tensorflow objectdetecton API 检测模型不出结果

检测模型

在经过上万次迭代训练自己的样本后，尝试检测模型；用相似环境下的图片作为检测样本。
以下是检测代码，copy自别处，修改自己的路径一类，cmd下运行，或者在配置好环境的pycharm 下运行。

import matplotlib
matplotlib.use('Agg')

import time

start = time.time()
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
import cv2

from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

if tf.__version__ < '1.4.0':
    raise ImportError('Please upgrade your tensorflow installation to v1.4.* or later!')

os.chdir('D:\\python_objectdetection\\models-master\\models-master\\research\\object_detection')

# Env setup
# This is needed to display the images.
# %matplotlib inline

# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")

# Object detection imports
from utils import label_map_util

from utils import visualization_utils as vis_util

# Model preparation
# What model to download.

# 训练的模型，是文件夹名称
MODEL_NAME = 'voc_inference_graphmatch'

# 对应的Frozen model位置
# Path to frozen detection graph. This is the actual model that is used for the 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('data/voc', 'voc_label.pbtxt')
#PATH_TO_LABELS = os.path.join('data', 'person_car.pbtxt')

# 改成自己例子中的类别数，我这边是20
NUM_CLASSES = 20

'''
#Download Model
自己的模型，不需要下载了
opener = urllib.request.URLopener()
opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)
tar_file = tarfile.open(MODEL_FILE)
for file in tar_file.getmembers():
  file_name = os.path.basename(file.name)
  if 'frozen_inference_graph.pb' in file_name:
    tar_file.extract(file, os.getcwd())
'''

# 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)


# Helper code
def load_image_into_numpy_array(image):
    (im_width, im_height) = image.size
    return np.array(image.getdata()).reshape(
        (im_height, im_width, 3)).astype(np.uint8)


# Detection

# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.
# 测试图片位置
PATH_TO_TEST_IMAGES_DIR = os.getcwd() + '\\test_images'
os.chdir(PATH_TO_TEST_IMAGES_DIR)
TEST_IMAGE_PATHS = os.listdir(PATH_TO_TEST_IMAGES_DIR)

# Size, in inches, of the output images.
IMAGE_SIZE = (12, 8)

output_path = ('D:\\python_objectdetection\\models-master\\models-master\\research\\object_detection\\test_images_out\\')

with detection_graph.as_default():
    with tf.Session(graph=detection_graph) as sess:
        # Definite input and output Tensors for detection_graph
        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.
        detection_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.
        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')
        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 = 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)
            # Actual detection.
            (boxes, scores, classes, num) = sess.run(
                [detection_boxes, detection_scores, detection_classes, num_detections],
                feed_dict={image_tensor: image_np_expanded})
#此处添加调试代码
            # 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)
            # 保存文件
            out_path=output_path + image_path.split('\\')[-1]
            cv2.imwrite(out_path, image_np)

end = time.time()
print("Execution Time: ", end - start)

调试寻找错误

不知道哪种原因，迟迟没有检测出结果，我也在纳闷：就算是错误输出也可以啊。有点悬，但是路还得继续。

所以开启了打断点调试功能。

断点调试基本就是左侧打断点，以debug模式运行，F8按行调试，具体可寻找相关介绍。

还有一种调试，就是中间关键数据输出：
最先怀疑的就是检测后数据是否小于阈值。
于是在检测后数据做判断，因此加了如下代码：

          
    #前边代码注释添加代码部分
             for i in range(len(scores)):
                if i <= 0.3:
                    continue
                print(i)

注意for循环遍历的写法，尝试了几种，这个没有报错。
这次没有输出边框的原因的确是检测后的scores均小于阈值，至于原因：

• 1、阈值设置过大；
• 2、模型训练次数过少。