def _run_net(self):
# inference
hm, xm, ym, zm = self.sess.run([self.heatmap, self.x_heatmap, self.y_heatmap, self.z_heatmap],
{self.input_crops: self.input_batch})
# average scale outputs
hm_size = self.box_size // self.hm_factor
hm_avg = np.zeros((hm_size, hm_size, self.joints_num))
xm_avg = np.zeros((hm_size, hm_size, self.joints_num))
ym_avg = np.zeros((hm_size, hm_size, self.joints_num))
zm_avg = np.zeros((hm_size, hm_size, self.joints_num))
for i in range(len(self.scales)):
rescale = 1.0 / self.scales[i]
scaled_hm = utils.img_scale(hm[i, :, :, :], rescale)
scaled_x_hm = utils.img_scale(xm[i, :, :, :], rescale)
scaled_y_hm = utils.img_scale(ym[i, :, :, :], rescale)
scaled_z_hm = utils.img_scale(zm[i, :, :, :], rescale)
mid = [scaled_hm.shape[0] // 2, scaled_hm.shape[1] // 2]
hm_avg += scaled_hm[mid[0] - hm_size//2: mid[0] + hm_size//2, mid[1] - hm_size//2: mid[1] + hm_size//2, :]
xm_avg += scaled_x_hm[mid[0] - hm_size//2: mid[0] + hm_size//2, mid[1] - hm_size//2: mid[1] + hm_size//2, :]
ym_avg += scaled_y_hm[mid[0] - hm_size//2: mid[0] + hm_size//2, mid[1] - hm_size//2: mid[1] + hm_size//2, :]
zm_avg += scaled_z_hm[mid[0] - hm_size//2: mid[0] + hm_size//2, mid[1] - hm_size//2: mid[1] + hm_size//2, :]
hm_avg /= len(self.scales)
xm_avg /= len(self.scales)
ym_avg /= len(self.scales)
zm_avg /= len(self.scales)
self.joints_2d = utils.extract_2d_joints_from_heatmap(hm_avg, self.box_size, self.hm_factor)
self.joints_3d = utils.extract_3d_joints_from_heatmap(self.joints_2d, xm_avg, ym_avg, zm_avg, self.box_size,
self.hm_factor)
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
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()