def __init__(self, model_path,use_cuda,map_location_flag,bad_time,left_time):
self.min_confidence = 0.3
self.nms_max_overlap = 1.0
self.extractor = Extractor(model_path, use_cuda=use_cuda,map_location_flag=map_location_flag)
# 违规时间
self.bad_time = bad_time
max_cosine_distance = 0.2
nn_budget = 100
metric = NearestNeighborDistanceMetric("cosine", max_cosine_distance, nn_budget)
self.tracker = Tracker(metric,left_time=left_time)
def __init__(self, model_path):
self.min_confidence = 0.3
self.nms_max_overlap = 1.0
self.extractor = Extractor(model_path, use_cuda=True)
max_cosine_distance = 0.2
nn_budget = 100
metric = NearestNeighborDistanceMetric("cosine", max_cosine_distance, nn_budget)
self.tracker = Tracker(metric)
def __init__(self, model_path):
super(DeepSort, self).__init__()
self.min_confidence = 0.3 #根据置信度对检测框进行过滤，即对置信度不足够高的检测框及特征予以删除；
self.nms_max_overlap = 1.0 #对检测框进行非最大值抑制，消除一个目标身上多个框的情况；
self.extractor = Extractor(model_path,
use_cuda=True) #读取当前帧目标检测框的位置及各检测框图像块的深度特征
max_cosine_distance = 0.2
nn_budget = 100
metric = NearestNeighborDistanceMetric("cosine", max_cosine_distance,
nn_budget)
self.tracker = Tracker(metric)
def __init__(self, model_path):
# 检测结果阈值。低于这个阈值的检测结果将会被忽略 # 过滤掉置信度小于self.min_confidence的bbox，生成detections
self.min_confidence = 0.25
self.nms_max_overlap = 1.0 # 非极大抑制的阈值 原始值1.0
# NMS (这里self.nms_max_overlap的值为1，即保留了所有的detections)
self.extractor = Extractor(model_path, use_cuda=True)
max_cosine_distance = 0.2 # 0.2 余弦距离的控制阈值 调节这个能改善IDsw
# 描述的区域的最大值 它是一个列表，列出了每次出现曲目的特征。nn_bodget确定此列表的大小。例如，如果它是10，则仅存储曲目在板上出现的最后10次的特征
nn_budget = 100
metric = NearestNeighborDistanceMetric("cosine", max_cosine_distance,
nn_budget)
self.tracker = Tracker(metric)
def run_demo(net, image_provider, height_size, cpu, track_ids): # , filename):
stride = 8
upsample_ratio = 4
num_keypoints = Pose.num_kpts # 18개
previous_poses = []
c = 0
ttt = 0
idxx = 0
csv_dict = {
'frame_number': [],
'driver_index': [],
'is_driver_flag': []
} #,'state' : []}
driver_find_flag = False
weird_state_flag = False
extractor = Extractor('default_checkpoints/ckpt.t7', True)
find_class = Find_assault(extractor)
for img in image_provider:
is_driver_flag = False
t5 = time.time()
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,
demo=True)
for kpt_id in range(all_keypoints.shape[0]): ##사이즈 변환
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)
pose.draw(img)
img = cv2.addWeighted(orig_img, 0.6, img, 0.4, 0)
# 운전자를 못 찾았으면 find_driver 에 들어감.
if driver_find_flag is False:
driver_find_flag, find_driver_count, find_state = find_class.find_driver(
current_poses, orig_img)
cv2.putText(img, "Driver_find_count : " + str(find_driver_count),
(0, 20), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0))
cv2.putText(img, "State : Find_Driver", (0, 50),
cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0))
else:
# print("idxx : ", idxx)
is_driver_flag, driver_index, weird_state_count, weird_state_flag = find_class.is_driver(
current_poses, orig_img)
cv2.putText(img, "Weird_State_Count : " + str(weird_state_count),
(0, 20), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0))
if weird_state_flag:
cv2.putText(img, 'State : ABNORMAL', (0, 50),
cv2.FONT_HERSHEY_COMPLEX, 1, (0, 0, 255))
else:
cv2.putText(img, "State : Driver_Found", (0, 50),
cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0))
# print("Driver_index :", driver_index)
# print("Driver_Flag : ", is_driver_flag)
# csv_dict['frame_number'].append(idxx)
# csv_dict['driver_index'].append(driver_index)
# csv_dict['is_driver_flag'].append(is_driver_flag)
# csv_dict['state'].append(state)
if track_ids == True: ##Track Poses
propagate_ids(previous_poses, current_poses)
previous_poses = current_poses
index_counter = 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))
cv2.putText(img, 'id: {}'.format(pose.id),
(pose.bbox[0], pose.bbox[1] - 16),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 255))
if is_driver_flag and index_counter == driver_index:
cv2.putText(img, 'DRIVER',
(pose.bbox[0] + 100, pose.bbox[1] - 16),
cv2.FONT_HERSHEY_COMPLEX, 1, (0, 0, 255))
index_counter += 1
tt = time.time()
fps = 1 / (tt - ttt)
print('fps=', fps)
ttt = time.time()
str_ = "FPS : %0.2f" % fps
cv2.putText(img, str_, (0, 100), cv2.FONT_HERSHEY_SIMPLEX, 1,
(0, 255, 0))
cv2.imshow('Lightweight Human Pose Estimation Python Demo', img)
# cv2_imshow(img)
cv2.imwrite('output/two_2/' + str(idxx) + '.png', img)
idxx += 1
key = cv2.waitKey(1)
if key == 27: # esc
return
df = pd.DataFrame(csv_dict)