tensorflow高效地推导pb模型,完整代码
from matplotlib import pyplot as plt import numpy as np import os import six.moves.urllib as urllib import sys import tarfile import tensorflow as tf import zipfile import glob from collections import defaultdict from io import StringIO from PIL import Image import DrawBox import cv2 from object_detection.utils import label_map_util from object_detection.utils import visualization_utils as vis_util SaveFilename='D:/FasterR-CNNImageTest/11/' if not os.path.exists(SaveFilename): os.mkdir(SaveFilename) with tf.device('/cpu:0'): cap = cv2.VideoCapture(0) PATH_TO_CKPT = 'pb/frozen_inference_graph.pb' PATH_TO_LABELS = os.path.join('dataset', 'pascal_label_map.pbtxt') NUM_CLASSES = 2 # 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='') 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, 3)).astype(np.uint8) # # Detection cnt = 0 PATH_TO_TEST_IMAGES_DIR = 'E:/PythonOpenCVCode/BalanceGeneratePicture/TestSetSaveImage/Test/JPEGImages' with detection_graph.as_default(): with tf.Session(graph=detection_graph) as sess: # while True: # for image_path in TEST_IMAGE_PATHS: #changed 20170825 # 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 pidImage in glob.glob(PATH_TO_TEST_IMAGES_DIR + "/*.jpg"): TEST_IMAGE_PATHS = [os.path.join(PATH_TO_TEST_IMAGES_DIR, pidImage)] # Size, in inches, of the output images. IMAGE_SIZE = (12, 8) 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}) # Visualization of the results of a detection. # print(boxes) DrawBox.visualize_boxes_and_labels_on_image_array( image_np, np.squeeze(boxes), np.squeeze(classes).astype(np.int32), np.squeeze(scores), category_index, max_boxes_to_draw=400, use_normalized_coordinates=True, groundtruth_box_visualization_color='red', line_thickness=8) #plt.figure(figsize=IMAGE_SIZE) cv2.imwrite(SaveFilename + os.path.basename(image_path), image_np) # plt.imshow(image_np) cnt = cnt + 1 print(image_path)