檢測模型

在經過上萬次迭代訓練自己的樣本後，嘗試檢測模型；用相似環境下的圖片作為檢測樣本。
以下是檢測程式碼，copy自別處，修改自己的路徑一類，cmd下執行，或者在配置好環境的pycharm 下執行。

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.

# 訓練的模型，是資料夾名稱
MODEL_NAME = 'voc_inference_graphmatch'

# 對應的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')

# 改成自己例子中的類別數，我這邊是20
NUM_CLASSES = 20

'''
#Download Model
自己的模型，不需要下載了
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.
# 測試圖片位置
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})
#此處新增除錯程式碼
            # 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)
            # 儲存檔案
            out_path=output_path + image_path.split('\\')[-1]
            cv2.imwrite(out_path, image_np)

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

 

除錯尋找錯誤

不知道哪種原因，遲遲沒有檢測出結果，我也在納悶：就算是錯誤輸出也可以啊。有點懸，但是路還得繼續。

所以開啟了打斷點除錯功能。

斷點除錯基本就是左側打斷點，以debug模式執行，F8按行除錯，具體可尋找相關介紹。

還有一種除錯，就是中間關鍵資料輸出：
最先懷疑的就是檢測後資料是否小於閾值。
於是在檢測後資料做判斷，因此加了如下程式碼：

          
    #前邊程式碼註釋新增程式碼部分
             for i in range(len(scores)):
                if i <= 0.3:
                    continue
                print(i)
 

注意for迴圈遍歷的寫法，嘗試了幾種，這個沒有報錯。
這次沒有輸出邊框的原因的確是檢測後的scores均小於閾值，至於原因：

• 1、閾值設定過大；
• 2、模型訓練次數過少。