（tensorflow学习）用Object Detection API实现摄像头实时物体检测

Myth丶恋晨 2022-08-29 00:11 151阅读 0赞

对于物体识别，谷歌已经有训练好的模型供我们使用，图方便不想自己训练的可以直接使用。  
说实话，装这个tensorflow真心麻烦，我建议用anaconda环境搭建，还要注意装的话装1.几的版本就可，用gpu跑的话注意显卡型号和版本是否兼容。真是踩无数坑啊。。建议找个细致一点的教程，一步一步来，至于这个我就不多写了。  
要获取预训练模型，[模型下载][Link 1]  
在这里还要添加模型目录，就是把下载的解压到anaconda的库目录下，可以搜相关教程即可。  
我的代码：

# coding: utf-8
    # 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 = 'C:/Users/POG/anaconda3/Lib/site-packages/tensorflow/models/research/object_detection/ssd_mobilenet_v1_coco_2018_01_28/frozen_inference_graph.pb'   # 模型及标签地址
    PATH_TO_LABELS = 'C:/Users/POG/anaconda3/Lib/site-packages/tensorflow/models/research/object_detection/data/mscoco_label_map.pbtxt'
    
    NUM_CLASSES = 90            # 检测对象个数
    
    camera_num = 0                # 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.compat.v1.GraphDef()
        with tf.compat.v1.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.compat.v1.ConfigProto()
    config.gpu_options.allow_growth = True
    with detection_graph.as_default():
        with tf.compat.v1.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_source = cv2.cvtColor(image_source, cv2.COLOR_BGR2RGB)
                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)
                image_np = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
                cv2.imshow("video", image_np)
                if cv2.waitKey(1) & 0xFF == ord('q'):  # 按q退出
                    break

我这里用的是tensorflow2的版本，代码会和1的有些不同。  
结果：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzUyMjkwNDM2_size_16_color_FFFFFF_t_70]

[Link 1]: http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2018_01_28.tar.gz
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzUyMjkwNDM2_size_16_color_FFFFFF_t_70]: /images/20220829/b725641da7c042f39147d33d67e6db2b.png