ValueError: Cannot feed value of shape (1,450，300) for Tensor which has shape '(？,?,?,3)'解决方案

最新推荐文章于 2023-10-20 09:13:10 发布

原创最新推荐文章于 2023-10-20 09:13:10 发布 · 1.6k 阅读

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本文介绍了一种使用SSD(SSingle Shot MultiBox Detector)进行目标检测的方法，详细展示了如何加载预训练模型，处理图像输入，并在图像上可视化检测结果。核心步骤包括：模型加载、图像预处理、运行检测及结果可视化。

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测试中遇到的问题，直接上代码，核心在53-55行，先通过cvtcolor转换通道，然后用PIL从numpy转换为PIL类型图像

 	import numpy as np
    import os
    import six.moves.urllib as urllib
    import sys
    import tarfile
    import tensorflow as tf
    import zipfile
    from collections import defaultdict
    from io import StringIO
    from matplotlib import pyplot as plt
    from PIL import Image
    sys.path.append("..")
    from utils import label_map_util
    from utils import visualization_utils as vis_util
    import cv2
    from timeit import default_timer as timer
    PATH_TO_CKPT = '/media/wxy/0007C67F000018F2/SSD_HHC/output92001/frozen_inference_graph.pb'
    PATH_TO_LABELS = '/media/wxy/0007C67F000018F2/SSD_HHC/dataset/pascal_label_map.pbtxt'
    
#os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
#os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
os.environ["CUDA_VISIBLE_DEVICES"] = "1"

NUM_CLASSES =2
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='')

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)
print(category_index)
'''
def load_image_into_numpy_array(image): 
  #image = Image.merge('RGB',(image,image,image))
  img = np.array(image)
  #print(image.size)
  return img
'''

IMAGE_SIZE = (12, 8) 

with detection_graph.as_default(): 
  with tf.Session(graph=detection_graph) as sess: 
    for image_path in os.listdir('SSD_image'): 
        start = timer()
        image = Image.open('./SSD_image'+'/'+image_path)

        image_np1 = np.array(image)
        image1=cv2.cvtColor(image_np1,cv2.COLOR_GRAY2BGR)
        pilimg=Image.fromarray(image1)

        image_np = np.array(pilimg)
        image_np_expanded = np.expand_dims(image_np, axis=0) 

        

        image_tensor = detection_graph.get_tensor_by_name('image_tensor:0') 
        boxes = detection_graph.get_tensor_by_name('detection_boxes:0')  
        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') 
        (boxes, scores, classes, num_detections) = sess.run(
            [boxes, scores, classes, num_detections], 
            feed_dict={image_tensor: image_np_expanded})
        end = timer()
        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) 
        (r, g, b)=cv2.split(image_np)
        image_np=cv2.merge([b,g,r])
        
        print(end - start)
        cv2.imwrite('./SSD_result_920'+'/'+image_path,image_np)