def run(self):
# initial BB by HOG detection
self.BB_init()
success, frame = self.cameraCapture.read()
frame = frame.T if self.T else frame
while success and cv2.waitKey(1) == -1:
found, w = self.hog.detectMultiScale(frame)
if len(found) > 0:
self._draw_BB_rect(frame, found[np.argmax(w)])
scale = 400 / self.H
frame = cv2.resize(frame, (0, 0), fx=scale, fy=scale)
cv2.imshow(self._box_init_window_name, frame)
if self._clicked:
self._clicked = False
cv2.destroyWindow(self._box_init_window_name)
break
success, frame = self.cameraCapture.read()
frame = frame.T if self.T else frame
x, y, w, h = self.rect
plt.show()
# main loop
success, frame = self.cameraCapture.read()
frame = frame.T if self.T else frame
while success and cv2.waitKey(1) == -1:
t = time.time()
# crop bounding box from the raw frame
frame_cropped = frame[y:y + h, x:x + w, :]
# crop --> one sqrare input img for CNN
self.frame_square = utils.img_scale_squareify(
frame_cropped, self.box_size)
# one sqrare input img --> a batch of sqrare input imgs
self._create_input_batch()
# sqrare input img batch --CNN net--> 2d and 3d skeleton joint coordinates
self._run_net()
# filter to smooth the joint coordinate results
self._joint_coord_filter()
## plot ##
# 2d plotting
self.frame_square = utils.draw_limbs_2d(self.frame_square,
self.joints_2d,
self.joint_parents)
cv2.imshow('2D Prediction', self.frame_square)
# 3d plotting
self._imshow_3d()
print('FPS: {:>2.2f}'.format(1 / (time.time() - t)))
success, frame = self.cameraCapture.read()
frame = frame.T if self.T else frame
self._exit()
def main(q_start3d, q_joints):
init()
x, y, w, h = hog_box()
q_start3d.put(True)
success, frame = camera_capture.read()
while success and cv2.waitKey(1) == -1:
if T:
# mirror
# frame = np.transpose(frame, axes=[1, 0, 2])
# no mirror
frame = np.rot90(frame, 3)
# crop bounding box from the raw frame
frame_cropped = frame[y:y + h, x:x + w, :]
# vnect estimation
joints_2d, joints_3d = estimator(frame_cropped)
q_joints.put(joints_3d)
# 2d plotting
joints_2d[:, 0] += y
joints_2d[:, 1] += x
frame_draw = utils.draw_limbs_2d(frame.copy(), joints_2d,
joint_parents, [x, y, w, h])
frame_draw = utils.img_scale(frame_draw, 1024 / max(W_img, H_img))
cv2.imshow('2D Prediction', frame_draw)
# update bounding box
y_min = (np.min(joints_2d[:, 0]))
y_max = (np.max(joints_2d[:, 0]))
x_min = (np.min(joints_2d[:, 1]))
x_max = (np.max(joints_2d[:, 1]))
buffer_x = 0.8 * (x_max - x_min + 1)
buffer_y = 0.2 * (y_max - y_min + 1)
x, y = (max(int(x_min - buffer_x / 2),
0), max(int(y_min - buffer_y / 2), 0))
w, h = (int(min(x_max - x_min + buffer_x, W_img - x)),
int(min(y_max - y_min + buffer_y, H_img - y)))
# update frame
success, frame = camera_capture.read()
# angles_file.close()
try:
camera_capture.release()
cv2.destroyAllWindows()
except Exception as e:
print(e)
raise
def main(q_start3d, q_joints):
init()
x, y, w, h = hog_box()
q_start3d.put(True)
t = time.time()
success, frame = camera_capture.read()
q_joints_list = []
# width = int(camera_capture.get(cv2.CAP_PROP_FRAME_WIDTH))
# height = int(camera_capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
# fourcc = cv2.VideoWriter_fourcc(*'XVID')
# writer = cv2.VideoWriter("output.avi", fourcc, 30.0,(width, height))
while success and cv2.waitKey(1) == -1:
# crop bounding box from the raw frame
frame = np.transpose(frame, axes=[1, 0, 2]).copy() if T else frame
frame_cropped = frame[y:y + h, x:x + w, :]
# vnect estimating process
joints_2d, joints_3d = estimator(frame_cropped)
q_joints.put(joints_3d)
q_joints_list.append(joints_3d)
# 2d plotting
frame_square = utils.img_scale_squarify(frame_cropped, box_size)
frame_square = utils.draw_limbs_2d(frame_square, joints_2d,
joint_parents)
cv2.imshow('2D Prediction', frame_square)
# writer.write(frame_square)
# write angle data into txt
# angles_file.write('%f %f %f %f %f %f %f %f\n' % tuple([a for a in angles]))
success, frame = camera_capture.read()
q_joints_numpy = interpolation(q_joints_list)
np.save(savepath, q_joints_numpy)
# angles_file.close()
try:
camera_capture.release()
# writer.release()
cv2.destroyAllWindows()
except Exception as e:
print(e)
raise
success, frame = camera_capture.read()
while success and cv2.waitKey(1) == -1:
if T:
# mirror
# frame = np.transpose(frame, axes=[1, 0, 2])
# no mirror
frame = np.rot90(frame, 3)
# crop bounding box from the raw frame
frame_cropped = frame[y: y + h, x: x + w, :]
# vnect estimation
joints_2d, joints_3d = estimator(frame_cropped)
# 2d plotting
joints_2d[:, 0] += y
joints_2d[:, 1] += x
frame_draw = utils.draw_limbs_2d(frame.copy(), joints_2d, joint_parents, [x, y, w, h])
frame_draw = utils.img_scale(frame_draw, 1024 / max(W_img, H_img))
cv2.imshow('2D Prediction', frame_draw)
# update bounding box
y_min = (np.min(joints_2d[:, 0]))
y_max = (np.max(joints_2d[:, 0]))
x_min = (np.min(joints_2d[:, 1]))
x_max = (np.max(joints_2d[:, 1]))
buffer_x = 0.8 * (x_max - x_min + 1)
buffer_y = 0.2 * (y_max - y_min + 1)
x, y = (max(int(x_min - buffer_x / 2), 0),
max(int(y_min - buffer_y / 2), 0))
w, h = (int(min(x_max - x_min + buffer_x, W_img - x)),
int(min(y_max - y_min + buffer_y, H_img - y)))
# update frame
success, frame = camera_capture.read()
from src import utils
from src.hog_box import HOGBox
from src.estimator import VNectEstimator
box_size = 368
hm_factor = 8
joints_num = 21
scales = [1.0, 0.7]
joint_parents = [1, 15, 1, 2, 3, 1, 5, 6, 14, 8,
9, 14, 11, 12, 14, 14, 1, 4, 7, 10,
13]
estimator = VNectEstimator()
img = cv2.imread('./pic/test_pic.jpg')
hog = HOGBox()
hog.clicked = True
choose, rect = hog(img.copy())
x, y, w, h = rect
img_cropped = img[y: y + h, x: x + w, :]
joints_2d, joints_3d = estimator(img_cropped)
# 3d plotting
utils.draw_limbs_3d(joints_3d, joint_parents)
# 2d plotting
joints_2d[:, 0] += y
joints_2d[:, 1] += x
img_draw = utils.draw_limbs_2d(img.copy(), joints_2d, joint_parents, [x, y, w, h])
cv2.imshow('2D Prediction', img_draw)
cv2.waitKey()
cv2.destroyAllWindows()
#################
### Main Loop ###
#################
## trigger any keyboard events to stop the loop ##
# start 3d skeletal animation plotting
utils.plot_3d_init(joint_parents, joints_iter_gen)
t = time.time()
success, frame = camera_capture.read()
while success and cv2.waitKey(1) == -1:
# crop bounding box from the raw frame
frame = np.transpose(frame, axes=[1, 0, 2]).copy() if T else frame
frame_cropped = frame[y:y + h, x:x + w, :]
# vnect estimating process
joints_2d, joints_3d = estimator(frame_cropped)
# 2d plotting
frame_square = utils.img_scale_squareify(frame_cropped, box_size)
frame_square = utils.draw_limbs_2d(frame_square, joints_2d, joint_parents)
cv2.imshow('2D Prediction', frame_square)
# write angle data into txt
# angles_file.write('%f %f %f %f %f %f %f %f\n' % tuple([a for a in angles]))
success, frame = camera_capture.read()
# angles_file.close()
my_exit(camera_capture)
hm_factor)
for i in range(21):
if i == 0:
himg = hm[0, :, :, i]
ximg = xm[0, :, :, i]
yimg = ym[0, :, :, i]
zimg = zm[0, :, :, i]
else:
tmp = hm[0, :, :, i]
himg = np.hstack([himg, tmp])
tmp = xm[0, :, :, i]
ximg = np.hstack([ximg, tmp])
tmp = ym[0, :, :, i]
yimg = np.hstack([yimg, tmp])
tmp = zm[0, :, :, i]
zimg = np.hstack([zimg, tmp])
all_hm = np.vstack([himg, ximg, yimg, zimg])
cv2.imshow('all heatmaps', all_hm * 128)
img_res2d = utils.draw_limbs_2d(img_square[0, ...], joints_2d,
limb_parents)
cv2.imshow('2D results', img_res2d)
cv2.waitKey()
cv2.destroyAllWindows()
print(hm[0, :, :, 0])
# np.savetxt('original', hm[0, :, :, 0])