if not grabbed:
raise ValueError("Camera read failed!")
bg = utils.image_resize(frame, height=600).astype(np.float32)
while True:
grabbed, frame = camera.read()
if not grabbed:
print "Camera read failed"
break
frame = utils.image_resize(frame, height=600)
height, width, channels = frame.shape
if not calibrated:
# Sample hand color
utils.add_text(frame, "Press space after covering rectangle with hand. Hit SPACE when ready")
x, y, w, h = width / 4, height / 2, 50, 50
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.imshow("Calibration", frame)
if cv2.waitKey(2) & 0xFF == ord(' '):
roi = frame[y:y + h, x:x + w, :]
roi_hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
min_value = np.amin(roi_hsv, (0, 1))
max_value = np.amax(roi_hsv, (0, 1))
cv2.destroyWindow("Calibration")
calibrated = True
else:
cv2.accumulateWeighted(frame, bg, 0.01)
frame ^= cv2.convertScaleAbs(bg)
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)
prev_frame
utils.add_text(frame, info)
cv2.imshow("Output", frame)
key = cv2.waitKey(1) & 0xFF
if key== ord('q'):
break
elif key == ord('r'):
p0 = []
camera.release()
cv2.destroyAllWindows()
raise ValueError("Camera read failed!")
bg = utils.image_resize(frame, height=600).astype(np.float32)
while True:
grabbed, frame = camera.read()
if not grabbed:
print "Camera read failed"
break
frame = utils.image_resize(frame, height=600)
height, width, channels = frame.shape
if not calibrated:
# Sample hand color
utils.add_text(
frame,
"Press space after covering rectangle with hand. Hit SPACE when ready"
)
x, y, w, h = width / 4, height / 2, 50, 50
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.imshow("Calibration", frame)
if cv2.waitKey(2) & 0xFF == ord(' '):
roi = frame[y:y + h, x:x + w, :]
roi_hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
min_value = np.amin(roi_hsv, (0, 1))
max_value = np.amax(roi_hsv, (0, 1))
cv2.destroyWindow("Calibration")
calibrated = True
else:
cv2.accumulateWeighted(frame, bg, 0.01)
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
if key == ord('w'):
calibration_rects[0] = (face_box[2], face_box[3])
print calibration_rects[0]
elif key == ord('c'):
calibration_rects[no_points_either_side] = (face_box[2], face_box[3])
print calibration_rects[no_points_either_side]
elif key == ord('s'):
calibration_rects[z_axis_length - 1] = (face_box[2], face_box[3])
print calibration_rects[z_axis_length - 1]
good = d < 1
new_tracks = []
for tr, (x, y), good_flag in zip(tracks, p1.reshape(-1, 2), good):
if not good_flag:
continue
tr.append((x, y))
if len(tr) > track_length:
del tr[0]
new_tracks.append(tr)
tracks = new_tracks
# print color
cv2.polylines(output, [np.int32(tr) for tr in tracks], False,
[200, 200, 200], 2)
utils.add_text(output, ("tracking %d" % len(tracks)))
track_interval += 1
if track_interval % 100 == 0:
mask = 255 * np.ones(curr_gray_frame.shape)
for x, y in [np.int32(tr[-1]) for tr in tracks]:
cv2.circle(mask, (x, y), 5, 0, -1)
p = cv2.goodFeaturesToTrack(prev_gray_frame,
mask=None,
**feature_params)
tracks = []
if p is not None:
for x, y in np.float32(p).reshape(-1, 2):
tracks.append([(x, y)])
prev_gray_frame = curr_gray_frame
roi_x:roi_x + roi_w])
# print row, col
cv2.circle(frame, (roi_x + col, roi_y + row), 5, (0, 255, 0), -1)
roi_x = x + w - roi_w - int(w * 0.13)
cv2.rectangle(frame, (roi_x, roi_y), (roi_x + roi_w, roi_y + roi_h),
(255, 255, 0), 2)
row, col = tracker.find_eye_center(gray[roi_y:roi_y + roi_h,
roi_x:roi_x + roi_w])
# print row, col
cv2.circle(frame, (roi_x + col, roi_y + row), 5, (0, 0, 255), -1)
else:
utils.add_text(frame, "Face not found!")
# row, col = tracker.find_eye_center(gray[:, 0:frame.shape[1] / 2])
# print row, col
#
# cv2.circle(frame, (col, row), 10, (255, 255, 0), -1)
#
# row, col = tracker.find_eye_center(gray[:, frame.shape[1] / 2:])
# print row, col
#
# cv2.circle(frame, (frame.shape[1] / 2 + col, row), 10, (255, 0, 0), -1)
cv2.imshow("Output", frame)
key = cv2.waitKey(1) & 0xFF
if key == ord('q'):
break
d = abs(p0 -p0r).reshape(-1,2).max(-1)
good = d < 1
new_tracks = []
for tr, (x, y), good_flag in zip(tracks, p1.reshape(-1,2), good):
if not good_flag:
continue
tr.append((x,y))
if len(tr) > track_length:
del tr[0]
new_tracks.append(tr)
tracks = new_tracks
# print color
cv2.polylines(output, [np.int32(tr) for tr in tracks], False, [200, 200, 200], 2)
utils.add_text(output, ("tracking %d" % len(tracks)))
track_interval += 1
if track_interval % 10 == 0:
mask = 255 * np.ones(curr_gray_frame.shape)
for x,y in [np.int32(tr[-1]) for tr in tracks]:
cv2.circle(mask, (x,y), 5, 0, -1)
p = cv2.goodFeaturesToTrack(prev_gray_frame, mask=None, **feature_params)
tracks = []
if p is not None:
for x,y in np.float32(p).reshape(-1,2):
tracks.append([(x,y)])
prev_gray_frame = curr_gray_frame
cv2.imshow("Output", output)
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
if key == ord('w'):
calibration_rects[0] = (face_box[2], face_box[3])
print calibration_rects[0]
elif key == ord('c'):
calibration_rects[no_points_either_side] = (face_box[2],
face_box[3])
print calibration_rects[no_points_either_side]
elif key == ord('s'):
calibration_rects[z_axis_length - 1] = (face_box[2], face_box[3])
cv2.rectangle(frame, (roi_x, roi_y), (roi_x + roi_w, roi_y + roi_h), (255, 0, 0), 2)
row, col = tracker.find_eye_center(gray[roi_y:roi_y + roi_h, roi_x:roi_x + roi_w])
# print row, col
cv2.circle(frame, (roi_x + col, roi_y + row), 5, (0, 255, 0), -1)
roi_x = x + w - roi_w - int(w * 0.13)
cv2.rectangle(frame, (roi_x, roi_y), (roi_x + roi_w, roi_y + roi_h), (255, 255, 0), 2)
row, col = tracker.find_eye_center(gray[roi_y:roi_y + roi_h, roi_x:roi_x + roi_w])
# print row, col
cv2.circle(frame, (roi_x + col, roi_y + row), 5, (0, 0, 255), -1)
else:
utils.add_text(frame, "Face not found!")
# row, col = tracker.find_eye_center(gray[:, 0:frame.shape[1] / 2])
# print row, col
#
# cv2.circle(frame, (col, row), 10, (255, 255, 0), -1)
#
# row, col = tracker.find_eye_center(gray[:, frame.shape[1] / 2:])
# print row, col
#
# cv2.circle(frame, (frame.shape[1] / 2 + col, row), 10, (255, 0, 0), -1)
cv2.imshow("Output", frame)
key = cv2.waitKey(1) & 0xFF
if key == ord('q'):
break
