def run_demo(net,action_net, image_provider, height_size, cpu):
net = net.eval()
if not cpu:
net = net.cuda()
stride = 8
upsample_ratio = 4
num_keypoints = Pose.num_kpts
i = 0
for img in image_provider:
orig_img = img.copy()
# print(i)
if i % 1 == 0:
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, demo=True)
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])
if len(pose.getKeyPoints()) >= 10:
current_poses.append(pose)
# current_poses.append(pose)
for pose in current_poses:
pose.img_pose = pose.draw(img,show_draw=True)
crown_proportion = pose.bbox[2]/pose.bbox[3] #宽高比
pose = action_detect(action_net,pose,crown_proportion)
if pose.pose_action == 'fall':
cv2.rectangle(img, (pose.bbox[0], pose.bbox[1]),
(pose.bbox[0] + pose.bbox[2], pose.bbox[1] + pose.bbox[3]), (0, 0, 255),thickness=3)
cv2.putText(img, 'state: {}'.format(pose.pose_action), (pose.bbox[0], pose.bbox[1] - 16),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 255))
else:
cv2.rectangle(img, (pose.bbox[0], pose.bbox[1]),
(pose.bbox[0] + pose.bbox[2], pose.bbox[1] + pose.bbox[3]), (0, 255, 0))
cv2.putText(img, 'state: {}'.format(pose.pose_action), (pose.bbox[0], pose.bbox[1] - 16),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 255, 0))
img = cv2.addWeighted(orig_img, 0.6, img, 0.4, 0)
cv2.imshow('Lightweight Human Pose Estimation Python Demo', img)
cv2.waitKey(1)
i += 1
cv2.destroyAllWindows()
def run_demo(net, action_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
with open(
"D:/py/openpose_lightweight/performance_evaluation/action_result.txt",
"a") as f:
for img, img_name, label in image_provider:
if label == 'fall':
label_ = 0
else:
label_ = 1
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, demo=True)
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])
if len(pose.getKeyPoints()) >= 12:
current_poses.append(pose)
for pose in current_poses:
pose.img_pose = pose.draw(img)
crown_proportion = pose.bbox[2] / pose.bbox[3] # 宽高比
pose = action_detect(action_net, pose, crown_proportion)
cv2.rectangle(img, (pose.bbox[0], pose.bbox[1]),
(pose.bbox[0] + pose.bbox[2],
pose.bbox[1] + pose.bbox[3]), (0, 255, 0))
f.write(
f"{label_} {pose.action_fall} {pose.action_normal}\n")
f.flush()
break
cv2.imshow('Lightweight Human Pose Estimation Python Demo', img)
cv2.waitKey(1)
def run_demo(net, action_net, image_provider, height_size, cpu, boxList):
net = net.eval()
print(torch.cuda.device_count())
print(torch.cuda.is_available())
a = torch.Tensor(5, 3)
a = a.cuda()
print(a)
if not cpu:
net = net.cuda()
stride = 8
upsample_ratio = 4
num_keypoints = Pose.num_kpts # 18
i = 0
for img in image_provider: # 遍历图像集
orig_img = img.copy() # copy 一份
# print(i)
fallFlag = 0
if i % 1 == 0:
heatmaps, pafs, scale, pad = infer_fast(
net, img, height_size, stride, upsample_ratio,
cpu) # 返回热图,paf,输入模型图象相比原始图像缩放倍数,输入模型图像padding尺寸
total_keypoints_num = 0
all_keypoints_by_type = [
] # all_keypoints_by_type为18个list，每个list包含Ni个当前点的x、y坐标，当前点热图值，当前点在所有特征点中的index
for kpt_idx in range(
num_keypoints): # 19th for bg 第19个为背景，之考虑前18个关节点
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, demo=True
) # 得到所有分配的人（前18维为每个人各个关节点在所有关节点中的索引，后两唯为每个人得分及每个人关节点数量），及所有关节点信息
for kpt_id in range(all_keypoints.shape[0]): # 依次将每个关节点信息缩放回原始图像上
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)): # 依次遍历找到的每个人
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])
posebox = (int(pose.bbox[0]), int(pose.bbox[1]),
int(pose.bbox[0]) + int(pose.bbox[2]),
int(pose.bbox[1]) + int(pose.bbox[3]))
coincideValue = coincide(boxList, posebox)
print(posebox)
print('coincideValue:' + str(coincideValue))
if len(
pose.getKeyPoints()
) >= 10 and coincideValue >= 0.3 and pose.lowerHalfFlag < 3: # 当人体的点数大于10个的时候算作一个人,同时判断yolov5的框和pose的框是否有交集并且占比30%,同时要有下半身
current_poses.append(pose)
for pose in current_poses:
pose.img_pose = pose.draw(img, is_save=True, show_draw=True)
crown_proportion = pose.bbox[2] / pose.bbox[3] #宽高比
pose = action_detect(action_net, pose,
crown_proportion) #判断摔倒还是正常
if pose.pose_action == 'fall':
cv2.rectangle(img, (pose.bbox[0], pose.bbox[1]),
(pose.bbox[0] + pose.bbox[2],
pose.bbox[1] + pose.bbox[3]), (0, 0, 255),
thickness=3)
cv2.putText(img, 'state: {}'.format(pose.pose_action),
(pose.bbox[0], pose.bbox[1] - 16),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 255))
fallFlag = 1
else:
cv2.rectangle(img, (pose.bbox[0], pose.bbox[1]),
(pose.bbox[0] + pose.bbox[2],
pose.bbox[1] + pose.bbox[3]), (0, 255, 0))
cv2.putText(img, 'state: {}'.format(pose.pose_action),
(pose.bbox[0], pose.bbox[1] - 16),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 255, 0))
# fallFlag = 1
if fallFlag == 1:
t = time.time()
# cv2.imwrite(f'C:/zqr/project/yolov5_openpose/Image/{t}.jpg', img)
print('我保存照片了')
img = cv2.addWeighted(orig_img, 0.6, img, 0.4, 0)
# 保存识别后的照片
# cv2.imwrite(f'C:/zqr/project/yolov5_openpose/Image/{t}.jpg', img)
# print('我保存照片了')
# cv2.imshow('Lightweight Human Pose Estimation Python Demo', img)
cv2.waitKey(1)
i += 1
cv2.destroyAllWindows()
