def capture():
camera = get_camera()
stamp, img = camera.capture()
scaled_image, gray = image_utils.img_preprocess(img)
faces = image_utils.detect_face(gray)
# Check if have face
if (len(faces) != 0):
[x, y, w, h] = image_utils.biggest_face(faces)
[x, y, w, h] = [int(x / 0.3), int(y / 0.3), int(w / 0.3), int(h / 0.3)]
crop_img = image_utils.img_crop(img, x, y, w, h)
img = image_utils.img_draw_rect(img, x, y, w, h)
if (w < 72):
# Size of face is too small
print('Please look closer')
else:
# Send FaceID api
base64_img = image_utils.img2base64(crop_img)
if (base64_img == ''):
print('Error when encode image')
else:
res = api_volley.verify(base64_img)
import pdb
pdb.set_trace()
print(res)
else:
print('Please look closer')
# import pdb; pdb.set_trace()
# return redirect(url_for('show_capture', timestamp=stamp))
return render_template('capture.html', stamp=stamp, path=stamp_file(stamp))
def camshift_face_track():
face_cascade = cv2.CascadeClassifier('Image_Lib/Face_Data/haarcascade_frontalface_default.xml')
termination = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1)
ALPHA = 0.5
camera = cv2.VideoCapture(0)
face_box = None
#wait till first face box is available
print "Waiting to get first face frame..."
while face_box is None:
grabbed, frame = camera.read()
if not grabbed:
raise EnvironmentError("Camera read failed!")
image_prev = cv2.pyrDown(frame)
face_box = utils.detect_face(face_cascade, image_prev)
print "Face found!"
prev_frames = image_prev.astype(np.float32)
while (True):
_, frame = camera.read()
image_curr = cv2.pyrDown(frame)
cv2.accumulateWeighted(image_curr, prev_frames, ALPHA)
image_curr = cv2.convertScaleAbs(prev_frames)
if face_box is not None:
face_box = camshift_track(image_curr, face_box, termination)
cv2.rectangle(image_curr, (face_box[0], face_box[1]), (face_box[0]+face_box[2], face_box[1] + face_box[3]),
(255, 0,0), 2)
# cv2.rectangle(image_curr, (box[0], box[1]), (box[0]+box[2], box[1] + box[3]),
# (0, 0,255), 2)
else:
face_box = utils.detect_face(face_cascade, image_curr)
cv2.imshow("Output", image_curr)
key = cv2.waitKey(1)
if key & 0xFF == ord('q'):
break
elif key & 0xFF == ord('r'):
print "Reseting face detection!"
face_box = None
p0 = []
prev_frame = None
while (True):
grabbed, frame = camera.read()
if not grabbed:
print "Camera read failed!"
break
frame = utils.image_resize(frame)
curr_frame_gray = cv2.equalizeHist(cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY))
info = ""
if prev_frame is None or len(p0) <= 3:
info = "Detecting..."
face = utils.detect_face(face_cascade, frame)
if face is not None:
prev_frame = curr_frame_gray
x,y,w,h = face
roi = np.zeros(prev_frame.shape, dtype=np.uint8)
roi[y:y+h, x:x+w] = 255
p0 = cv2.goodFeaturesToTrack(prev_frame, mask=roi, **feature_params)
else:
p1,st,err = cv2.calcOpticalFlowPyrLK(prev_frame, curr_frame_gray, p0, None, **lk_params)
# Update points being tracked to new good set
p0 = p1[st==1].reshape(-1,1,2)
info = "Tracking: %d" % len(p0)
for pt in p1:
a,b = pt.ravel()
cv2.circle(frame, (a,b), 3, (0,255,0), -1)
# ap.add_argument("-r", "--right", required=True, help="Path to right image")
# args = vars(ap.parse_args())
# imgL = cv2.pyrDown(cv2.imread(args["left"])) # downscale images for faster processing
# imgR = cv2.pyrDown(cv2.imread(args["right"]))
# stereo_match(imgL, imgR)
camera = cv2.VideoCapture(0)
accumulated_verts = None
face_box = None
while face_box is None:
grabbed, frame = camera.read()
if not grabbed:
raise EnvironmentError("Camera read failed!")
img = frame
face_box = utils.detect_face(face_cascade, img)
img_ref = get_resized_image(img, face_box)
print img_ref.shape
disparity = np.zeros((img_ref.shape[0], img_ref.shape[1]), dtype=np.uint8)
while (True):
_, frame = camera.read()
img = frame
face_box = utils.detect_face(face_cascade, img)
if face_box is not None:
img_curr = get_resized_image(img, face_box)
disparity |= stereo_match(img_ref, img_curr) # cv2.bitwise_and(disparity, stereo_match(imgRef, imgR))
# ap.add_argument("-r", "--right", required=True, help="Path to right image")
# args = vars(ap.parse_args())
# imgL = cv2.pyrDown(cv2.imread(args["left"])) # downscale images for faster processing
# imgR = cv2.pyrDown(cv2.imread(args["right"]))
# stereo_match(imgL, imgR)
camera = cv2.VideoCapture(0)
accumulated_verts = None
face_box = None
while face_box is None:
grabbed, frame = camera.read()
if not grabbed:
raise EnvironmentError("Camera read failed!")
img = frame
face_box = utils.detect_face(face_cascade, img)
img_ref = get_resized_image(img, face_box)
print img_ref.shape
disparity = np.zeros((img_ref.shape[0], img_ref.shape[1]), dtype=np.uint8)
while (True):
_, frame = camera.read()
img = frame
face_box = utils.detect_face(face_cascade, img)
if face_box is not None:
img_curr = get_resized_image(img, face_box)
disparity |= stereo_match(
img_ref, img_curr
return p
camera = cv2.VideoCapture(0)
face_cascade = cv2.CascadeClassifier(
'Image_Lib/Face_Data/haarcascade_frontalface_default.xml')
calibrate = True
calibration_rects = {}
while True:
face_box = None
grabbed, frame = camera.read()
frame = utils.image_resize(frame, height=600)
face_box = utils.detect_face(face_cascade, frame)
if face_box is None:
continue
cv2.rectangle(frame, (face_box[0], face_box[1]),
(face_box[0] + face_box[2], face_box[1] + face_box[3]),
(255, 0, 0), 2)
if calibrate:
utils.add_text(
frame, "Press: W - closest, S - farthest, C - neutral, Q - Done")
no_points_either_side = z_axis_length / 2
cv2.imshow("Calibrating...", frame)
key = cv2.waitKey(1) & 0xFF
