(window_name, frame)
'''
bounding_boxes, landmarks = detect_faces(frame)
image = show_bboxes(image, bounding_boxes, landmarks)
'''
if (ret):
cv_img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image = Image.fromarray(cv_img)
image = image.resize((320, 240), Image.ANTIALIAS)
bounding_boxes, landmarks = detect_faces(image)
image = show_bboxes(image, bounding_boxes, landmarks)
img = cv2.cvtColor(numpy.asarray(image), cv2.COLOR_RGB2BGR)
cv2.imshow("face detect", img)
if cv2.waitKey(100) & 0xff == ord('q'):
break
capture.release()
cv2.destroyAllWindows()
def main():
image = Image.open('images/test.jpg')
opencv_image = cv2.imread("images/test.jpg")
print("image size: ", image.size, ", opencv image size: ", opencv_image.shape)
bounding_boxes, landmarks = detect_faces(image)
print("bounding_boxes: ", bounding_boxes, ", landmarks: ", landmarks)
# save face pic
for bbox in bounding_boxes:
cropped = opencv_image[int(bbox[1] - 20):int(bbox[3] + 20), int(bbox[0] - 20):int(bbox[2]) + 20]
print("cropped size: ", cropped.shape)
cv2.imshow("Face extract", cropped)
# get eyes center xy
lks = landmarks[0]
center_x = (lks[0] + lks[1]) / 2 - bbox[0]
center_y = (lks[5] + lks[6]) / 2 - bbox[1]
# center_x = (lks[0] + lks[1]) / 2
# center_y = (lks[5] + lks[6]) / 2
eyesCenter = (center_x, center_y)
# get eye angle
dy = lks[6] - lks[5]
dx = lks[1] - lks[0]
angle = math.atan(dy/dx) / 3.14 * 180.0
# get rototion matrix
retval = cv2.getRotationMatrix2D(eyesCenter, angle, 1.0)
print("angle: ", angle, ", retval: ", retval, ", eyesCenter: ", eyesCenter, ", cropped size: ", cropped.shape)
# get warp affine
#dst = cv2.warpAffine(opencv_image, retval, opencv_image.size)
dst = cv2.warpAffine(cropped, retval, (cropped.shape[1], cropped.shape[0]))
print("dst: ", type(dst), "len dst: ", len(dst), "dst shape: ", dst.shape)
cv2.imshow("Face alignment", dst)
image = show_bboxes(image, bounding_boxes, landmarks)
image.show()
cv2.waitKey(0)
def main():
image = Image.open('images/test3.jpg')
bounding_boxes, landmarks = detect_faces(image)
image = show_bboxes(image, bounding_boxes, landmarks)
image.show()
def main():
cap = cv2.VideoCapture(
'/home/admin/face_detection/dataset/xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx.mp4'
)
mog = cv2.createBackgroundSubtractorMOG2(1, 55)
while (1):
ret, frame = cap.read()
if (ret == False):
break
if (i % 5):
i += 1
continue
j = str(i)
cv2.imwrite('image/' + j + '.jpg', frame)
#image = Image.open('images/test3.jpg')
image1 = Image.open('image/' + j + '.jpg')
path = 'image/' + j + '.jpg'
bounding_boxes, landmarks = detect_faces(image1)
#image = show_bboxes(image, bounding_boxes, landmarks)
image2 = show_bboxes(image1, bounding_boxes, landmarks)
a = 0
for b in bounding_boxes:
a = 1
break
if a:
fgmask = mog.apply(frame, 1)
dilate = cv2.dilate(fgmask, (21, 21), iterations=1)
(cnts, _) = cv2.findContours(dilate.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
ee = open('chayi/' + j + ' chayi' + '.txt', 'a')
for c in cnts:
c_area = cv2.contourArea(c)
if (c_area > 3000):
cc = str(c_area)
ee.write(cc + '\n')
f = open('txt' + p + '/' + j + '.txt', 'a')
for b in bounding_boxes:
x = ((b[0] + b[2]) / 2) / (image1.size[0])
y = (0.5 * b[1] + 0.5 * b[3]) / (image1.size[1])
w = ((b[2] - b[0]) * 1.0) / (image1.size[0])
h = ((b[3] - b[1]) * 1.0) / (image1.size[1])
x = str(x)
y = str(y)
w = str(w)
h = str(h)
f.write('0 ' + x + ' ' + y + ' ' + w + ' ' + h + '\n')
z = int(j)
image1.save('dataset' + p + '/' + j + '.jpg')
break
#image.show()
#image1.show()
i += 1