def get_detect(camer_id):
global log_list
# print("[INFO] Confirming inputs...")
# mylog.addHandler(filehandler)
image_paths = []
batch_size = 1
count = 0
count_hat = 0
count_frock = 0
count_glove = 0
count_shoe = 0
raw_hat = 0
raw_frock = 0
raw_glove = 0
raw_shoe = 0
label_hat = '1'
label_frock = '1'
label_glove = '1'
label_shoe = '1'
faces_label = 1000
count_faces = 0
# last_faces = faces_label
video_reader = cv2.VideoCapture(camer_id)
video_reader.set(3, 640)
with ThreadPoolExecutor(10) as executor:
while (True):
# while(True):
ret, image = video_reader.read()
if ret == True:
image_paths += [image]
count += 1
# cv2.imshow('Image',image)
if (len(image_paths) == batch_size) or (ret == False
and len(image_paths) > 0):
for img in image_paths:
out = preson_detect(img)
img_h, img_w, img_ch = img.shape
for i in range(len(out)):
start = time.time()
x0, y0, x1, y1 = out[i, 3:7]
# print ("coordinate:",x0,y0,x1,y1)
start_x = int(img_w * x0)
start_y = int(img_h * y0)
end_x = int(img_w * x1)
end_y = int(img_h * y1)
if out[i, 2] < 0.8:
continue
if start_x < 30 or end_x > 580:
continue
w = end_x - start_x
h = end_y - start_y
images = img[start_y:end_y, start_x:end_x]
image_h, image_w, image_ch = images.shape
if image_h < norm_size or image_w < norm_size or image_h < 300 or image_w > 200:
continue
# print("image")
####head_start##
head_width = int(h * 0.25 + h * 0.05)
if (int(start_x + end_x - head_width) / 2 < start_x
or int((start_x + end_x + head_width) / 2) >
end_x):
continue
if int((start_x + end_x - head_width) /
2) > start_x + 0.1 * w:
image_head = img[
int(start_y - h * 0.05):int(start_y +
h * 0.25),
int((start_x + end_x - head_width) /
2):int((start_x + end_x + head_width) / 2)]
else:
image_head = img[int(start_y -
h * 0.05):int(start_y +
h * 0.25),
int(start_x +
0.1 * w):int(end_x - 0.1 * w)]
####head_end##
image_hand = img[int(start_y + h * 0.2):int(start_y +
h * 0.5),
(start_x - 15):(end_x + 20)]
image_shoe = img[int(start_y + h * 0.8):(end_y),
start_x:end_x]
if image_shoe.shape[0] == 0:
continue
# print("image_head",image_head)
if image_hand.shape[0] == 0:
continue
if image_hand.shape[1] == 0:
continue
if image_head.shape[0] == 0:
continue
image_body = img[int(start_y + w * 0.6):end_y,
start_x:end_x]
if image_body.shape[0] == 0:
continue
label_hat = predict_hat(image_head) # 0代表true
if raw_hat == int(label_hat):
count_hat += 1
else:
raw_hat = int(label_hat)
count_hat = 0
if label_hat == '1':
if img.shape[0] == 0 or img.shape[1] == 0:
continue
label_frock = predict_frock(image_body) # 0代表true
if raw_frock == int(label_frock):
count_frock += 1
else:
raw_frock = int(label_frock)
count_frock = 0
if label_frock == '0':
label_glove = predict_glove(image_hand)
if raw_glove == int(label_glove):
count_glove += 1
else:
raw_glove = int(label_glove)
count_glove = 0
label_shoe = predict_shoe(image_shoe)
# if int(label_shoe) == 0:
# label_shoe = predict_edge(image_shoe)
if raw_shoe == int(label_shoe):
count_shoe += 1
else:
raw_shoe = int(label_shoe)
count_shoe = 0
label_dic = {"label_hat":label_hat,"label_frock":label_frock,"label_glove":label_glove,"label_shoe":label_shoe,\
"count_hat":count_hat,"count_frock":count_frock, "count_glove":count_glove , "count_shoe":count_shoe
}
local_list = [start_x, start_y, end_x, end_y]
count_list = [
count_hat, count_frock, count_glove, count_shoe
]
drew_rectangle(img, label_dic, local_list, count_list)
if count_faces < 1:
faces_label = "unknown"
# myThread(label_dic,count_list,count,faces_label,img,camer_id+1).start()
executor.submit(myThread, label_dic, count_list, count,
faces_label, img, camer_id + 1)
# upload_log(label_dic,count_list,count,faces_label,img)
end = time.time()
print("=" * 50, (end - start) * 1000)
image_paths = []
# cv2.imshow("image", img)
ret, jpeg = cv2.imencode('.jpg', img)
yield (b'--frame\r\n'
b'application/octet-stream: image/jpeg\r\n\r\n' +
jpeg.tobytes() + b'\r\n\r\n')
def get_detect(id_video):
global control_color
dq = deque(maxlen=1)
t_read_video = threading.Thread(target=read_video, args=(dq, ))
# t_read_video = threading.Thread(target=read_video_vlc, args=(dq,))
t_put_data = threading.Thread(target=put_data)
t_read_video.start()
t_put_data.start()
# myout = save_video(video_reader, "./video.mp4", sz)
my_track_dict = {"0": None} #save the info of track_id
track_smooth_dict = {} #smooth the imshow
#创建追踪器
tracker = cv2.MultiTracker_create()
init_once = False
save_file = mk_dir()
num = 0 #有数据循环次数
success_num = 0 #有检测次数
t1 = time.time() #重新检测时间初始值
my_result = {} #存储缓冲数据
index_key = 0
lenth_boxs = 0
while True:
#avoid the memory error.
if len(my_track_dict) > 50:
my_track_dict = {} #save the info of track_id
if len(my_result) > 50:
my_result = {} #存储缓冲数据
# print(len(my_track_dict))
#read camera data
if dq:
img = dq.popleft()
print("=====================", img.shape)
else:
time.sleep(0.05)
continue
start_time = time.time() #开始计时,测试单贞照片处理时间
num += 1
if num % 100 == 1:
cv2.imwrite(save_file + "/_{}.jpg".format(num), img)
img_h, img_w, img_ch = img.shape
print(img.shape)
#2、防止裁剪或推理时把画的框裁剪上
show_image = img.copy()
frame = img.copy()
#the predict of person.
boxs, confidence, class_names = [], [], []
out = preson_detect(img)
#过滤规定区域内的人性框进行判断，transform the object detection data to input tracter
for i in range(len(out)):
#========my_setting==============
if out[i, 2] > 0.7:
# print(out[i])
left = int(out[i, 3] * img_w)
top = int(out[i, 4] * img_h)
p_w = int(out[i, 5] * img_w - out[i, 3] * img_w)
p_h = int(out[i, 6] * img_h - out[i, 4] * img_h)
right = left + p_w
bottom = top + p_h
#detect the person in setting area.
point1 = [int((left + right) / 2), bottom]
# my_index = inner_point(point1)
my_index = True
print("my_index", my_index)
if my_index:
boxs.append([left, top, p_w, p_h])
class_names.append("person")
confidence.append(out[i, 2])
#========my_setting==============
t2 = time.time()
detect_time = t2 - t1
control_time = 2 ##setting detect time， detect one time in m second
control_buttom = False
if detect_time > control_time:
control_buttom = True
t1 = time.time()
# if not init_once or control_time>5 or lenth_boxs!=len(boxs):
if not init_once or control_buttom:
# tracker = cv2.MultiTracker_create()
for one_box in boxs:
ok = tracker.add(cv2.TrackerMIL_create(), frame,
tuple(one_box))
init_once = True
index_key = max([int(i_key) for i_key in my_track_dict.keys()])
ok, boxes = tracker.update(frame)
print(ok, boxes)
for index, newbox in enumerate(boxes):
p1 = (int(newbox[0]), int(newbox[1]))
p2 = (int(newbox[0] + newbox[2]), int(newbox[1] + newbox[3]))
cv2.rectangle(frame, p1, p2, (200, 0, 0))
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(frame, str(index),
(int(newbox[0]), int(newbox[1] - 20)), font, 1.2,
(0, 0, 225), 2)
cv2.namedWindow("aidong_unicom", 0)
cv2.imshow('aidong_unicom', frame)
key = cv2.waitKey(33)
def get_detect(id_video):
global control_color
from collections import deque
dq = deque(maxlen=1)
t1 = threading.Thread(target=read_video, args=(dq,))
t1.start()
my_num = 0
while True:
start_time = time.time()
if dq:
img = dq.pop()
else:
cv2.waitKey(20)
continue
show_image = img.copy()
out = preson_detect(img)
img_h, img_w, img_ch = img.shape
for i in range(len(out)):
#人形框限定条件，根据置信度限定不符合要求的人形框。
if out[i, 2] < 0.7:
continue
#attentions
x0, y0, x1, y1 = out[i, 3:7]
start_x = int(img_w * x0)
start_y = int(img_h * y0)
end_x = int(img_w * x1)
end_y = int(img_h * y1)
point1 = [int((start_x+end_x)/2), end_y]
my_index = inner_point(point1)
if not my_index:
continue
clip_images = img[start_y:end_y, start_x:end_x]
h, w, c = clip_images.shape
#根据图像大小限定不符合要求的人形框。
if h > 96 and w > 48:
#3、保存图片,修改保存名称
my_num += 1
#save_name = os.path.join(save_dir, "v1_person{}.jpg".format(my_num))
#cv2.imwrite(save_name, clip_images)
label_dict = muti_attr(clip_images)
print(label_dict)
# i_count=0
# font=cv2.FONT_HERSHEY_COMPLEX
if "coat" in label_dict.keys():
#4、在原始图像上画矩形。
if label_dict["coat"] == "Yes":
cv2.rectangle(show_image, (start_x, start_y), (end_x, end_y), (0, 255, 0), 2)
else:
cv2.rectangle(show_image, (start_x, start_y), (end_x, end_y), (0, 0, 255), 2)
#5、在原始图像加标注。
if control_color:
show_image = draw_person_attr(show_image, label_dict, end_x, start_y)
# for key in label_dict:
# text=key+":"+str(label_dict[key])
# if label_dict[key] == "Yes":
# cv2.putText(show_image,text,(end_x,start_y+i_count),font,0.7,(0,255,0),2)
# else:
# cv2.putText(show_image,text,(end_x,start_y+i_count),font,0.7,(0,0,255),2)
# i_count+=20
draw_muti(show_image)
end_time = time.time()
mytime = (end_time-start_time)*1000
print("================", show_image.shape,mytime)
ret2, jpeg = cv2.imencode('.jpg', show_image)
yield (b'--frame\r\n'
b'application/octet-stream: image/jpeg\r\n\r\n' + jpeg.tobytes() + b'\r\n\r\n')
# cv2.namedWindow("image{}".format("benchi"), cv2.WINDOW_NORMAL)
# cv2.imshow("image{}".format("benchi"), show_image)
# key = cv2.waitKey(1)
# if key == 27:
# break
cv2.destroyAllWindows()
def get_detect(id_video):
global control_color
dq = deque(maxlen=1)
t_read_video = threading.Thread(target=read_video, args=(dq, ))
t_put_data = threading.Thread(target=put_data)
t_read_video.start()
t_put_data.start()
# myout = save_video(video_reader, "./video.mp4", sz)
max_cosine_distance = 0.3
nn_budget = None
nms_max_overlap = 1.0
counter = []
my_track_dict = {} #save the info of track_id
track_smooth_dict = {} #smooth the imshow
pts = [deque(maxlen=30) for _ in range(9999)]
#deep_sort
model_filename = 'model_data/market1501.pb'
encoder = gdet.create_box_encoder(model_filename, batch_size=1)
metric = nn_matching.NearestNeighborDistanceMetric("cosine",
max_cosine_distance,
nn_budget)
tracker = Tracker(metric)
list_file = open('detection_rslt.txt', 'w')
save_file = mk_dir()
num = 0
t1 = time.time()
while True:
#avoid the memory error.
if len(my_track_dict) > 50:
my_track_dict = {}
print(len(my_track_dict))
if dq:
img = dq.pop()
else:
time.sleep(0.05)
continue
start_time = time.time()
num += 1
if num % 500 == 1:
cv2.imwrite(save_file + "/_{}.jpg".format(num), img)
img_h, img_w, img_ch = img.shape
print(img.shape)
#2、防止裁剪或推理时把画的框裁剪上
show_image = img.copy()
frame = img.copy()
#the predict of person.
boxs, confidence, class_names = [], [], []
out = preson_detect(img)
#transform the object detection data to input tracter
for i in range(len(out)):
#========my_setting==============
if out[i, 2] > 0.7:
# print(out[i])
left = int(out[i, 3] * img_w)
top = int(out[i, 4] * img_h)
p_w = int(out[i, 5] * img_w - out[i, 3] * img_w)
p_h = int(out[i, 6] * img_h - out[i, 4] * img_h)
right = left + p_w
bottom = top + p_h
#detect the person in setting area.
point1 = [int((left + right) / 2), bottom]
my_index = inner_point(point1)
if my_index:
boxs.append([left, top, p_w, p_h])
class_names.append("person")
confidence.append(out[i, 2])
#start use the tracker
features = encoder(frame, boxs)
# score to 1.0 here.
detections = [
Detection(bbox, 1.0, feature)
for bbox, feature in zip(boxs, features)
]
# Run non-maxima suppression.
boxes = np.array([d.tlwh for d in detections])
scores = np.array([d.confidence for d in detections])
indices = preprocessing.non_max_suppression(boxes, nms_max_overlap,
scores)
detections = [detections[i] for i in indices]
# Call the tracker
tracker.predict()
tracker.update(detections)
i = int(0)
indexIDs = []
#setting detect time
t2 = time.time()
detect_time = t2 - t1
#========my_setting==============
control_time = 0.2 #detect one time in m second
if detect_time > control_time:
t1 = time.time()
for det, track in zip(detections, tracker.tracks):
# if not track.is_confirmed() or track.time_since_update > 1:
if not track.is_confirmed() or track.time_since_update > 1:
continue
#print(track.track_id)
#draw the boxs of object detection.
pbox = det.to_tlbr()
#cv2.rectangle(frame,(int(pbox[0]), int(pbox[1])), (int(pbox[2]), int(pbox[3])),(255,255,255), 2)
my_key = str(int(track.track_id))
#========my_setting==============
#if my_key increase or time lt 3s, will be re_detection.
if my_key not in my_track_dict.keys(
) or detect_time > control_time:
# print(my_key)
# print(my_track_dict.keys())
#the code of processing the person box.
label_dict = get_labels(img, pbox)
print("**" * 20, label_dict)
if type(label_dict) == type(None):
continue
if "coat" not in label_dict.keys():
continue
my_track_dict[my_key] = label_dict
# draw the attr of person.
frame = draw_person_attr(frame, my_track_dict[my_key], pbox,
control_color)
indexIDs.append(int(track.track_id))
counter.append(int(track.track_id))
bbox = track.to_tlbr()
color = [int(c) for c in COLORS[indexIDs[i] % len(COLORS)]]
#define the color of rectangle.
if my_track_dict[my_key]["coat"] == "Yes":
color_rect = (0, 255, 0)
else:
color_rect = (0, 0, 255)
#center_loc = [int((bbox[0]+bbox[2])/2), int((bbox[1]+bbox[3])/2)]
if my_key not in track_smooth_dict.keys():
print("---------------------------------------------------->")
cv2.rectangle(frame, (int(bbox[0]), int(bbox[1])),
(int(bbox[2]), int(bbox[3])), (color_rect), 3)
track_smooth_dict[my_key] = bbox
else:
fbox = track_smooth_dict[my_key]
a = int((bbox[0] + fbox[0]) / 2)
b = int((bbox[1] + fbox[1]) / 2)
c = int((bbox[2] + fbox[2]) / 2)
d = int((bbox[3] + fbox[3]) / 2)
cv2.rectangle(frame, (a, b), (c, d), (color_rect), 3)
track_smooth_dict[my_key] = bbox
#draw the boxs of track.
#cv2.rectangle(frame, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])),(color), 3)
if True:
cv2.putText(frame, str(track.track_id),
(int(bbox[0]), int(bbox[1] - 50)), 0, 5e-3 * 150,
(color), 2)
if len(class_names) > 0:
class_name = class_names[0]
cv2.putText(frame, str(class_names[0]),
(int(bbox[0]), int(bbox[1] - 20)), 0,
5e-3 * 150, (color), 2)
i += 1
# 控制上传频率
if num % 200 == 1:
my_result = my_track_dict[my_key]
pic_name = str(int(time.time())) + "_" + my_key
# put_data(my_key, my_result, frame)
q_put_img.append([pic_name, my_result, frame])
count = len(set(counter))
#draw the gurdline.
draw_muti(frame)
# cv2.putText(frame, "Total Pedestrian Counter: "+str(count),(int(20), int(120)),0, 5e-3 * 200, (0,255,0),2)
# cv2.putText(frame, "Current Pedestrian Counter: "+str(i),(int(20), int(80)),0, 5e-3 * 200, (0,255,0),2)
end_time = time.time()
my_one_time = (end_time - start_time) * 1000
print("====={}=====".format(num), my_one_time)
frame = cv2.resize(frame, (640, 360))
ret2, jpeg = cv2.imencode('.jpg', frame)
yield (b'--frame\r\n'
b'application/octet-stream: image/jpeg\r\n\r\n' +
jpeg.tobytes() + b'\r\n\r\n')
def get_detect(id_video):
global control_color
dq = deque(maxlen=1)
thread_start(dq)
fbox = []
bbox = []
person_dit = {}
threshold_iou = 0.8
my_track_dict = {}
save_file = mk_dir()
num = 0
t1 = time.time()
while True:
#avoid the memory error.
if len(my_track_dict) > 50:
my_track_dict = {}
if dq:
img = dq.pop()
else:
time.sleep(0.05)
continue
start_time = time.time()
num += 1
if num % 5000 == 1:
cv2.imwrite(save_file + "/_{}.jpg".format(num), img)
img_h, img_w, img_ch = img.shape
print(img.shape)
#2、防止裁剪或推理时把画的框裁剪上
show_image = img.copy()
frame = img.copy()
#the predict of person.
out = preson_detect(img)
# print("------------->", out)
boxes = []
#transform the object detection data to input tracter
for i in range(len(out)):
#========my_setting==============
if out[i, 2] > 0.7:
# print(out[i])
left = int(out[i, 3] * img_w)
top = int(out[i, 4] * img_h)
p_w = int(out[i, 5] * img_w - out[i, 3] * img_w)
p_h = int(out[i, 6] * img_h - out[i, 4] * img_h)
right = left + p_w
bottom = top + p_h
#detect the person in setting area.
point1 = [int((left + right) / 2), bottom]
my_index = inner_point(point1)
# if my_index:
#========my_setting==============
if True:
boxes.append([left, top, p_w, p_h])
print(boxes)
if not boxes:
# time.sleep(0.02)
continue
#tracker algorithm 追踪算法
if not fbox:
fbox = boxes
init_len = len(fbox)
for i_d in range(1, init_len + 1):
person_dit[str(i_d)] = fbox[i_d - 1]
continue
else:
bbox = boxes
print("---------->fbox", fbox)
print("---------->bbox", bbox)
person_new = person_dit.copy()
#删除上一贞出现这贞消失的数据，并更新一直存在的坐标
for key in person_dit:
for one in bbox:
my_iou = iou_cal(person_new[key], one)
if my_iou > threshold_iou:
person_new[key] = new_loc(person_new[key], one)
break
else:
del person_new[key]
#添加这贞新增而上一贞不存在的数据
for one in bbox:
for key in person_dit:
my_iou = iou_cal(person_dit[key], one)
if my_iou > threshold_iou:
break
else:
init_len += 1
person_new[str(init_len)] = one #等于the value of this frame
fbox = bbox
if not person_dit:
continue
t2 = time.time()
detect_time = t2 - t1
#========my_setting==============
control_time = 1 #detect one time in m second
if detect_time > control_time:
t1 = time.time()
indexIDs = []
for my_key in person_new:
#if my_key increase or time lt 3s, will be re_detection.
if my_key not in my_track_dict.keys(
) or detect_time > control_time:
# print(my_key)
# print(my_track_dict.keys())
#the code of processing the person box.
label_dict = get_labels(img, person_new[my_key])
print("**" * 20, label_dict)
if type(label_dict) == type(None):
continue
if "coat" not in label_dict.keys():
continue
my_track_dict[my_key] = label_dict
# draw the attr of person.
indexIDs.append(int(my_key))
frame = draw_person_attr(frame, my_track_dict[my_key],
person_new[my_key], control_color)
# color = [int(c) for c in COLORS[indexIDs[i] % len(COLORS)]]
#define the color of rectangle.
if my_track_dict[my_key]["coat"] == "Yes":
color_rect = (0, 255, 0)
else:
color_rect = (0, 0, 255)
cv2.rectangle(
frame,
(int(person_new[my_key][0]), int(person_new[my_key][1])),
(int(person_new[my_key][2]), int(person_new[my_key][3])),
(color_rect), 3)
#draw the boxs of track.
#cv2.rectangle(frame, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])),(color), 3)
if True:
cv2.putText(frame, str(my_key), (int(
person_new[my_key][0]), int(person_new[my_key][1] - 50)),
0, 5e-3 * 150, (225, 0, 0), 2)
class_names = "person"
cv2.putText(frame, str(class_names), (int(
person_new[my_key][0]), int(person_new[my_key][1] - 20)),
0, 5e-3 * 150, (225, 0, 0), 2)
i += 1
cv2.imshow("img", frame)
key = cv2.waitKey(33)
if key == 27:
cv2.destroyAllWindows()
video_reader.release()
break
def get_detect(id_video):
global control_color
dq = deque(maxlen=1)
t_read_video = threading.Thread(target=read_video, args=(dq,))
# t_read_video = threading.Thread(target=read_video_vlc, args=(dq,))
t_put_data = threading.Thread(target=put_data)
t_read_video.start()
t_put_data.start()
# myout = save_video(video_reader, "./video.mp4", sz)
max_cosine_distance = 0.3
nn_budget = None
nms_max_overlap = 1.0
counter = []
my_track_dict = {} #save the info of track_id
track_smooth_dict = {} #smooth the imshow
pts = [deque(maxlen=30) for _ in range(9999)]
#deep_sort
model_filename = 'model_data/market1501.pb'
encoder = gdet.create_box_encoder(model_filename,batch_size=1)
metric = nn_matching.NearestNeighborDistanceMetric("cosine", max_cosine_distance, nn_budget)
tracker = Tracker(metric)
list_file = open('detection_rslt.txt', 'w')
save_file = mk_dir()
num = 0 #有数据循环次数
success_num = 0 #有检测次数
t1 = time.time() #重新检测时间初始值
my_result = {} #存储缓冲数据
while True:
#avoid the memory error.
if len(my_track_dict)>50:
my_track_dict = {} #save the info of track_id
if len(my_result) > 50:
my_result = {} #存储缓冲数据
# print(len(my_track_dict))
#read camera data
if dq:
img = dq.popleft()
print("=====================", img.shape)
else:
time.sleep(0.05)
continue
start_time = time.time() #开始计时,测试单贞照片处理时间
num += 1
if num % 100 == 1:
cv2.imwrite(save_file+"/_{}.jpg".format(num), img)
img_h, img_w, img_ch = img.shape
print(img.shape)
#2、防止裁剪或推理时把画的框裁剪上
show_image = img.copy()
frame = img.copy()
#the predict of person.
boxs, confidence, class_names = [], [], []
out = preson_detect(img)
#过滤规定区域内的人性框进行判断，transform the object detection data to input tracter
for i in range(len(out)):
#========my_setting==============
if out[i, 2] > 0.7:
# print(out[i])
left = int(out[i, 3]*img_w)
top = int(out[i, 4]*img_h)
p_w = int(out[i, 5]*img_w-out[i, 3]*img_w)
p_h = int(out[i, 6]*img_h-out[i, 4]*img_h)
right = left + p_w
bottom = top + p_h
#detect the person in setting area.
point1 = [int((left+right)/2), bottom]
my_index = inner_point(point1)
print("my_index", my_index)
if my_index:
boxs.append([left, top, p_w, p_h])
class_names.append("person")
confidence.append(out[i, 2])
#start use the tracker
features = encoder(frame, boxs)
# score to 1.0 here.
detections = [Detection(bbox, 1.0, feature) for bbox, feature in zip(boxs, features)]
# Run non-maxima suppression.
boxes = np.array([d.tlwh for d in detections])
scores = np.array([d.confidence for d in detections])
indices = preprocessing.non_max_suppression(boxes, nms_max_overlap, scores)
detections = [detections[i] for i in indices]
# Call the tracker
tracker.predict()
tracker.update(detections)
i = int(0)
indexIDs = []
#========my_setting==============
t2 = time.time()
detect_time = t2 - t1
control_time = 0.2 ##setting detect time， detect one time in m second
if detect_time > control_time:
t1 = time.time()
#对追踪结果进行处理，和对单个人性框进行识别
for det, track in zip(detections, tracker.tracks):
if not track.is_confirmed() or track.time_since_update > 1:
continue
pbox = det.to_tlbr() #draw the boxs of object detection. #pbox为人单个人形框
my_key = str(int(track.track_id))
#========my_setting==============
#if my_key increase or time lt xxs, will be re_detection.
if my_key not in my_track_dict.keys() or detect_time>control_time:
# print(my_key)
# print(my_track_dict.keys())
#the code of processing the person box.
label_dict = get_labels(img, pbox) #pbox为人单个人形框
# print("============================================", label_dict)
#label_dict 可能为None
if type(label_dict) == type(None):
continue
if "coat" not in label_dict.keys():
continue
my_track_dict[my_key] = label_dict
frame = draw_person_attr(frame, my_track_dict[my_key], pbox, control_color) # draw the attr of person.
indexIDs.append(int(track.track_id))
counter.append(int(track.track_id))
bbox = track.to_tlbr()
#有负值，注意----------------------
bbox = [0 if i < 0 else int(i) for i in bbox]
color = [int(c) for c in COLORS[indexIDs[i] % len(COLORS)]]
# define the color of rectangle.
if my_track_dict[my_key]["coat"] == 1:
color_rect = (0, 255, 0)
else:
color_rect = (0, 0, 255)
# smooth the rectangle. 平滑矩形框
#center_loc = [int((bbox[0]+bbox[2])/2), int((bbox[1]+bbox[3])/2)]
if my_key not in track_smooth_dict.keys():
cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]),(color_rect), 3)
track_smooth_dict[my_key] = bbox
else:
fbox = track_smooth_dict[my_key]
a = int((bbox[0]+fbox[0])/2)
b = int((bbox[1]+fbox[1])/2)
c = int((bbox[2]+fbox[2])/2)
d = int((bbox[3]+fbox[3])/2)
cv2.rectangle(frame, (a, b), (c, d),(color_rect), 3)
track_smooth_dict[my_key] = bbox
#draw the boxs of track.
#cv2.rectangle(frame, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])),(color), 3)
#添加文字显示
if True:
cv2.putText(frame,str(track.track_id),(bbox[0], bbox[1] -50),0, 5e-3 * 150, (color),2)
if len(class_names) > 0:
class_name = class_names[0]
cv2.putText(frame, str(class_names[0]),(bbox[0], bbox[1] -20),0, 5e-3 * 150, (color),2)
i += 1
# print("my_track_dict[my_key]", my_track_dict[my_key])
if my_key not in my_result:
#不要直接等于，会出错，深浅拷贝问题
my_result[my_key] = my_track_dict[my_key].copy()
my_result[my_key]["mysum"] = 0
# print("++++++init++++++++++",my_result[my_key])
else:
for key1 in my_track_dict[my_key]:
if key1 in my_result[my_key]:
if key1 == "face":
if my_track_dict[my_key]["face"] != "Unknown":
my_result[my_key]["face"] = my_track_dict[my_key]["face"]
else:
my_result[my_key][key1] = my_result[my_key][key1] + my_track_dict[my_key][key1]
else:
my_result[my_key][key1] = my_track_dict[my_key][key1]
my_result[my_key]["mysum"] = my_result[my_key]["mysum"] + 1
# print(my_key, "-------->", my_result[my_key])
if my_result[my_key]["mysum"] > 50:
# print("-------->", my_result[my_key])
my_cum_result = {}
for key2 in my_result[my_key]:
if key2 == "face":
my_cum_result[key2] = my_result[my_key][key2]
else:
if my_result[my_key][key2]/my_result[my_key]["mysum"] < 0.3:
my_cum_result[key2] = 0
else:
my_cum_result[key2] = 1
del my_cum_result["mysum"] #删除“mysum”属性再上传
del my_result[my_key] #减少内存负担，上传的人员要删除
if "coat" in my_cum_result and "hat" in my_cum_result and "gloves" in my_cum_result and "shoes" in my_cum_result:
pic_name = str(id_video) + "_" + str(int(time.time())) + "_" + my_key
# put_data(my_key, my_result, frame)
# print("--------put_data-------->", pic_name, bbox)
q_put_img.append([pic_name, my_cum_result, show_image[bbox[1]:bbox[3], bbox[0]:bbox[2]]])
# my_result[my_key] = {"hat":0, "coat":0, "gloves":0,"shoes":0, "mysum":0}
count = len(set(counter))
#draw the gurdline.画警戒线
draw_muti(frame)
# cv2.putText(frame, "Total Pedestrian Counter: "+str(count),(int(20), int(120)),0, 5e-3 * 200, (0,255,0),2)
# cv2.putText(frame, "Current Pedestrian Counter: "+str(i),(int(20), int(80)),0, 5e-3 * 200, (0,255,0),2)
end_time = time.time() #结束计时,测试单贞照片处理时间
my_one_time = (end_time - start_time) * 1000
print("====={}=====".format(num), my_one_time)
# frame = cv2.resize(frame, (640, 360))
# ret2, jpeg = cv2.imencode('.jpg', frame)
# yield (b'--frame\r\n'
# b'application/octet-stream: image/jpeg\r\n\r\n' + jpeg.tobytes() + b'\r\n\r\n')
cv2.namedWindow("aidong_unicom", 0)
cv2.imshow('aidong_unicom', frame)
key = cv2.waitKey(33)
if key == 27:
myout.release()
video_reader.release()
cv2.destroyAllWindows()
break
def get_detect(id_video):
from collections import deque
dq = deque(maxlen=1)
t1 = threading.Thread(target=read_video, args=(dq, ))
t1.start()
# myout = save_video(video_reader, "./video.mp4", sz)
max_cosine_distance = 0.3
nn_budget = None
nms_max_overlap = 1.0
counter = []
my_track_dict = {} #save the info of track_id
track_smooth_dict = {} #smooth the imshow
pts = [deque(maxlen=30) for _ in range(9999)]
#deep_sort
model_filename = 'model_data/market1501.pb'
encoder = gdet.create_box_encoder(model_filename, batch_size=1)
metric = nn_matching.NearestNeighborDistanceMetric("cosine",
max_cosine_distance,
nn_budget)
tracker = Tracker(metric)
list_file = open('detection_rslt.txt', 'w')
my_num = 0
num = 0
t1 = time.time()
fps = 0
fps1 = time.time()
while True:
if dq:
img = dq.pop()
else:
key = cv2.waitKey(20)
continue
#TEST FPS
fps2 = time.time()
fps += 1
if fps2 - fps1 > 1:
print(fps)
fps = 0
fps1 = time.time()
start_time = time.time()
num += 1
#frame = cv2.imread(filename)
#1、读取中文路径
#img = cv2.imdecode(np.fromfile(filename,dtype=np.uint8), cv2.IMREAD_COLOR)
img_h, img_w, img_ch = img.shape
print(img.shape)
#2、防止裁剪或推理时把画的框裁剪上
show_image = img.copy()
frame = img.copy()
#the predict of person.
boxs, confidence, class_names = [], [], []
out = preson_detect(img)
#transform the object detection data to input tracter
for i in range(len(out)):
#========my_setting==============
if out[i, 2] > 0.7:
# print(out[i])
left = int(out[i, 3] * img_w)
top = int(out[i, 4] * img_h)
p_w = int(out[i, 5] * img_w - out[i, 3] * img_w)
p_h = int(out[i, 6] * img_h - out[i, 4] * img_h)
right = left + p_w
bottom = top + p_h
#detect the person in setting area.
point1 = [int((left + right) / 2), bottom]
my_index = inner_point(point1)
if my_index:
boxs.append([left, top, p_w, p_h])
class_names.append("person")
confidence.append(out[i, 2])
#start use the tracker
features = encoder(frame, boxs)
# score to 1.0 here).
detections = [
Detection(bbox, 1.0, feature)
for bbox, feature in zip(boxs, features)
]
# Run non-maxima suppression.
boxes = np.array([d.tlwh for d in detections])
scores = np.array([d.confidence for d in detections])
indices = preprocessing.non_max_suppression(boxes, nms_max_overlap,
scores)
detections = [detections[i] for i in indices]
# Call the tracker
tracker.predict()
tracker.update(detections)
i = int(0)
indexIDs = []
#setting detect time
t2 = time.time()
detect_time = t2 - t1
#========my_setting==============
control = 1 #detect one time in 3 second
if detect_time > control:
t1 = time.time()
for det, track in zip(detections, tracker.tracks):
# if not track.is_confirmed() or track.time_since_update > 1:
if not track.is_confirmed() or track.time_since_update > 1:
continue
#print(track.track_id)
#draw the boxs of object detection.
pbox = det.to_tlbr()
#cv2.rectangle(frame,(int(pbox[0]), int(pbox[1])), (int(pbox[2]), int(pbox[3])),(255,255,255), 2)
my_key = str(int(track.track_id))
#========my_setting==============
#if my_key increase or time lt 3s, will be re_detection.
if my_key not in my_track_dict.keys() or detect_time > control:
print(my_key)
print(my_track_dict.keys())
#the code of processing the person box.
label_dict = person_detect(img, pbox)
if not label_dict:
continue
my_track_dict[my_key] = label_dict
frame = draw_person_attr(frame, my_track_dict[my_key], pbox)
indexIDs.append(int(track.track_id))
counter.append(int(track.track_id))
bbox = track.to_tlbr()
color = [int(c) for c in COLORS[indexIDs[i] % len(COLORS)]]
#center_loc = [int((bbox[0]+bbox[2])/2), int((bbox[1]+bbox[3])/2)]
if my_key not in track_smooth_dict.keys():
cv2.rectangle(frame, (int(bbox[0]), int(bbox[1])),
(int(bbox[2]), int(bbox[3])), (color), 3)
track_smooth_dict[my_key] = bbox
else:
fbox = track_smooth_dict[my_key]
a = int((bbox[0] + fbox[0]) / 2)
b = int((bbox[1] + fbox[1]) / 2)
c = int((bbox[2] + fbox[2]) / 2)
d = int((bbox[3] + fbox[3]) / 2)
cv2.rectangle(frame, (a, b), (c, d), (color), 3)
track_smooth_dict[my_key] = bbox
#draw the boxs of track.
#cv2.rectangle(frame, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])),(color), 3)
cv2.putText(frame, str(track.track_id),
(int(bbox[0]), int(bbox[1] - 50)), 0, 5e-3 * 150,
(color), 2)
if len(class_names) > 0:
class_name = class_names[0]
cv2.putText(frame, str(class_names[0]),
(int(bbox[0]), int(bbox[1] - 20)), 0, 5e-3 * 150,
(color), 2)
i += 1
count = len(set(counter))
#draw the gurdline.
draw_muti(frame)
cv2.putText(frame, "Total Pedestrian Counter: " + str(count),
(int(20), int(120)), 0, 5e-3 * 200, (0, 255, 0), 2)
cv2.putText(frame, "Current Pedestrian Counter: " + str(i),
(int(20), int(80)), 0, 5e-3 * 200, (0, 255, 0), 2)
#cv2.putText(frame, "FPS: %f"%(fps),(int(20), int(40)),0, 5e-3 * 200, (0,255,0),3)
# cv2.namedWindow("YOLO4_Deep_SORT", 0)
#cv2.resizeWindow('YOLO4_Deep_SORT', 640, 480)
# cv2.imshow('YOLO4_Deep_SORT', frame)
# myout.write(frame)
# frame = cv2.resize(frame, (640, 360))
ret2, jpeg = cv2.imencode('.jpg', frame)
yield (b'--frame\r\n'
b'application/octet-stream: image/jpeg\r\n\r\n' +
jpeg.tobytes() + b'\r\n\r\n')
end_time = time.time()
my_one_time = (end_time - start_time) * 1000
print("====={}=====".format(num), my_one_time)
start_time = time.time()
num += 1
#frame = cv2.imread(filename)
#1、读取中文路径
#img = cv2.imdecode(np.fromfile(filename,dtype=np.uint8), cv2.IMREAD_COLOR)
img_h, img_w, img_ch = img.shape
print(img.shape)
#2、防止裁剪或推理时把画的框裁剪上
show_image = img.copy()
frame = img.copy()
#the predict of person.
boxs, confidence, class_names = [], [], []
out = preson_detect(img)
#transform the object detection data to input tracter
for i in range(len(out)):
#========my_setting==============
if out[i, 2] > 0.7:
# print(out[i])
left = int(out[i, 3]*img_w)
top = int(out[i, 4]*img_h)
p_w = int(out[i, 5]*img_w-out[i, 3]*img_w)
p_h = int(out[i, 6]*img_h-out[i, 4]*img_h)
right = left + p_w
bottom = top + p_h
#detect the person in setting area.
def get_detect(id_video):
global control_color
dq = deque(maxlen=1)
t_read_video = threading.Thread(target=read_video, args=(dq,))
# t_read_video = threading.Thread(target=read_video_vlc, args=(dq,))
t_put_data = threading.Thread(target=put_data)
t_read_video.start()
t_put_data.start()
# myout = save_video(video_reader, "./video.mp4", sz)
my_track_dict = {} #save the info of track_id
track_smooth_dict = {} #smooth the imshow
save_file = mk_dir()
num = 0 #有数据循环次数
success_num = 0 #有检测次数
t1 = time.time() #重新检测时间初始值
my_result = {} #存储缓冲数据
fbox = []
bbox = []
person_dit = {}
while True:
#avoid the memory error.
if len(my_track_dict)>50:
my_track_dict = {} #save the info of track_id
if len(my_result) > 50:
my_result = {} #存储缓冲数据
# print(len(my_track_dict))
#read camera data
if dq:
img = dq.popleft()
print("=====================", img.shape)
else:
time.sleep(0.05)
continue
start_time = time.time() #开始计时,测试单贞照片处理时间
num += 1
if num % 1000 == 1:
cv2.imwrite(save_file+"/_{}.jpg".format(num), img)
img_h, img_w, img_ch = img.shape
print(img.shape)
#2、防止裁剪或推理时把画的框裁剪上
show_image = img.copy()
frame = img.copy()
#the predict of person.
boxes = []
out = preson_detect(img)
#print(out)
#过滤规定区域内的人性框进行判断，transform the object detection data to input tracter
for i in range(len(out)):
#========my_setting==============
if out[i, 2] > 0.7:
#print("------------------>===================================>", out)
# print(out[i])
left = int(out[i, 3]*img_w)
top = int(out[i, 4]*img_h)
right = int(out[i, 5]*img_w)
bottom = int(out[i, 6]*img_h)
#detect the person in setting area.
point1 = [int((left+right)/2), bottom]
# my_index = inner_point(point1)
my_index = True
print("my_index", my_index)
if my_index:
boxes.append([left, top, right, bottom])
if boxes:
#跟踪算法
if not fbox:
fbox = boxes
init_len = len(fbox)
for i_d in range(0, init_len):
person_dit[str(i_d)] = fbox[i_d][-4:]
continue
else:
bbox = boxes
#print("------------------>===================================>", boxes)
# print("------------------>===================================>", 2)
del_list = []
for key_a in person_dit:
for one_a in bbox:
my_flag = edn_distance(person_dit[key_a], one_a)
if my_flag:
person_dit[key_a] = new_loc(person_dit[key_a], one_a)
# person_dit[key_a] = one_a
break
else:
del_list.append(key_a)
for key_b in del_list:
del person_dit[key_b]
for one_c in bbox:
for key_c in person_dit:
my_flag = edn_distance(person_dit[key_c], one_c)
if my_flag:
break
else:
init_len += 1
person_dit[str(init_len)] = one_c[-4:]
fbox = bbox
#print("---------------------------------------------------------->===================================>", 3)
#检测条件设置
t2 = time.time()
detect_time = t2 - t1
control_time = 1 ##setting detect time， detect one time in m second
if detect_time > control_time:
t1 = time.time()
#属性检测
for my_key in person_dit:
#加入控制条件
# if my_key not in my_track_dict.keys() or detect_time>control_time:
if True:
#the code of processing the person box.
label_dict = get_labels(frame, person_dit[my_key]) #pbox为人单个人形框
# print("============================================", label_dict)
#label_dict 可能为None
if type(label_dict) == type(None):
continue
if "coat" not in label_dict.keys():
continue
my_track_dict[my_key] = label_dict
#draw the boxs of track.
new_box = person_dit[my_key]
p1 = (new_box[0], new_box[1])
p2 = (new_box[2], new_box[3])
cv2.rectangle(show_image, p1, p2, (255,0,0), 2, 1)
#添加文字显示
cv2.putText(show_image, "person:"+my_key, (new_box[0], new_box[1]), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50,170,50),2)
show_image = draw_person_attr(show_image, my_track_dict[my_key], new_box, control_color) # draw the attr of person.
#结果上传
if my_key not in my_result:
#不要直接等于，会出错，深浅拷贝问题
my_result[my_key] = my_track_dict[my_key].copy()
my_result[my_key]["mysum"] = 0
# print("++++++init++++++++++",my_result[my_key])
else:
for key1 in my_track_dict[my_key]:
if key1 in my_result[my_key]:
if key1 == "face":
if my_track_dict[my_key]["face"] != "Unknown":
my_result[my_key]["face"] = my_track_dict[my_key]["face"]
else:
my_result[my_key][key1] = my_result[my_key][key1] + my_track_dict[my_key][key1]
else:
my_result[my_key][key1] = my_track_dict[my_key][key1]
my_result[my_key]["mysum"] = my_result[my_key]["mysum"] + 1
# print(my_key, "-------->", my_result[my_key])
if my_result[my_key]["mysum"] > 20:
# print("-------->", my_result[my_key])
my_cum_result = {}
for key2 in my_result[my_key]:
if key2 == "face":
my_cum_result[key2] = my_result[my_key][key2]
else:
if my_result[my_key][key2]/my_result[my_key]["mysum"] < 0.3:
my_cum_result[key2] = 0
else:
my_cum_result[key2] = 1
del my_cum_result["mysum"] #删除“mysum”属性再上传
del my_result[my_key] #减少内存负担，上传的人员要删除
if "coat" in my_cum_result and "hat" in my_cum_result and "gloves" in my_cum_result and "shoes" in my_cum_result:
pic_name = str(id_video) + "_" + str(int(time.time())) + "_" + my_key
# put_data(my_key, my_result, frame)
# print("--------put_data-------->", pic_name, bbox)
loction = person_dit[my_key]
q_put_img.append([pic_name, my_cum_result, show_image[loction[1]:loction[3], loction[0]:loction[2]]])
# my_result[my_key] = {"hat":0, "coat":0, "gloves":0,"shoes":0, "mysum":0}
#draw the gurdline.画警戒线
draw_muti(show_image)
end_time = time.time() #结束计时,测试单贞照片处理时间
my_one_time = (end_time - start_time) * 1000
print("====={}=====".format(num), my_one_time)
#print("-------------->", num, my_result)
#cv2.namedWindow("aidong_unicom", 0)
#cv2.imshow('aidong_unicom', show_image)
#key = cv2.waitKey(1)
show_image = cv2.resize(show_image, (640, 360))
ret2, jpeg = cv2.imencode('.jpg', show_image)
yield (b'--frame\r\n'
b'application/octet-stream: image/jpeg\r\n\r\n' + jpeg.tobytes() + b'\r\n\r\n')
else:
#draw the gurdline.画警戒线
draw_muti(show_image)
end_time = time.time() #结束计时,测试单贞照片处理时间
my_one_time = (end_time - start_time) * 1000
print("====={}=====".format(num), my_one_time)
print("-------------->", num, person_dit)
#cv2.namedWindow("aidong_unicom", 0)
#cv2.imshow('aidong_unicom', show_image)
#key = cv2.waitKey(1)
show_image = cv2.resize(show_image, (640, 360))
ret2, jpeg = cv2.imencode('.jpg', show_image)
yield (b'--frame\r\n'
b'application/octet-stream: image/jpeg\r\n\r\n' + jpeg.tobytes() + b'\r\n\r\n')
def get_detect(rtsp_addr, camerId):
global control_color
dq = deque(maxlen=5)
t_read_video = threading.Thread(target=read_video, args=(dq, rtsp_addr))
#t_read_video = threading.Thread(target=read_video_vlc, args=(dq, rtsp_addr))
t_put_data = threading.Thread(target=put_data, args=(camerId, ))
t_read_video.start()
t_put_data.start()
# myout = save_video(video_reader, "./video.mp4", sz)
my_track_dict = {} #save the info of track_id
save_file = mk_dir()
num = 0 #有数据循环次数
t1 = time.time() #重新检测时间初始值
my_result = {} #存储缓冲数据
mystage = 1 #默认都检测
fbox = []
bbox = []
person_dit = {}
#加载电子围栏
try:
points2 = get_fence(camerId)
#print("-------"*100, points2)
points2 = [[int(float(pt[0]) * 1280),
int(float(pt[1]) * 720)] for pt in points2]
#print("-------"*100, points2)
except:
points2 = [[0, 0], [0, 10], [10, 10], [10, 0]]
print("no gardline!please create new line.")
while True:
# avoid the memory error.
if len(my_track_dict) > 50:
my_track_dict = {} #save the info of track_id
if len(my_result) > 50:
my_result = {} #存储缓冲数据
# print(len(my_track_dict))
#read camera data
if dq:
img = dq.popleft()
print("=====================", img.shape)
else:
print(
"------------------------------>dq------------------->is nan.............."
)
time.sleep(0.02)
continue
start_time = time.time() #开始计时,测试单贞照片处理时间
num += 1
if num % 1000 == 1:
pass
#cv2.imwrite(save_file+"/_{}.jpg".format(num), img)
img_h, img_w, img_ch = img.shape
print(img.shape)
#2、防止裁剪或推理时把画的框裁剪上
show_image = img.copy()
frame = img.copy()
#增加拆解阶段的判断
if num % 10 == 1:
stage_result = mul_stage_model(img)
#print(stage_result)
for i in range(len(stage_result)):
if stage_result[i, 2] > 0.97:
mystage = int(stage_result[i, 1])
#mytest = stage_result[i]
print(stage_result[i, 1], "----------mystage-------->" * 2,
stage_result[i, 2])
break
else:
mystage = 1
#if mystage == 2:
#cv2.rectangle(show_image, (int(mytest[3]*1280), int(mytest[4]*720)), (int(mytest[3]*1280+mytest[5]*1280), int(mytest[4]*720+mytest[6]*720)), (0, 149, 230), 1)
#mystage = 1
cv2.rectangle(show_image, (10, 10), (400, 100), (120, 149, 230), -1)
if mystage == 2:
show_image = add_ch_text(show_image,
"第一拆解阶段:",
15,
15,
textColor=(255, 255, 255),
textSize=30)
show_image = add_ch_text(show_image,
"仅检测衣服和鞋子",
15,
60,
textColor=(255, 255, 255),
textSize=30)
elif mystage == 1:
show_image = add_ch_text(show_image,
"第二拆解阶段:",
15,
15,
textColor=(255, 255, 255),
textSize=30)
show_image = add_ch_text(show_image,
"检测帽子、衣服、手套、鞋子",
15,
60,
textColor=(255, 255, 255),
textSize=30)
#the predict of person.
boxes = []
out = preson_detect(img)
#print(out)
#过滤规定区域内的人性框进行判断，transform the object detection data to input tracter
for i in range(len(out)):
#========my_setting==============
if out[i, 2] > 0.7:
#print("------------------>===================================>", out)
# print(out[i])
left = int(out[i, 3] * img_w)
top = int(out[i, 4] * img_h)
right = int(out[i, 5] * img_w)
bottom = int(out[i, 6] * img_h)
#detect the person in setting area.
point1 = [int((left + right) / 2), bottom]
my_index = inner_point(point1, points2)
# my_index = True
print("my_index", my_index)
if my_index:
boxes.append([left, top, right, bottom])
if boxes:
#if False:
#跟踪算法
if not fbox:
fbox = boxes
init_len = len(fbox)
for i_d in range(0, init_len):
person_dit[str(i_d)] = fbox[i_d][-4:]
continue
else:
bbox = boxes
#print("------------------>===================================>", boxes)
# print("------------------>===================================>", 2)
del_list = []
for key_a in person_dit:
for one_a in bbox:
my_flag = edn_distance(person_dit[key_a], one_a)
if my_flag:
person_dit[key_a] = new_loc(person_dit[key_a], one_a)
# person_dit[key_a] = one_a
break
else:
del_list.append(key_a)
for key_b in del_list:
del person_dit[key_b]
for one_c in bbox:
for key_c in person_dit:
my_flag = edn_distance(person_dit[key_c], one_c)
if my_flag:
break
else:
init_len += 1
person_dit[str(init_len)] = one_c[-4:]
fbox = bbox
#print("---------------------------------->===================================>", 3)
#检测条件设置
t2 = time.time()
detect_time = t2 - t1
control_time = 1 ##setting detect time， detect one time in m second
if detect_time > control_time:
t1 = time.time()
#属性检测
for my_key in person_dit:
#加入控制条件
# if my_key not in my_track_dict.keys() or detect_time>control_time:
if True:
#the code of processing the person box.
label_dict = get_labels(frame, person_dit[my_key],
mystage) #pbox为人单个人形框
print("============================================",
label_dict)
#label_dict 可能为None
if type(label_dict) == type(None):
continue
if "coat" not in label_dict.keys():
continue
my_track_dict[my_key] = label_dict
#draw the boxs of track.
new_box = person_dit[my_key]
p1 = (new_box[0], new_box[1])
p2 = (new_box[2], new_box[3])
cv2.rectangle(show_image, p1, p2, (255, 0, 0), 2, 1)
#添加文字显示
cv2.putText(show_image, "person:" + my_key,
(new_box[0], new_box[1] - 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2)
show_image = draw_person_attr(
show_image, my_track_dict[my_key], new_box,
control_color) # draw the attr of person.
#结果上传
if my_key not in my_result:
#不要直接等于，会出错，深浅拷贝问题
my_result[my_key] = my_track_dict[my_key].copy()
my_result[my_key]["mysum"] = 0
# print("++++++init++++++++++",my_result[my_key])
else:
for key1 in my_track_dict[my_key]:
if key1 in my_result[my_key]:
if key1 == "face":
if my_track_dict[my_key]["face"] != "":
my_result[my_key]["face"] = my_track_dict[
my_key]["face"]
else:
my_result[my_key][key1] = my_result[my_key][
key1] + my_track_dict[my_key][key1]
else:
my_result[my_key][key1] = my_track_dict[my_key][
key1]
my_result[my_key]["mysum"] = my_result[my_key]["mysum"] + 1
# print(my_key, "-------->", my_result[my_key])
if my_result[my_key]["mysum"] > 30:
# print("-------->", my_result[my_key])
my_cum_result = {}
for key2 in my_result[my_key]:
if key2 == "face":
my_cum_result[key2] = my_result[my_key][key2]
else:
if my_result[my_key][key2] / my_result[my_key][
"mysum"] < 0.3:
my_cum_result[key2] = 0
else:
my_cum_result[key2] = 1
del my_cum_result["mysum"] #删除“mysum”属性再上传
del my_result[my_key] #减少内存负担，上传的人员要删除
#if "coat" in my_cum_result and "hat" in my_cum_result and "gloves" in my_cum_result and "shoes" in my_cum_resulti:
print(my_cum_result)
if "coat" in my_cum_result and "shoes" in my_cum_result:
pic_name = str(camerId) + "_" + str(int(
time.time())) + "_" + my_key
# put_data(my_key, my_result, frame)
print("--------put_data-------->" * 2)
loction = person_dit[my_key]
q_put_img.append([
mystage, pic_name, my_cum_result,
frame[loction[1]:loction[3], loction[0]:loction[2]]
])
# my_result[my_key] = {"hat":0, "coat":0, "gloves":0,"shoes":0, "mysum":0}
#draw the gurdline.画警戒线
draw_muti(show_image, points2)
end_time = time.time() #结束计时,测试单贞照片处理时间
my_one_time = (end_time - start_time) * 1000
print("====={}=====".format(num), my_one_time)
print("-------------->", num, my_result)
#cv2.namedWindow("aidong_unicom", 0)
#cv2.imshow('aidong_unicom', show_image)
#key = cv2.waitKey(1)
show_image = cv2.resize(show_image, (640, 360))
ret2, jpeg = cv2.imencode('.jpg', show_image)
yield (b'--frame\r\n'
b'application/octet-stream: image/jpeg\r\n\r\n' +
jpeg.tobytes() + b'\r\n\r\n')
else:
#draw the gurdline.画警戒线
draw_muti(show_image, points2)
end_time = time.time() #结束计时,测试单贞照片处理时间
my_one_time = (end_time - start_time) * 1000
print("=====a{}=====".format(num), my_one_time)
print("-------------->", num, person_dit)
#cv2.namedWindow("aidong_unicom", 0)
#cv2.imshow('aidong_unicom', show_image)
#key = cv2.waitKey(1)
show_image = cv2.resize(show_image, (640, 360))
ret2, jpeg = cv2.imencode('.jpg', show_image)
jpg_data = jpeg.tobytes()
yield (b'--frame\r\n'
b'application/octet-stream: image/jpeg\r\n\r\n' + jpg_data +
b'\r\n\r\n')
def process_my(self, id_video):
global control_color
num = 0
t1 = time.time()
self.mkdir_my()
while True:
#avoid the memory error.
if len(self.my_track_dict)>50:
self.my_track_dict = {}
print(len(self.my_track_dict))
if self.dq:
img = self.dq.pop()
else:
key = cv2.waitKey(20)
continue
start_time = time.time()
num += 1
if num % 500 == 1:
cv2.imwrite(save_file+"/_{}.jpg".format(num), img)
img_h, img_w, img_ch = img.shape
print(img.shape)
#2、防止裁剪或推理时把画的框裁剪上
show_image = img.copy()
frame = img.copy()
#the predict of person.
boxs, confidence, class_names = [], [], []
out = preson_detect(img)
#transform the object detection data to input tracter
for i in range(len(out)):
#========my_setting==============
if out[i, 2] > 0.7:
# print(out[i])
left = int(out[i, 3]*img_w)
top = int(out[i, 4]*img_h)
p_w = int(out[i, 5]*img_w-out[i, 3]*img_w)
p_h = int(out[i, 6]*img_h-out[i, 4]*img_h)
right = left + p_w
bottom = top + p_h
#detect the person in setting area.
point1 = [int((left+right)/2), bottom]
my_index = inner_point(point1)
if my_index:
boxs.append([left, top, p_w, p_h])
class_names.append("person")
confidence.append(out[i, 2])
#start use the self.tracker
features = self.encoder(frame,boxs)
# score to 1.0 here).
detections = [Detection(bbox, 1.0, feature) for bbox, feature in zip(boxs, features)]
# Run non-maxima suppression.
boxes = np.array([d.tlwh for d in detections])
scores = np.array([d.confidence for d in detections])
indices = preprocessing.non_max_suppression(boxes, self.nms_max_overlap, scores)
detections = [detections[i] for i in indices]
# Call the self.tracker
self.tracker.predict()
self.tracker.update(detections)
i = int(0)
indexIDs = []
#setting detect time
t2 = time.time()
detect_time = t2-t1
#========my_setting==============
control_time = 0.2 #detect one time in m second
if detect_time > control_time:
t1 = time.time()
for det, track in zip(detections, self.tracker.tracks):
# if not track.is_confirmed() or track.time_since_update > 1:
if not track.is_confirmed() or track.time_since_update > 1:
continue
#print(track.track_id)
#draw the boxs of object detection.
pbox = det.to_tlbr()
#cv2.rectangle(frame,(int(pbox[0]), int(pbox[1])), (int(pbox[2]), int(pbox[3])),(255,255,255), 2)
my_key = str(int(track.track_id))
#========my_setting==============
#if my_key increase or time lt 3s, will be re_detection.
if my_key not in self.my_track_dict.keys() or detect_time>control_time:
print(my_key)
print(self.my_track_dict.keys())
#the code of processing the person box.
label_dict = person_detect(img, pbox)
print("**"*20,label_dict)
if type(label_dict) == type(None):
continue
if "coat" not in label_dict.keys():
continue
self.my_track_dict[my_key] = label_dict
# draw the attr of person.
frame = draw_person_attr(frame, self.my_track_dict[my_key], pbox, control_color)
indexIDs.append(int(track.track_id))
self.counter.append(int(track.track_id))
bbox = track.to_tlbr()
color = [int(c) for c in COLORS[indexIDs[i] % len(COLORS)]]
#define the color of rectangle.
if self.my_track_dict[my_key]["coat"] == "Yes":
color_rect = (0, 255, 0)
else:
color_rect = (0, 0, 255)
#center_loc = [int((bbox[0]+bbox[2])/2), int((bbox[1]+bbox[3])/2)]
if my_key not in self.track_smooth_dict.keys():
cv2.rectangle(frame, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])),(color_rect), 3)
self.track_smooth_dict[my_key] = bbox
else:
fbox = self.track_smooth_dict[my_key]
a = int((bbox[0]+fbox[0])/2)
b = int((bbox[1]+fbox[1])/2)
c = int((bbox[2]+fbox[2])/2)
d = int((bbox[3]+fbox[3])/2)
cv2.rectangle(frame, (a, b), (c, d),(color_rect), 3)
self.track_smooth_dict[my_key] = bbox
#draw the boxs of track.
#cv2.rectangle(frame, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])),(color), 3)
if True:
cv2.putText(frame,str(track.track_id),(int(bbox[0]), int(bbox[1] -50)),0, 5e-3 * 150, (color),2)
if len(class_names) > 0:
class_name = class_names[0]
cv2.putText(frame, str(class_names[0]),(int(bbox[0]), int(bbox[1] -20)),0, 5e-3 * 150, (color),2)
i += 1
#put_data
# if num % 50 == 1:
# my_result = self.my_track_dict[my_key]
# put_data(my_key, my_result, frame)
count = len(set(self.counter))
#draw the gurdline.
draw_muti(frame)
# cv2.putText(frame, "Total Pedestrian self.counter: "+str(count),(int(20), int(120)),0, 5e-3 * 200, (0,255,0),2)
# cv2.putText(frame, "Current Pedestrian self.counter: "+str(i),(int(20), int(80)),0, 5e-3 * 200, (0,255,0),2)
end_time = time.time()
my_one_time = (end_time - start_time) * 1000
print("====={}=====".format(num), my_one_time)
# frame = cv2.resize(frame, (640, 360))
ret2, jpeg = cv2.imencode('.jpg', frame)
yield (b'--frame\r\n'
b'application/octet-stream: image/jpeg\r\n\r\n' + jpeg.tobytes() + b'\r\n\r\n')
def get_detect(id_video):
my_num = 0
num = 0
video_reader = cv2.VideoCapture(0)
#video_reader = cv2.VideoCapture("rtsp://*****:*****@192.168.199.200/Streaming/Channels/1")
#video_reader.set(cv2.CAP_PROP_FPS, 20)
video_reader.set(cv2.CAP_PROP_FOURCC,
cv2.VideoWriter.fourcc('M', 'J', 'P', 'G'))
video_reader.set(3, 1280)
video_reader.set(4, 720)
video_reader.set(cv2.CAP_PROP_FPS, 25)
while True:
start_time = time.time()
num += 1
print("======", num)
ret1, img = video_reader.read()
if not ret1:
continue
if type(img) == type(None):
continue
#frame = cv2.imread(filename)
#1、读取中文路径
#img = cv2.imdecode(np.fromfile(filename,dtype=np.uint8), cv2.IMREAD_COLOR)
print(img.shape)
#2、防止裁剪或推理时把画的框裁剪上
show_image = img.copy()
if num % 2 == 1:
out = preson_detect(img)
img_h, img_w, img_ch = img.shape
for i in range(len(out)):
#人形框限定条件，根据置信度限定不符合要求的人形框。
if out[i, 2] < 0.7:
continue
#attentions
x0, y0, x1, y1 = out[i, 3:7]
start_x = int(img_w * x0)
start_y = int(img_h * y0)
end_x = int(img_w * x1)
end_y = int(img_h * y1)
point1 = [int((start_x + end_x) / 2), end_y]
my_index = inner_point(point1)
if not my_index:
continue
clip_images = img[start_y:end_y, start_x:end_x]
h, w, c = clip_images.shape
#根据图像大小限定不符合要求的人形框。
if h > 96 and w > 48:
#3、保存图片,修改保存名称
my_num += 1
#save_name = os.path.join(save_dir, "v1_person{}.jpg".format(my_num))
#cv2.imwrite(save_name, clip_images)
label_dict = muti_attr(clip_images)
print(label_dict)
i_count = 0
font = cv2.FONT_HERSHEY_COMPLEX
if "coat" in label_dict.keys():
#4、在原始图像上画矩形。
if label_dict["coat"] == "Yes":
cv2.rectangle(show_image, (start_x, start_y),
(end_x, end_y), (0, 255, 0), 2)
else:
cv2.rectangle(show_image, (start_x, start_y),
(end_x, end_y), (0, 0, 255), 2)
#5、在原始图像上画矩形。
for key in label_dict:
if key == "hat":
a = "帽子"
if label_dict[key] == "Yes":
b = "合格"
else:
b = "不合格"
elif key == "coat":
a = "衣服"
if label_dict[key] == "Yes":
b = "合格"
else:
b = "不合格"
elif key == "gloves":
a = "手套"
if label_dict[key] == "Yes":
b = "合格"
else:
b = "不合格"
elif key == "shoes":
a = "鞋子"
if label_dict[key] == "Yes":
b = "合格"
else:
b = "不合格"
text = a + ":" + str(b)
print(text)
if label_dict[key] == "Yes":
show_image = add_ch_text(show_image,
text,
end_x,
start_y + i_count,
textColor=(0, 255, 0),
textSize=30)
#cv2.putText(show_image,text,(end_x,start_y+i_count),font,0.7,(0,255,0),2)
elif label_dict[key] == "No":
show_image = add_ch_text(show_image,
text,
end_x,
start_y + i_count,
textColor=(255, 0, 0),
textSize=30)
#cv2.putText(show_image,text,(end_x,start_y+i_count),font,0.7,(0,0,255),2)
i_count += 35
draw_muti(show_image)
ret2, jpeg = cv2.imencode('.jpg', show_image)
yield (b'--frame\r\n'
b'application/octet-stream: image/jpeg\r\n\r\n' +
jpeg.tobytes() + b'\r\n\r\n')
else:
time.sleep(0.01)
end_time = time.time()
mytime = (end_time - start_time) * 1000
print(mytime)
