今天，test-191204-單個(gè)攝像頭調(diào)用multiprocessing線程隊(duì)列queue識(shí)別時(shí)，報(bào)錯(cuò)：

D:\20191031_tensorflow_yolov3\python\python.exe D:/20191031_tensorflow_yolov3/tensorflow-yolov3/test-191204-單個(gè)攝像頭調(diào)用multiprocessing線程隊(duì)列queue識(shí)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(gè)攝像頭調(diào)用multiprocessing線程隊(duì)列queue識(shí)別.py", line 223, in <module>YoloTest().dontla_evaluate_detect()File "D:/20191031_tensorflow_yolov3/tensorflow-yolov3/test-191204-單個(gè)攝像頭調(diào)用multiprocessing線程隊(duì)列queue識(shí)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(bào)錯(cuò)代碼：

# -*- coding: utf-8 -*- """ @File : test-191204-單個(gè)攝像頭調(diào)用multiprocessing線程隊(duì)列queue識(shí)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(shù)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(biāo)記（bool類型值）self.write_image = cfg.TEST.WRITE_IMAGE# D·C 191115：__C.TEST.WRITE_IMAGE_PATH = "./data/detection/"（識(shí)別圖片畫框并標(biāo)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(shè)置為Trueself.show_label = cfg.TEST.SHOW_LABEL# D·C 191120：創(chuàng)建命名空間“input”with tf.name_scope('input'):# D·C 191120：建立變量（創(chuàng)建占位符開辟內(nèi)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(chuàng)建命名空間“指數(shù)滑動(dòng)平均”with tf.name_scope('ema'):ema_obj = tf.train.ExponentialMovingAverage(self.moving_ave_decay)# D·C 191120：在允許軟設(shè)備放置的會(huì)話中啟動(dòng)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(dòng)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(jì)算滑動(dòng)平均值時(shí)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(xùn)練時(shí)恢復(fù)模型self.saver.restore(self.sess, self.weight_file)def predict(self, image):# D·C 191107：復(fù)制一份圖片的鏡像，避免對(duì)圖片直接操作改變圖片的內(nèi)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(shù)將源圖結(jié)合input_size，將其轉(zhuǎn)換成預(yù)投喂的方形圖像（作者默認(rèn)544×544，中間為縮小尺寸的源圖，上下空區(qū)域?yàn)榛覉D）：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(chuàng)建新軸，不懂要?jiǎng)?chuàng)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(gè)box可能存放了預(yù)測(cè)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(gè)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(shù)個(gè)6列數(shù)組（后面self.num_classes)個(gè)數(shù)存放的貌似是這個(gè)框?qū)儆陬惖母怕?#xff09;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(cè)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(biāo)，第五列是得分，第六列是對(duì)應(yīng)類下標(biāo)bboxes = utils.postprocess_boxes(pred_bbox, (org_h, org_w), self.input_size, self.score_threshold)# D·C 191111：猜測(cè)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(hào)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(chuàng)建對(duì)齊對(duì)象（深度對(duì)齊顏色）align1 = rs.align(rs.stream.color)try:while True:frames1 = pipeline1.wait_for_frames()# 獲取對(duì)齊幀集aligned_frames1 = align1.process(frames1)# 獲取對(duì)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(nèi)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(jìn)程運(yùn)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。。。

總結(jié)

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