今天，test-191204-單個攝像頭調用multiprocessing線程隊列queue識別時，報錯：

D:\20191031_tensorflow_yolov3\python\python.exe D:/20191031_tensorflow_yolov3/tensorflow-yolov3/test-191204-單個攝像頭調用multiprocessing線程隊列queue識別.py 2019-12-05 14:40:38.472262: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 2019-12-05 14:40:39.549686: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1411] Found device 0 with properties: name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.6575 pciBusID: 0000:0e:00.0 totalMemory: 11.00GiB freeMemory: 9.10GiB 2019-12-05 14:40:39.731129: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1411] Found device 1 with properties: name: GeForce GT 710 major: 3 minor: 5 memoryClockRate(GHz): 0.954 pciBusID: 0000:05:00.0 totalMemory: 2.00GiB freeMemory: 1.67GiB 2019-12-05 14:40:39.731780: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1461] Ignoring visible gpu device (device: 1, name: GeForce GT 710, pci bus id: 0000:05:00.0, compute capability: 3.5) with Cuda compute capability 3.5. The minimum required Cuda capability is 3.7. 2019-12-05 14:40:39.732402: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1490] Adding visible gpu devices: 0 2019-12-05 14:40:41.527990: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] Device interconnect StreamExecutor with strength 1 edge matrix: 2019-12-05 14:40:41.528315: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977] 0 1 2019-12-05 14:40:41.528511: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0: N N 2019-12-05 14:40:41.528706: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 1: N N 2019-12-05 14:40:41.529236: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1103] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 8789 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:0e:00.0, compute capability: 6.1) Traceback (most recent call last):File "D:/20191031_tensorflow_yolov3/tensorflow-yolov3/test-191204-單個攝像頭調用multiprocessing線程隊列queue識別.py", line 223, in <module>YoloTest().dontla_evaluate_detect()File "D:/20191031_tensorflow_yolov3/tensorflow-yolov3/test-191204-單個攝像頭調用multiprocessing線程隊列queue識別.py", line 201, in dontla_evaluate_detectprocess.start()File "D:\20191031_tensorflow_yolov3\python\lib\multiprocessing\process.py", line 105, in startself._popen = self._Popen(self)File "D:\20191031_tensorflow_yolov3\python\lib\multiprocessing\context.py", line 223, in _Popenreturn _default_context.get_context().Process._Popen(process_obj)File "D:\20191031_tensorflow_yolov3\python\lib\multiprocessing\context.py", line 322, in _Popenreturn Popen(process_obj)File "D:\20191031_tensorflow_yolov3\python\lib\multiprocessing\popen_spawn_win32.py", line 65, in __init__reduction.dump(process_obj, to_child)File "D:\20191031_tensorflow_yolov3\python\lib\multiprocessing\reduction.py", line 60, in dumpForkingPickler(file, protocol).dump(obj) AttributeError: Can't pickle local object 'YoloTest.dontla_evaluate_detect.<locals>.predict_result'Process finished with exit code 1

報錯代碼：

# -*- coding: utf-8 -*- """ @File : test-191204-單個攝像頭調用multiprocessing線程隊列queue識別.py @Time : 2019/12/5 13:50 @Author : Dontla @Email : sxana@qq.com @Software: PyCharm """ import multiprocessing import cv2 import numpy as np import tensorflow as tf import core.utils as utils from core.config import cfg from core.yolov3 import YOLOV3 import pyrealsense2 as rs import functoolsclass YoloTest(object):def __init__(self):# D·C 191111：__C.TEST.INPUT_SIZE = 544self.input_size = cfg.TEST.INPUT_SIZEself.anchor_per_scale = cfg.YOLO.ANCHOR_PER_SCALE# Dontla 191106注釋：初始化class.names文件的字典信息屬性self.classes = utils.read_class_names(cfg.YOLO.CLASSES)# D·C 191115：類數量屬性self.num_classes = len(self.classes)self.anchors = np.array(utils.get_anchors(cfg.YOLO.ANCHORS))# D·C 191111：__C.TEST.SCORE_THRESHOLD = 0.3self.score_threshold = cfg.TEST.SCORE_THRESHOLD# D·C 191120：__C.TEST.IOU_THRESHOLD = 0.45self.iou_threshold = cfg.TEST.IOU_THRESHOLDself.moving_ave_decay = cfg.YOLO.MOVING_AVE_DECAY# D·C 191120：__C.TEST.ANNOT_PATH = "./data/dataset/Dontla/20191023_Artificial_Flower/test.txt"self.annotation_path = cfg.TEST.ANNOT_PATH# D·C 191120：__C.TEST.WEIGHT_FILE = "./checkpoint/f_g_c_weights_files/yolov3_test_loss=15.8845.ckpt-47"self.weight_file = cfg.TEST.WEIGHT_FILE# D·C 191115：可寫標記（bool類型值）self.write_image = cfg.TEST.WRITE_IMAGE# D·C 191115：__C.TEST.WRITE_IMAGE_PATH = "./data/detection/"（識別圖片畫框并標注文本后寫入的圖片路徑）self.write_image_path = cfg.TEST.WRITE_IMAGE_PATH# D·C 191116：TEST.SHOW_LABEL設置為Trueself.show_label = cfg.TEST.SHOW_LABEL# D·C 191120：創建命名空間“input”with tf.name_scope('input'):# D·C 191120：建立變量（創建占位符開辟內存空間）self.input_data = tf.placeholder(dtype=tf.float32, name='input_data')self.trainable = tf.placeholder(dtype=tf.bool, name='trainable')model = YOLOV3(self.input_data, self.trainable)self.pred_sbbox, self.pred_mbbox, self.pred_lbbox = model.pred_sbbox, model.pred_mbbox, model.pred_lbbox# D·C 191120：創建命名空間“指數滑動平均”with tf.name_scope('ema'):ema_obj = tf.train.ExponentialMovingAverage(self.moving_ave_decay)# D·C 191120：在允許軟設備放置的會話中啟動圖形并記錄放置決策。（不懂啥意思。。。）allow_soft_placement=True表示允許tf自動選擇可用的GPU和CPUself.sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))# D·C 191120：variables_to_restore()用于加載模型計算滑動平均值時將影子變量直接映射到變量本身self.saver = tf.train.Saver(ema_obj.variables_to_restore())# D·C 191120：用于下次訓練時恢復模型self.saver.restore(self.sess, self.weight_file)def predict(self, image):# D·C 191107：復制一份圖片的鏡像，避免對圖片直接操作改變圖片的內在屬性org_image = np.copy(image)# D·C 191107：獲取圖片尺寸org_h, org_w, _ = org_image.shape# D·C 191108：該函數將源圖結合input_size，將其轉換成預投喂的方形圖像（作者默認544×544，中間為縮小尺寸的源圖，上下空區域為灰圖）：image_data = utils.image_preprocess(image, [self.input_size, self.input_size])# D·C 191108：打印維度看看：# print(image_data.shape)# (544, 544, 3)# D·C 191108：創建新軸，不懂要創建新軸干嘛？image_data = image_data[np.newaxis, ...]# D·C 191108：打印維度看看：# print(image_data.shape)# (1, 544, 544, 3)# D·C 191110：三個box可能存放了預測框圖（可能是N多的框，有用的沒用的重疊的都在里面）的信息（但是打印出來的值完全看不懂啊喂？）pred_sbbox, pred_mbbox, pred_lbbox = self.sess.run([self.pred_sbbox, self.pred_mbbox, self.pred_lbbox],feed_dict={self.input_data: image_data,self.trainable: False})# D·C 191110：打印三個box的類型、形狀和值看看：# print(type(pred_sbbox))# print(type(pred_mbbox))# print(type(pred_lbbox))# 都是<class 'numpy.ndarray'># print(pred_sbbox.shape)# print(pred_mbbox.shape)# print(pred_lbbox.shape)# (1, 68, 68, 3, 6)# (1, 34, 34, 3, 6)# (1, 17, 17, 3, 6)# print(pred_sbbox)# print(pred_mbbox)# print(pred_lbbox)# D·C 191110：（-1，6）表示不知道有多少行，反正你給我整成6列，然后concatenate又把它們仨給疊起來，最終得到無數個6列數組（后面self.num_classes)個數存放的貌似是這個框屬于類的概率）pred_bbox = np.concatenate([np.reshape(pred_sbbox, (-1, 5 + self.num_classes)),np.reshape(pred_mbbox, (-1, 5 + self.num_classes)),np.reshape(pred_lbbox, (-1, 5 + self.num_classes))], axis=0)# D·C 191111：打印pred_bbox和它的維度看看：# print(pred_bbox)# print(pred_bbox.shape)# (18207, 6)# D·C 191111：猜測是第一道過濾，過濾掉score_threshold以下的圖片，過濾完之后少了好多：# D·C 191115：bboxes維度為[n,6]，前四列是坐標，第五列是得分，第六列是對應類下標bboxes = utils.postprocess_boxes(pred_bbox, (org_h, org_w), self.input_size, self.score_threshold)# D·C 191111：猜測是第二道過濾，過濾掉iou_threshold以下的圖片：bboxes = utils.nms(bboxes, self.iou_threshold)return bboxesdef dontla_evaluate_detect(self):pipeline1 = rs.pipeline()config1 = rs.config()ctx = rs.context()# 通過程序去獲取已連接攝像頭序列號serial1 = ctx.devices[0].get_info(rs.camera_info.serial_number)config1.enable_device(serial1)config1.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)config1.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)pipeline1.start(config1)# 創建對齊對象（深度對齊顏色）align1 = rs.align(rs.stream.color)try:while True:frames1 = pipeline1.wait_for_frames()# 獲取對齊幀集aligned_frames1 = align1.process(frames1)# 獲取對齊后的深度幀和彩色幀aligned_depth_frame1 = aligned_frames1.get_depth_frame()color_frame1 = aligned_frames1.get_color_frame()# 獲取顏色幀內參color_profile1 = color_frame1.get_profile()cvsprofile1 = rs.video_stream_profile(color_profile1)color_intrin1 = cvsprofile1.get_intrinsics()color_intrin_part1 = [color_intrin1.ppx, color_intrin1.ppy, color_intrin1.fx, color_intrin1.fy]# if not aligned_depth_frame1 or not color_frame1:# continue# if not aligned_depth_frame2 or not color_frame2:# continuecolor_image1 = np.asanyarray(color_frame1.get_data())# D·C 191121：顯示幀看看# cv2.namedWindow('RealSense', cv2.WINDOW_AUTOSIZE)# cv2.imshow('RealSense', color_frame)# cv2.waitKey(1)def predict_result(color_image, queue):queue.put(self.predict(color_image))queue = multiprocessing.Queue()jobs = []for i in range(1):process = multiprocessing.Process(target=predict_result, args=(color_image1, queue))jobs.append(process)process.start()# 等待進程運行完for process in jobs:process.join()results = [queue.get() for j in jobs]bboxes_pr1 = results[0]# bboxes_pr1 = self.predict(color_image1)# bboxes_pr2 = self.predict(color_image2)image1 = utils.draw_bbox(color_image1, bboxes_pr1, aligned_depth_frame1, color_intrin_part1,show_label=self.show_label)# cv2.namedWindow('RealSense', cv2.WINDOW_AUTOSIZE)cv2.imshow('window1', image1)cv2.waitKey(1)finally:pipeline1.stop()if __name__ == '__main__':YoloTest().dontla_evaluate_detect()

參考解決辦法：AttributeError: Can’t pickle local object 解決辦法

反正我是沒解決。。。

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