Python 如何解决多处理错误：；名称错误：名称'；W'；“未定义”；
我在尝试优化视频上运行的对象检测代码时遇到此错误。代码进行初始检测，然后处理它们以获得边界框和类索引。传递给Python 如何解决多处理错误：；名称错误：名称'；W'；“未定义”；,python,multithreading,multiprocessing,Python,Multithreading,Multiprocessing,我在尝试优化视频上运行的对象检测代码时遇到此错误。代码进行初始检测，然后处理它们以获得边界框和类索引。传递给池.map\u async函数的目标函数是： def process_detection( detections ): for detection_level in detections: for detection in detection_level: # print(detection.shape) # (85,)
池.map\u async def process_detection( detections ):
for detection_level in detections:
for detection in detection_level:
# print(detection.shape) # (85,) shape
xcenter, ycenter, width, height = np.asarray([W, H, W, H]) * detection[0:4]
confidence_encoded = detection[5:] # (80,) array
index_class = np.argmax(confidence_encoded) #index of max confidence
confidence = confidence_encoded[index_class] # float value of confidence (probability)
class_predicted = classes[index_class] # class predicted
# print(class_predicted)
if confidence > 0.5:
if class_predicted == "person":
print("{} , {:.2f}".format(class_predicted, confidence))
# continue
topX = int(xcenter - width/2.)
topY = int(xcenter - height/2.)
width = int(width)
height = int(height)
confidence = float(confidence)
bbox = [topX, topY, width, height]
boxes.append(bbox)
confidences.append(confidence)
classIDs.append(index_class)
classes_pred.append(class_predicted)
return [boxes, confidences, classIDs, classes_pred]
回溯（最近一次呼叫最后一次）：
文件“C:\Users\naqas\OneDrive\Desktop\tracking\u code\u new.py”，第181行，在
main（）
文件“C:\Users\naqas\OneDrive\Desktop\tracking\u code\u new.py”，主目录第134行
RESULTS=RESULTS.get（）
get中第657行的文件“C:\Users\naqas\Anaconda3\lib\multiprocessing\pool.py”
提升自我价值
NameError:未定义名称“W”
def main():
pool = Pool(processes=os.cpu_count()) # make a process pool for multi-processing
path = Path("C:/","Users","naqas","Downloads","content","gdrive","My Drive","YOLOv3")
classes = open(str(path.joinpath("coco.names")), "r").read().strip().split("\n")
colors = np.random.randint(0,255,(1000,3),dtype="uint8")
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# reading the video
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
cap = cv2.VideoCapture(str(path.joinpath("video_.mp4")))
_, frame = cap.read()
if frame is None:
print(f"FRAME IS NOT READ")
else:
# frame = resize(frame, width=500)
H, W = frame.shape[0:2]
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# <model>
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
configPath = path.joinpath("yolov3.cfg")
weightsPath = path.joinpath("yolov3.weights")
net = cv2.dnn.readNetFromDarknet(str(configPath), str(weightsPath))
ln = net.getLayerNames()
ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]
writer = None
boxes = []
confidences = []
classIDs = []
classes_pred = []
fps_ = FPS().start()
i = 0
while True:
try:
if writer is None:
writer = cv2.VideoWriter("./detections.avi", cv2.VideoWriter_fourcc(*"MJPG"), cv2.CAP_PROP_FPS, (W, H))
_, frame = cap.read() # reading the frame
# frame = resize(frame, width=W) # resizing the frame
blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416),
swapRB=True, crop=False) # yolov3 version
net.setInput(blob)
start = time()
detections = net.forward(ln)
end = time()
print(f"{(end-start):.2f} seconds taken for detection")
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# after detection
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# print(len(detections)) # detections from 3 levels
# print(detections[0].shape, detections[-1].shape) # detections are in [N, D] format with D = 85
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
results = pool.map_async(process_detection, detections)
pool.close()
pool.join()
print(results)
# boxes, confidences, classIDs, classes_pred = results.get()
RESULTS = results.get()
def main（）：
pool=pool（processs=os.cpu_count（））#为多处理创建一个进程池
路径=路径（“C:/”、“用户”、“纳卡”、“下载”、“内容”、“gdrive”、“我的驱动器”、“YOLOv3”）
classes=open（str（path.joinpath（“coco.names”），“r”）.read（）.strip（）.split（“\n”）
colors=np.random.randint（0255，（1000,3），dtype=“uint8”）
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#阅读视频
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
cap=cv2.VideoCapture（str（path.joinpath（“video.mp4”））
_，frame=cap.read（）
如果框架为无：
打印（f“未读取帧”）
其他：
#框架=调整大小（框架，宽度=500）
H、 W=框架形状[0:2]
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
configPath=path.joinpath（“yolov3.cfg”）
weightsPath=path.joinpath（“yolov3.weights”）
net=cv2.dnn.readNetFromDarknet（str（configPath），str（weightsPath））
ln=net.getLayerNames（）
ln=[ln[i[0]-1]表示net.getUnconnectedOutLayers（）中的i
writer=无
框=[]
信任=[]
ClassID=[]
类_pred=[]
fps=fps（）.start（）
i=0
尽管如此：
尝试：
如果writer为None：
writer=cv2.VideoWriter（“./detections.avi”，cv2.VideoWriter\u fourcc（*“MJPG”），cv2.CAP\u PROP\u FPS，（W，H））
_，frame=cap.read（）#读取帧
#框架=调整大小（框架，宽度=W）#调整框架大小
blob=cv2.dnn.blobFromImage（帧，1/255.0，（416416），
swapRB=True，crop=False）#yolov3版本
net.setInput（blob）
开始=时间（）
检测=净转发（ln）
结束=时间（）
打印（f“{（结束-开始）：.2f}检测所用的秒数”）
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#发现后
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#打印（len（检测））#三级检测
#打印（检测[0]。形状，检测[-1]。形状）#检测为[N，D]格式，D=85
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
结果=pool.map\u async（进程检测，检测）
pool.close（）
pool.join（）
打印（结果）
#框、信任、ClassID、类\u pred=results.get（）
RESULTS=RESULTS.get（）
您能建议如何解决这个奇怪的错误吗？
WH过程检测中使用，但未定义。您需要将数据传入或使其全球化。
def main():
pool = Pool(processes=os.cpu_count()) # make a process pool for multi-processing
path = Path("C:/","Users","naqas","Downloads","content","gdrive","My Drive","YOLOv3")
classes = open(str(path.joinpath("coco.names")), "r").read().strip().split("\n")
colors = np.random.randint(0,255,(1000,3),dtype="uint8")
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# reading the video
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
cap = cv2.VideoCapture(str(path.joinpath("video_.mp4")))
_, frame = cap.read()
if frame is None:
print(f"FRAME IS NOT READ")
else:
# frame = resize(frame, width=500)
H, W = frame.shape[0:2]
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# <model>
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
configPath = path.joinpath("yolov3.cfg")
weightsPath = path.joinpath("yolov3.weights")
net = cv2.dnn.readNetFromDarknet(str(configPath), str(weightsPath))
ln = net.getLayerNames()
ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]
writer = None
boxes = []
confidences = []
classIDs = []
classes_pred = []
fps_ = FPS().start()
i = 0
while True:
try:
if writer is None:
writer = cv2.VideoWriter("./detections.avi", cv2.VideoWriter_fourcc(*"MJPG"), cv2.CAP_PROP_FPS, (W, H))
_, frame = cap.read() # reading the frame
# frame = resize(frame, width=W) # resizing the frame
blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416),
swapRB=True, crop=False) # yolov3 version
net.setInput(blob)
start = time()
detections = net.forward(ln)
end = time()
print(f"{(end-start):.2f} seconds taken for detection")
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# after detection
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# print(len(detections)) # detections from 3 levels
# print(detections[0].shape, detections[-1].shape) # detections are in [N, D] format with D = 85
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
results = pool.map_async(process_detection, detections)
pool.close()
pool.join()
print(results)
# boxes, confidences, classIDs, classes_pred = results.get()
RESULTS = results.get()