只是一個測試，測試在并發運行下，同時開啟兩個攝像頭獲取視頻流并調用識別函數的運行結果，以后在攝像頭多的情況下，肯定不能這樣，需要批量創建各種對象。

并發，指的是不在多線程的情況下，每個攝像頭的視頻流送給識別函數輪流進行識別并返回結果，其中一個識別時，另一個需等待。

這里配置Intel Realsense D435攝像頭各種對象時，用的是最傻的一個一個創建的方法。

# -*- encoding: utf-8 -*- """ @File : test-同時調用兩個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 rsclass 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()pipeline2 = rs.pipeline()config1 = rs.config()config2 = 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)serial2 = ctx.devices[1].get_info(rs.camera_info.serial_number)config1.enable_device(serial1)config2.enable_device(serial2)config1.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)config2.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)config1.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)config2.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)pipeline1.start(config1)pipeline2.start(config2)# 創建對齊對象（深度對齊顏色）align1 = rs.align(rs.stream.color)align2 = rs.align(rs.stream.color)try:while True:frames1 = pipeline1.wait_for_frames()frames2 = pipeline2.wait_for_frames()# 獲取對齊幀集aligned_frames1 = align1.process(frames1)aligned_frames2 = align2.process(frames2)# 獲取對齊后的深度幀和彩色幀aligned_depth_frame1 = aligned_frames1.get_depth_frame()aligned_depth_frame2 = aligned_frames2.get_depth_frame()color_frame1 = aligned_frames1.get_color_frame()color_frame2 = aligned_frames2.get_color_frame()# 獲取顏色幀內參color_profile1 = color_frame1.get_profile()color_profile2 = color_frame2.get_profile()cvsprofile1 = rs.video_stream_profile(color_profile1)cvsprofile2 = rs.video_stream_profile(color_profile2)color_intrin1 = cvsprofile1.get_intrinsics()color_intrin2 = cvsprofile2.get_intrinsics()color_intrin_part1 = [color_intrin1.ppx, color_intrin1.ppy, color_intrin1.fx, color_intrin1.fy]color_intrin_part2 = [color_intrin2.ppx, color_intrin2.ppy, color_intrin2.fx, color_intrin2.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())color_image2 = np.asanyarray(color_frame2.get_data())# D·C 191121：顯示幀看看# cv2.namedWindow('RealSense', cv2.WINDOW_AUTOSIZE)# cv2.imshow('RealSense', color_frame)# cv2.waitKey(1)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)image2 = utils.draw_bbox(color_image2, bboxes_pr2, aligned_depth_frame2, color_intrin_part2,show_label=self.show_label)# cv2.namedWindow('RealSense', cv2.WINDOW_AUTOSIZE)cv2.imshow('window1', image1)cv2.imshow('window2', image2)cv2.waitKey(1)finally:pipeline1.stop()pipeline2.stop()if __name__ == '__main__':YoloTest().dontla_evaluate_detect()

總結

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