解決方案：
參考：Intel Realsense D435報錯 RuntimeError: MFCreateDeviceSource(_device_attrs, &_source) returned: HResult

完整示例代碼：
主要看def dontla_evaluate_detect(self)

# -*- coding: utf-8 -*- """ @File : test-使用locals()函數批量配置攝像頭運行識別程序并畫框.py @Time : 2019/11/26 11:20 @Author : Dontla @Email : sxana@qq.com @Software: PyCharm """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 timeclass 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):ctx = rs.context()devices = ctx.query_devices()# 循環reset攝像頭# hardware_reset()后是不是應該延遲一段時間？不延遲就會報錯for dev in devices:dev.hardware_reset()print('攝像頭{}重置成功'.format(i for i, x in enumerate(devices) if x == dev))time.sleep(5)# Check if the cameras are all connectedcam_num = len(ctx.devices)print('攝像頭個數：{}'.format(cam_num))if cam_num != 6:print('The cameras are not all connected!')else:# Print the serial number of the connected camera and its USB interface numbern = 0# 創建需要顯示在窗口上的備注信息（窗口名）serial_list = []for i in ctx.devices:n += 1serial_list.append('camera{}; serials number {}; usb port {}'.format(n, i.get_info(rs.camera_info.serial_number),i.get_info(rs.camera_info.usb_type_descriptor)))print('serial number {}：{}；usb port：{}'.format(n, i.get_info(rs.camera_info.serial_number),i.get_info(rs.camera_info.usb_type_descriptor)))# print(serial_list)for i in range(cam_num):locals()['pipeline' + str(i)] = rs.pipeline()locals()['config' + str(i)] = rs.config()locals()['serial' + str(i)] = ctx.devices[i].get_info(rs.camera_info.serial_number)locals()['config' + str(i)].enable_device(locals()['serial' + str(i)])locals()['config' + str(i)].enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)locals()['config' + str(i)].enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)locals()['pipeline' + str(i)].start(locals()['config' + str(i)])# 創建對齊對象（深度對齊顏色）locals()['align' + str(i)] = rs.align(rs.stream.color)try:# 設置break標志break2 = Falsewhile True:for i in range(cam_num):locals()['frames' + str(i)] = locals()['pipeline' + str(i)].wait_for_frames()# 獲取對齊幀集locals()['aligned_frames' + str(i)] = locals()['align' + str(i)].process(locals()['frames' + str(i)])# 獲取對齊后的深度幀和彩色幀locals()['aligned_depth_frame' + str(i)] = locals()['aligned_frames' + str(i)].get_depth_frame()locals()['color_frame' + str(i)] = locals()['aligned_frames' + str(i)].get_color_frame()if not locals()['aligned_depth_frame' + str(i)] or not locals()['color_frame' + str(i)]:continue# 獲取顏色幀內參locals()['color_profile' + str(i)] = locals()['color_frame' + str(i)].get_profile()locals()['cvsprofile' + str(i)] = rs.video_stream_profile(locals()['color_profile' + str(i)])locals()['color_intrin' + str(i)] = locals()['cvsprofile' + str(i)].get_intrinsics()locals()['color_intrin_part' + str(i)] = [locals()['color_intrin' + str(i)].ppx,locals()['color_intrin' + str(i)].ppy,locals()['color_intrin' + str(i)].fx,locals()['color_intrin' + str(i)].fy]locals()['color_image' + str(i)] = np.asanyarray(locals()['color_frame' + str(i)].get_data())# D·C 191121：顯示幀看看# cv2.namedWindow('RealSense', cv2.WINDOW_AUTOSIZE)# cv2.imshow('RealSense', color_frame)# cv2.waitKey(1)locals()['bboxes_pr' + str(i)] = self.predict(locals()['color_image' + str(i)])locals()['image' + str(i)] = utils.draw_bbox(locals()['color_image' + str(i)],locals()['bboxes_pr' + str(i)],locals()['aligned_depth_frame' + str(i)],locals()['color_intrin_part' + str(i)],show_label=self.show_label)# cv2.namedWindow('RealSense', cv2.WINDOW_AUTOSIZE)# cv2.imshow('window{}'.format(i), locals()['image' + str(i)])cv2.imshow('{}'.format(serial_list[i]), locals()['image' + str(i)])key = cv2.waitKey(1)# 如果按下ESC，則跳出循環if key == 27:# 貌似直接用return也行# returnbreak2 = Truebreakif break2 == True:breakfinally:# 停止所有流locals()['pipeline' + str(i)].stop()# 銷毀所有窗口cv2.destroyAllWindows()if __name__ == '__main__':YoloTest().dontla_evaluate_detect()

總結

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