def process_multi(img_path, model):
image = common.read_imgfile(img_path, None, None)
filename = os.path.split(img_path)[1].split('.')[0]
scales = ast.literal_eval(node_or_string='[None]')
humans = model.inference(image, scales=scales)
image = model.draw_humans(image, humans, imgcopy=False)
# image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
path_save_2d = path = './results/' + filename + '_2d.jpg'
cv2.imwrite(path_save_2d, image)
# cv2.imshow('result', image)
#cv2.waitKey()
#poseLifting = Prob3dPose('/data/ai/JF/pose_estimation/multi_pose_estimator/lifting/models/prob_model_params.mat')
poseLifting = Prob3dPose('./lifting/models/prob_model_params.mat')
image_h, image_w = image.shape[:2]
standard_w = 640
standard_h = 480
pose_2d_mpiis = []
visibilities = []
for human in humans:
pose_2d_mpii, visibility = common.MPIIPart.from_coco(human)
pose_2d_mpiis.append([(int(x * image_w + 0.5), int(y * image_h + 0.5))
for x, y in pose_2d_mpii])
visibilities.append(visibility)
pose_2d_mpiis = np.array(pose_2d_mpiis)
visibilities = np.array(visibilities)
transformed_pose2d, weights = poseLifting.transform_joints(
pose_2d_mpiis, visibilities)
pose_3d = poseLifting.compute_3d(transformed_pose2d, weights)
num = len(pose_3d)
if num % 2 == 0:
l = num // 2
else:
l = num // 2 + 1
IMAGES_PATH = './results/'
if not os.path.exists(IMAGES_PATH):
os.makedirs(IMAGES_PATH)
path_save_3d = './results/' + filename + '_3d.png'
fig = plt.figure()
for i, single_3d in enumerate(pose_3d):
plot_pose(single_3d, i, l, fig, num)
plt.savefig(path_save_3d)
# plt.show()
return path_save_2d, path_save_3d
def main():
parser = argparse.ArgumentParser(description='tf-pose-estimation run')
parser.add_argument('--image', type=str, default='./images/p3.jpg')
parser.add_argument(
'--resolution',
type=str,
default='432x368',
help='network input resolution. default=432x368')
parser.add_argument(
'--model',
type=str,
default='mobilenet_thin',
help='cmu / mobilenet_thin')
parser.add_argument(
'--scales',
type=str,
default='[None]',
help='for multiple scales, eg. [1.0, (1.1, 0.05)]')
parser.add_argument(
'--stick_only',
action='store_true',
help='save output without other analysis')
parser.add_argument('--plot_3d', action='store_true', help='save 3d poses')
args = parser.parse_args()
scales = ast.literal_eval(args.scales)
w, h = model_wh(args.resolution)
e = TfPoseEstimator(get_graph_path(args.model), target_size=(w, h))
# estimate human poses from a single image !
image = common.read_imgfile(args.image, None, None)
# image = cv2.fastNlMeansDenoisingColored(image, None, 10, 10, 7, 21)
t = time.time()
humans = e.inference(image, scales=scales)
elapsed = time.time() - t
logger.info('inference image: %s in %.4f seconds.' % (args.image, elapsed))
image = TfPoseEstimator.draw_humans(image, humans, imgcopy=False)
# cv2.imshow('tf-pose-estimation result', image)
# cv2.waitKey()
import matplotlib.pyplot as plt
fig = plt.figure()
a = fig.add_subplot(2, 2, 1)
a.set_title('Result')
rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
if args.stick_only:
cv2.imwrite('output/test.png', image)
logger.info('image saved: {}'.format('output/test.png'))
import sys
sys.exit(0)
plt.imshow(rgb_image)
bgimg = cv2.cvtColor(image.astype(np.uint8), cv2.COLOR_BGR2RGB)
bgimg = cv2.resize(
bgimg, (e.heatMat.shape[1], e.heatMat.shape[0]),
interpolation=cv2.INTER_AREA)
# show network output
a = fig.add_subplot(2, 2, 2)
plt.imshow(bgimg, alpha=0.5)
tmp = np.amax(e.heatMat[:, :, :-1], axis=2)
plt.imshow(tmp, cmap=plt.cm.gray, alpha=0.5)
plt.colorbar()
tmp2 = e.pafMat.transpose((2, 0, 1))
tmp2_odd = np.amax(np.absolute(tmp2[::2, :, :]), axis=0)
tmp2_even = np.amax(np.absolute(tmp2[1::2, :, :]), axis=0)
a = fig.add_subplot(2, 2, 3)
a.set_title('Vectormap-x')
# plt.imshow(CocoPose.get_bgimg(inp, target_size=(vectmap.shape[1], vectmap.shape[0])), alpha=0.5)
plt.imshow(tmp2_odd, cmap=plt.cm.gray, alpha=0.5)
plt.colorbar()
a = fig.add_subplot(2, 2, 4)
a.set_title('Vectormap-y')
# plt.imshow(CocoPose.get_bgimg(inp, target_size=(vectmap.shape[1], vectmap.shape[0])), alpha=0.5)
plt.imshow(tmp2_even, cmap=plt.cm.gray, alpha=0.5)
plt.colorbar()
if not os.path.exists('output'):
os.mkdir('output')
plt.savefig('output/test.png')
logger.info('image saved: {}'.format('output/test.png'))
if not args.plot_3d:
import sys
sys.exit(0)
#For plotting in 3d
logger.info('3d lifting initialization.')
poseLifting = Prob3dPose('./src/lifting/models/prob_model_params.mat')
image_h, image_w = image.shape[:2]
standard_w = 640
standard_h = 480
pose_2d_mpiis = []
visibilities = []
for human in humans:
pose_2d_mpii, visibility = common.MPIIPart.from_coco(human)
pose_2d_mpiis.append([(int(x * standard_w + 0.5),
int(y * standard_h + 0.5))
for x, y in pose_2d_mpii])
visibilities.append(visibility)
pose_2d_mpiis = np.array(pose_2d_mpiis)
visibilities = np.array(visibilities)
transformed_pose2d, weights = poseLifting.transform_joints(
pose_2d_mpiis, visibilities)
pose_3d = poseLifting.compute_3d(transformed_pose2d, weights)
for i, single_3d in enumerate(pose_3d):
plot_pose(single_3d)
plt.draw()
plt.savefig('output/pose_3d_test.png')
logger.info('3d plot saved: {}'.format('output/pose_3d_test.png'))
plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
# show network output
a = fig.add_subplot(2, 2, 2)
# plt.imshow(CocoPose.get_bgimg(inp, target_size=(heatmap.shape[1], heatmap.shape[0])), alpha=0.5)
tmp = np.amax(e.heatMat, axis=2)
plt.imshow(tmp, cmap=plt.cm.gray, alpha=0.5)
plt.colorbar()
tmp2 = e.pafMat.transpose((2, 0, 1))
tmp2_odd = np.amax(np.absolute(tmp2[::2, :, :]), axis=0)
tmp2_even = np.amax(np.absolute(tmp2[1::2, :, :]), axis=0)
a = fig.add_subplot(2, 2, 3)
a.set_title('Vectormap-x')
# plt.imshow(CocoPose.get_bgimg(inp, target_size=(vectmap.shape[1], vectmap.shape[0])), alpha=0.5)
plt.imshow(tmp2_odd, cmap=plt.cm.gray, alpha=0.5)
plt.colorbar()
a = fig.add_subplot(2, 2, 4)
a.set_title('Vectormap-y')
# plt.imshow(CocoPose.get_bgimg(inp, target_size=(vectmap.shape[1], vectmap.shape[0])), alpha=0.5)
plt.imshow(tmp2_even, cmap=plt.cm.gray, alpha=0.5)
plt.colorbar()
for i, single_3d in enumerate(pose_3d):
plot_pose(single_3d)
plt.show()
pass
plt.colorbar()
plt.show()
import sys
sys.exit(0)
logger.info('3d lifting initialization.')
poseLifting = Prob3dPose('./src/lifting/models/prob_model_params.mat')
image_h, image_w = image.shape[:2]
standard_w = 640
standard_h = 480
pose_2d_mpiis = []
visibilities = []
for human in humans:
pose_2d_mpii, visibility = common.MPIIPart.from_coco(human)
pose_2d_mpiis.append([(int(x * standard_w + 0.5), int(y * standard_h + 0.5)) for x, y in pose_2d_mpii])
visibilities.append(visibility)
pose_2d_mpiis = np.array(pose_2d_mpiis)
visibilities = np.array(visibilities)
transformed_pose2d, weights = poseLifting.transform_joints(pose_2d_mpiis, visibilities)
pose_3d = poseLifting.compute_3d(transformed_pose2d, weights)
for i, single_3d in enumerate(pose_3d):
plot_pose(single_3d)
plt.show()
pass
def adHocData(self):
directory_in_str = sys.path[0] + "\\..\\images\\OurTest\\"
# Delete old csv files
try:
os.remove(outputfile)
os.remove(cleanedOutputfile)
except OSError:
pass
# Run through every image in the folder
for file in os.listdir(directory_in_str):
filename = os.fsdecode(file)
if filename.endswith(".jpg") or filename.endswith(".png"):
fullpath = directory_in_str + filename
# Estimate human poses from a single image !
image = common.read_imgfile(fullpath, None, None)
# image = cv2.fastNlMeansDenoisingColored(image, None, 10, 10, 7, 21)
t = time.time()
humans = self.e.inference(image, scales=self.scales)
elapsed = time.time() - t
logger.info('inference image: %s in %.4f seconds.' %
(fullpath, elapsed))
image = TfPoseEstimator.draw_humans(image,
humans,
imgcopy=False)
# cv2.imshow('tf-pose-estimation result', image)
# cv2.waitKey()
myFile = open(outputfile, 'a')
# myFile.write(str(filename) + ',')
# print(filename)
myFile.write('\n')
myFile.close()
# Attempt to plot our skeletons onto the image
try:
fig = plt.figure()
a = fig.add_subplot(2, 2, 1)
a.set_title('Result')
plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
bgimg = cv2.cvtColor(image.astype(np.uint8),
cv2.COLOR_BGR2RGB)
bgimg = cv2.resize(
bgimg,
(self.e.heatMat.shape[1], self.e.heatMat.shape[0]),
interpolation=cv2.INTER_AREA)
# show network output
a = fig.add_subplot(2, 2, 2)
plt.imshow(bgimg, alpha=0.5)
tmp = np.amax(self.e.heatMat[:, :, :-1], axis=2)
plt.imshow(tmp, cmap=plt.cm.gray, alpha=0.5)
plt.colorbar()
tmp2 = self.e.pafMat.transpose((2, 0, 1))
tmp2_odd = np.amax(np.absolute(tmp2[::2, :, :]), axis=0)
tmp2_even = np.amax(np.absolute(tmp2[1::2, :, :]), axis=0)
a = fig.add_subplot(2, 2, 3)
a.set_title('Vectormap-x')
# plt.imshow(CocoPose.get_bgimg(inp, target_size=(vectmap.shape[1], vectmap.shape[0])), alpha=0.5)
plt.imshow(tmp2_odd, cmap=plt.cm.gray, alpha=0.5)
plt.colorbar()
a = fig.add_subplot(2, 2, 4)
a.set_title('Vectormap-y')
# plt.imshow(CocoPose.get_bgimg(inp, target_size=(vectmap.shape[1], vectmap.shape[0])), alpha=0.5)
plt.imshow(tmp2_even, cmap=plt.cm.gray, alpha=0.5)
plt.colorbar()
plt.show()
logger.info('3d lifting initialization.')
poseLifting = Prob3dPose(
sys.path[0] +
'\\lifting\\models\\prob_model_params.mat')
image_h, image_w = image.shape[:2]
standard_w = 640
standard_h = 480
pose_2d_mpiis = []
visibilities = []
for human in humans:
pose_2d_mpii, visibility = common.MPIIPart.from_coco(
human)
pose_2d_mpiis.append([(int(x * standard_w + 0.5),
int(y * standard_h + 0.5))
for x, y in pose_2d_mpii])
visibilities.append(visibility)
pose_2d_mpiis = np.array(pose_2d_mpiis)
visibilities = np.array(visibilities)
transformed_pose2d, weights = poseLifting.transform_joints(
pose_2d_mpiis, visibilities)
pose_3d = poseLifting.compute_3d(transformed_pose2d,
weights)
for i, single_3d in enumerate(pose_3d):
plot_pose(single_3d)
plt.show()
except:
print("Error when plotting image ")
dataScriptGenerator.dataCleanup(self)
pose_3d = poseLifting.compute_3d(transformed_pose2d, weights)
# print(pose_3d,'\n')
num = len(pose_3d)
if num % 2 == 0:
l = num // 2
else:
l = num // 2 + 1
VIDEOS_PATH = './results/3D_video/'
if not os.path.exists(VIDEOS_PATH):
os.makedirs(VIDEOS_PATH)
fig = plt.figure()
for i, single_3d in enumerate(pose_3d):
# print('i:',i)
# print('num:', num)
plot_pose(single_3d, i, l, fig, num)
# plt.show()
plt.savefig('./results/3D_video/img_3d.png')
img_3d = cv2.imread('./results/3D_video/img_3d.png')
video_3d.write(img_3d)
# plt.show()
#########################################
if ret == False:
break
# if cv2.waitKey(1) == 27:
# break
video_3d.release()
video.release()
cv2.destroyAllWindows()
