用摄像机来完成实时检测

此程序基于Tensorflow object detection API。

视频演示：https://www.bilibili.com/video/av32418677/?p=1

 # By Bend_Function
 # https://space.bilibili.com/275177832
 # 可以放在任何文件夹下运行（前提正确配置API[环境变量]）
 # 退出 按q键
 
 import numpy as np
 import tensorflow as tf
 import cv2
 import os
 
 from object_detection.utils import visualization_utils as vis_util
 from object_detection.utils import label_map_util
 
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
 cv2.setUseOptimized(True)           # 加速cv
 
 # 要改的内容
 ###############################################
 PATH_TO_CKPT = 'model\\ssd_mobilenet_v1_graph.pb'   # 模型及标签地址
 PATH_TO_LABELS = 'model\\mscoco_label_map.pbtxt'
 
 NUM_CLASSES = 90            # 检测对象个数
 
 camera_num = 1                 # 要打开的摄像头编号，可能是0或1
 width, height = 1280,720    # 视频分辨率
 ###############################################
 
 # Load a (frozen) Tensorflow model into memory.
 detection_graph = tf.Graph()
 with detection_graph.as_default():
     od_graph_def = tf.GraphDef()
     with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
         serialized_graph = fid.read()
         od_graph_def.ParseFromString(serialized_graph)
         tf.import_graph_def(od_graph_def, name='')
 
 # Loading label map
 label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
 categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
 category_index = label_map_util.create_category_index(categories)
 
 
 mv = cv2.VideoCapture(camera_num)  # 打开摄像头
 
 mv.set(3, width)     # 设置分辨率
 mv.set(4, height)
 
 
 config = tf.ConfigProto()
 config.gpu_options.allow_growth = True
 with detection_graph.as_default():
     with tf.Session(graph=detection_graph, config=config) as sess:
         image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
         detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
         detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
         detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
         num_detections = detection_graph.get_tensor_by_name('num_detections:0')
 
         while True:
             ret, image_source = mv.read()  # 读取视频帧
             image_np = cv2.resize(image_source , (width, height), interpolation=cv2.INTER_CUBIC)
             image_np_expanded = np.expand_dims(image_np, axis=0)
             # Actual detection.
             (boxes, scores, classes, num) = sess.run(
                 [detection_boxes, detection_scores, detection_classes, num_detections],
                 feed_dict={image_tensor: image_np_expanded})
             # Visualization of the results of a detection.
             vis_util.visualize_boxes_and_labels_on_image_array(
                 image_np,
                 np.squeeze(boxes),
                 np.squeeze(classes).astype(np.int32),
                 np.squeeze(scores),
                 category_index,
                 use_normalized_coordinates=True,
                 line_thickness=4)
             cv2.imshow("video", image_np)
             if cv2.waitKey(1) & 0xFF == ord('q'):  # 按q退出
                 break
 cap.release()            
 cv2.destroyAllWindows()                                          # 基本操作