我是TensorFlow的新手,某些图片出现此评估错误,而在其他图片上工作正常。代码有什么问题吗?
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
import zipfile
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util
# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")
MODEL_NAME = 'new_graph' # change to whatever folder has the new graph
# MODEL_FILE = MODEL_NAME + '.tar.gz' # these lines not needed as we are using our own model
# DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('training', 'object-detection.pbtxt') # our labels are in training/object-detection.pbkt
NUM_CLASSES = 3
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)
def load_image_into_numpy_array(image):
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape(
(im_height, im_width, 4)).astype(np.uint8)
PATH_TO_TEST_IMAGES_DIR = 'test_images'
TEST_IMAGE_PATHS = [os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.png'.format(i)) for i in range(1, 8)]
# adjust range for # of images in folder
# Size, in inches, of the output images.
IMAGE_SIZE = (12, 8)
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
i = 0
for image_path in TEST_IMAGE_PATHS:
image = Image.open(image_path)
# the array based representation of the image will be used later in order to prepare the
# result image with boxes and labels on it.
# # image = np.reshape(image, ())
# print(image)
image_np = load_image_into_numpy_array(image)
# 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})
# 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)
plt.figure(figsize=IMAGE_SIZE)
plt.imshow(image_np) # matplotlib is configured for command line only so we save the outputs instead
plt.savefig("outputs/detection_output{}.png".format(i)) # create an outputs folder for the images to be saved
i = i+1 # this was a quick fix for iteration, create a pull request if you'd like
错误:
追溯(最近一次通话):文件“ custom_model_images.py”,第82行,位于feed_dict = {image_tensor:image_np_expanded}中)
在运行run_metadata_ptr中,文件“ C:\ Python36 \ lib \ site-packages \ tensorflow_core \ python \ client \ session.py”,第956行)
_run(np_val.shape,subfeed_t.name,str(subfeed_t.get_shape()))中的文件“ C:\ Python36 \ lib \ site-packages \ tensorflow_core \ python \ client \ session.py”,行1156
ValueError:无法为张量'image_tensor:0'提供形状((?,?,?,3)'的形状(1、233、472、4)值
看起来您的某些图像具有三个通道,其他图像-四个通道(可能带有alpha通道)。您的模型需要三个渠道
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