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import argparse
import cv2
import numpy as np
import torch
from models.with_mobilenet import PoseEstimationWithMobileNet
from modules.keypoints import extract_keypoints, group_keypoints
from modules.load_state import load_state
from modules.pose import Pose, track_poses
from val import normalize, pad_width
class ImageReader(object):
def __init__(self, file_names):
self.file_names = file_names
self.max_idx = len(file_names)
def __iter__(self):
self.idx = 0
return self
def __next__(self):
if self.idx == self.max_idx:
raise StopIteration
img = cv2.imread(self.file_names[self.idx], cv2.IMREAD_COLOR)
if img.size == 0:
raise IOError('Image {} cannot be read'.format(self.file_names[self.idx]))
self.idx = self.idx + 1
return img
class VideoReader(object):
def __init__(self, file_name):
self.file_name = file_name
try: # OpenCV needs int to read from webcam
self.file_name = int(file_name)
except ValueError:
pass
def __iter__(self):
self.cap = cv2.VideoCapture(self.file_name)
if not self.cap.isOpened():
raise IOError('Video {} cannot be opened'.format(self.file_name))
return self
def __next__(self):
was_read, img = self.cap.read()
if not was_read:
raise StopIteration
return img
def infer_fast(net, img, net_input_height_size, stride, upsample_ratio, cpu,
pad_value=(0, 0, 0), img_mean=np.array([128, 128, 128], np.float32), img_scale=np.float32(1/256)):
height, width, _ = img.shape
scale = net_input_height_size / height
scaled_img = cv2.resize(img, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_LINEAR)
scaled_img = normalize(scaled_img, img_mean, img_scale)
min_dims = [net_input_height_size, max(scaled_img.shape[1], net_input_height_size)]
padded_img, pad = pad_width(scaled_img, stride, pad_value, min_dims)
tensor_img = torch.from_numpy(padded_img).permute(2, 0, 1).unsqueeze(0).float()
if not cpu:
tensor_img = tensor_img.cuda()
stages_output = net(tensor_img)
stage2_heatmaps = stages_output[-2]
heatmaps = np.transpose(stage2_heatmaps.squeeze().cpu().data.numpy(), (1, 2, 0))
heatmaps = cv2.resize(heatmaps, (0, 0), fx=upsample_ratio, fy=upsample_ratio, interpolation=cv2.INTER_CUBIC)
stage2_pafs = stages_output[-1]
pafs = np.transpose(stage2_pafs.squeeze().cpu().data.numpy(), (1, 2, 0))
pafs = cv2.resize(pafs, (0, 0), fx=upsample_ratio, fy=upsample_ratio, interpolation=cv2.INTER_CUBIC)
return heatmaps, pafs, scale, pad
def run_demo(net, image_provider, height_size, cpu, track, smooth):
net = net.eval()
if not cpu:
net = net.cuda()
stride = 8
upsample_ratio = 4
num_keypoints = Pose.num_kpts
previous_poses = []
delay = 1
for img in image_provider:
orig_img = img.copy()
heatmaps, pafs, scale, pad = infer_fast(net, img, height_size, stride, upsample_ratio, cpu)
total_keypoints_num = 0
all_keypoints_by_type = []
for kpt_idx in range(num_keypoints): # 19th for bg
total_keypoints_num += extract_keypoints(heatmaps[:, :, kpt_idx], all_keypoints_by_type, total_keypoints_num)
pose_entries, all_keypoints = group_keypoints(all_keypoints_by_type, pafs)
for kpt_id in range(all_keypoints.shape[0]):
all_keypoints[kpt_id, 0] = (all_keypoints[kpt_id, 0] * stride / upsample_ratio - pad[1]) / scale
all_keypoints[kpt_id, 1] = (all_keypoints[kpt_id, 1] * stride / upsample_ratio - pad[0]) / scale
current_poses = []
for n in range(len(pose_entries)):
if len(pose_entries[n]) == 0:
continue
pose_keypoints = np.ones((num_keypoints, 2), dtype=np.int32) * -1
for kpt_id in range(num_keypoints):
if pose_entries[n][kpt_id] != -1.0: # keypoint was found
pose_keypoints[kpt_id, 0] = int(all_keypoints[int(pose_entries[n][kpt_id]), 0])
pose_keypoints[kpt_id, 1] = int(all_keypoints[int(pose_entries[n][kpt_id]), 1])
pose = Pose(pose_keypoints, pose_entries[n][18])
current_poses.append(pose)
if track:
track_poses(previous_poses, current_poses, smooth=smooth)
previous_poses = current_poses
for pose in current_poses:
pose.draw(img)
img = cv2.addWeighted(orig_img, 0.6, img, 0.4, 0)
for pose in current_poses:
cv2.rectangle(img, (pose.bbox[0], pose.bbox[1]),
(pose.bbox[0] + pose.bbox[2], pose.bbox[1] + pose.bbox[3]), (0, 255, 0))
if track:
cv2.putText(img, 'id: {}'.format(pose.id), (pose.bbox[0], pose.bbox[1] - 16),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 255))
cv2.imshow('Lightweight Human Pose Estimation Python Demo', img)
key = cv2.waitKey(delay)
if key == 27: # esc
return
elif key == 112: # 'p'
if delay == 1:
delay = 0
else:
delay = 1
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='''Lightweight human pose estimation python demo.
This is just for quick results preview.
Please, consider c++ demo for the best performance.''')
parser.add_argument('--checkpoint-path', type=str, required=True, help='path to the checkpoint')
parser.add_argument('--height-size', type=int, default=256, help='network input layer height size')
parser.add_argument('--video', type=str, default='', help='path to video file or camera id')
parser.add_argument('--images', nargs='+', default='', help='path to input image(s)')
parser.add_argument('--cpu', action='store_true', help='run network inference on cpu')
parser.add_argument('--track', type=int, default=1, help='track pose id in video')
parser.add_argument('--smooth', type=int, default=1, help='smooth pose keypoints')
args = parser.parse_args()
if args.video == '' and args.images == '':
raise ValueError('Either --video or --image has to be provided')
net = PoseEstimationWithMobileNet()
checkpoint = torch.load(args.checkpoint_path, map_location='cpu')
load_state(net, checkpoint)
frame_provider = ImageReader(args.images)
if args.video != '':
frame_provider = VideoReader(args.video)
else:
args.track = 0
run_demo(net, frame_provider, args.height_size, args.cpu, args.track, args.smooth)
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