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使用Python的对象检测项目_Python_Tensorflow_Machine Learning_Computer Vision_Object Detection - Fatal编程技术网

使用Python的对象检测项目

使用Python的对象检测项目,python,tensorflow,machine-learning,computer-vision,object-detection,Python,Tensorflow,Machine Learning,Computer Vision,Object Detection,我正在做一个物体检测项目。我遇到了很多错误,我已经修复了大部分错误,只剩下一个错误了。这是我的google colab笔记本: 错误在检测部分 以下是我的检测部分的代码: with detection_graph.as_default(): with tf.Session(graph=detection_graph) as sess: ret = True while (ret): ret,image_np = cap.read()

我正在做一个物体检测项目。我遇到了很多错误,我已经修复了大部分错误,只剩下一个错误了。这是我的google colab笔记本:

错误在检测部分

以下是我的检测部分的代码:

with detection_graph.as_default():
    with tf.Session(graph=detection_graph) as sess:
        ret = True
        while (ret):
            ret,image_np = cap.read()

            # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
            image_np_expanded = np.expand_dims(image_np, axis=0)
            image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')

            # Each box represents a part of the image where a particular object was detected.
            boxes = detection_graph.get_tensor_by_name('detection_boxes:0')

            # Each score represent how level of confidence for each of the objects.
            # Score is shown on the result image, together with the class label.
            scores = detection_graph.get_tensor_by_name('detection_scores:0')
            classes = detection_graph.get_tensor_by_name('detection_classes:0')
            num_detections = detection_graph.get_tensor_by_name('num_detections:0')

            # Actual detection.
            (boxes, scores, classes, num_detections) = sess.run(
                [boxes, scores, classes, num_detections],
                feed_dict={image_tensor: image_np_expanded})
                # 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=8)

            cv2.imshow('image',cv2.resize(image_np,(1280,960)))
            if cv2.waitKey(25) & 0xFF == ord('q'):
                cv2.destroyAllWindows()
                cap.release()

                break
这是该代码的输出或错误:

TypeError                                 Traceback (most recent call last)
<ipython-input-22-7fd26fbacd23> in <module>()
     21             (boxes, scores, classes, num_detections) = sess.run(
     22                 [boxes, scores, classes, num_detections],
---> 23                 feed_dict={image_tensor: image_np_expanded})
     24                 # feed_dict=[image_tensor, image_np_expanded])
     25 

2 frames
/usr/local/lib/python3.6/dist-packages/numpy/core/_asarray.py in asarray(a, dtype, order)
     83 
     84     """
---> 85     return array(a, dtype, copy=False, order=order)
     86 
     87 

TypeError: int() argument must be a string, a bytes-like object or a number, not 'NoneType'
TypeError回溯(最近一次调用)
在()
21(框、分数、类别、数量检测)=sess.run(
22[方框、分数、等级、数量检测],
--->23 feed_dict={image_tensor:image_np_expanded})
24#feed_dict=[图像张量,图像np_展开])
25
2帧
/asarray中的usr/local/lib/python3.6/dist-packages/numpy/core//\u asarray.py(a,数据类型,顺序)
83
84     """
--->85返回数组(a,数据类型,副本=False,顺序=order)
86
87
TypeError:int()参数必须是字符串、类似字节的对象或数字,而不是“NoneType”

为了更好地理解,请从上面的链接中查看我在google colab中的完整项目。

错误是因为没有提供图像。由于您无法直接从google colab打开网络摄像头,请查看如何执行此操作的答案。

嗨,Zain。表单中的问题“这是我的外部代码运行程序,请为我修复它。”“我们不在这里讨论这个话题。它有两个问题:(1)问题代码需要存在于问题本身中-欢迎外部代码运行者,但不要求他们理解问题,因为问题一解决,他们就会被修复;(2) 我们希望问题作者明白他们将完成大部分工作。