def upload_file():
if request.method == 'POST':
code = request.get_data()
img = base64.b64decode(code.decode().split(',')[1])
fstr = IMAGE_FILE_DIR + '/' + time.strftime("%Y%m%d%H%M%S") + '.jpg'
file = open(fstr, 'wb')
file.write(img)
image = cv2.imread(fstr)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # conversion to rgb
# create pose estimator
image_size = image.shape
pose_estimator = PoseEstimator(image_size, SESSION_PATH,
PROB_MODEL_PATH)
# load model
pose_estimator.initialise()
# estimation
pose_2d, visibility, pose_3d = pose_estimator.estimate(image)
# close model
pose_estimator.close()
# Show 2D and 3D poses
file.close()
print(pose_2d)
draw_limbs(in_image, data_2d, joint_visibility)
return (base64.b64encode(in_image), 200)
def display_results(in_image, data_2d, joint_visibility, data_3d):
"""Plot 2D and 3D poses for each of the people in the image."""
plt.figure()
draw_limbs(in_image, data_2d, joint_visibility)
plt.imshow(in_image)
plt.axis('off')
# Show 3D poses
for single_3D in data_3d:
# or plot_pose(Prob3dPose.centre_all(single_3D))
plot_pose(single_3D)
plt.show()
def display_results(in_image, data_2d, joint_visibility, data_3d, frame):
"""Plot 2D and 3D poses for each of the people in the image."""
plt.figure()
draw_limbs(in_image, data_2d, joint_visibility)
# plt.imshow(in_image)
plt.axis('off')
# Show 3D poses
for single_3D in data_3d:
# or plot_pose(Prob3dPose.centre_all(single_3D))
plot_pose(single_3D)
pngName = 'png/test_{0}.jpg'.format(str(frame).zfill(12))
plt.savefig(pngName)
def display_results(in_image, data_2d, joint_visibility, data_3d):
"""
FOR VISUALIZATION OF 'LIFTING FROM THE DEEP' OUTPUT FOR ONE FRAME
Plot 2D and 3D poses for each of the people in the image.
"""
plt.figure()
draw_limbs(in_image, data_2d, joint_visibility)
plt.imshow(in_image)
plt.axis('off')
# Show 3D poses
for single_3D in data_3d:
# or plot_pose(Prob3dPose.centre_all(single_3D))
plot_pose(single_3D)
plt.show()
def display_results(mode, in_image, data_2d, visibilities, data_3d):
"""Plot 2D and 3D poses for each of the people in the image."""
n_poses = len(data_3d)
if mode == 'openpose':
color_im = OpPoseEstimator.draw_humans(in_image,
data_2d,
imgcopy=False)
else:
draw_limbs(in_image, data_2d, visibilities)
plt.subplot(1, n_poses + 1, 1)
plt.imshow(in_image)
plt.axis('off')
# Show 3D poses
for idx, single_3D in enumerate(data_3d):
plot_pose(Prob3dPose.centre_all(single_3D),
visibility_to_3d(visibilities[idx]), n_poses + 1, idx + 2)
def display_results(in_image, data_2d, joint_visibility, data_3d, i):
"""
Plot 2D and 3D poses for each of the people in the image;
:param in_image:
:param data_2d:
:param joint_visibility:
:param data_3d:
:param i:
:return:
"""
plt.figure(i)
draw_limbs(in_image, data_2d, joint_visibility, i)
plt.imshow(in_image)
# plt.axis('off')
# Show 3D poses
for single_3D in data_3d:
# or plot_pose(Prob3dPose.centre_all(single_3D))
plot_pose(single_3D)
plt.show()
def estimate_video():
fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
video_writer1 = cv2.VideoWriter(
PROJECT_PATH +
'/data/videos/test_result_{}.mp4'.format(str(time.time())), fourcc,
30.0, (640, 480))
cap = cv2.VideoCapture(PROJECT_PATH + '/data/videos/test_video.mp4')
import mpl_toolkits.mplot3d.axes3d as p3
fig = plt.figure()
ax = fig.gca(projection='3d')
pose_list = []
def animate(pose):
assert (pose.ndim == 2)
assert (pose.shape[0] == 3)
ax.clear()
for c in _CONNECTION:
col = '#%02x%02x%02x' % joint_color(c[0])
ax.plot([pose[0, c[0]], pose[0, c[1]]],
[pose[1, c[0]], pose[1, c[1]]],
[pose[2, c[0]], pose[2, c[1]]],
c=col)
for j in range(pose.shape[1]):
col = '#%02x%02x%02x' % joint_color(j)
ax.scatter(pose[0, j],
pose[1, j],
pose[2, j],
c=col,
marker='o',
edgecolor=col)
# smallest = pose.min()
# largest = pose.max()
# print('smallest:', smallest) # -885.36476784
# print('largest:', largest) # 809.97294291
#
# ax.set_xlim3d(smallest, largest)
# ax.set_ylim3d(smallest, largest)
# ax.set_zlim3d(smallest, largest)
ax.set_xlim3d(-1000, 1000)
ax.set_ylim3d(-1000, 1000)
ax.set_zlim3d(-1000, 1000)
if (cap.isOpened() == False):
print("Error opening video stream or file")
import matplotlib.animation as manimation
FFMpegWriter = manimation.writers['ffmpeg']
metadata = dict(title='Movie Test',
artist='Matplotlib',
comment='Movie support!')
writer = FFMpegWriter(fps=30, metadata=metadata)
# create pose estimator
pose_estimator = PoseEstimator((480, 640, 3), SESSION_PATH,
PROB_MODEL_PATH)
# load model
pose_estimator.initialise()
count = 0
while cap.isOpened():
count += 1
if count == 300:
break
print('count:{}'.format(str(count)))
ret_val, frame = cap.read()
frame = cv2.resize(frame, (640, 480))
try:
# estimation
start = time.time()
pose_2d, visibility, pose_3d = pose_estimator.estimate(frame)
print(time.time() - start)
except:
continue
pose_list.append(pose_3d[0])
draw_limbs(frame, pose_2d, visibility)
video_writer1.write(frame)
# close model
pose_estimator.close()
with writer.saving(
fig, PROJECT_PATH +
'/data/videos/test_result_3d_{}.mp4'.format(str(time.time())),
100):
for i in range(len(pose_list)):
animate(pose_list[i])
writer.grab_frame()
