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 from object_detection.utils import label_map_util from object_detection.utils import visualization_utils as vis_util
# 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 = 'workspace/training_demo/annotations/label_map.pbtxt'
# Number of classes to detect NUM_CLASSES = 5
# Load a (frozen) Tensorflow model into memory. detection_graph = tf.Graph() with detection_graph.as_default(): od_graph_def = tf.compat.v1.GraphDef() with tf.io.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 maps map indices to category names, so that when our convolution network predicts `5`, we know that this corresponds to `airplane`. Here we use internal utility functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine 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)
data_dir = 'workspace/training_demo/test_data'
# Detection with detection_graph.as_default(): with tf.compat.v1.Session(graph=detection_graph) as sess: for filename in os.listdir(data_dir): image_np = np.array(Image.open(os.path.join(data_dir, filename))) # Expand dimensions since the model expects images to have shape: [1, None, None, 3] image_np_expanded = np.expand_dims(image_np, axis=0) # Extract image tensor image_tensor = detection_graph.get_tensor_by_name('image_tensor:0') # Extract detection boxes boxes = detection_graph.get_tensor_by_name('detection_boxes:0') # Extract detection scores scores = detection_graph.get_tensor_by_name('detection_scores:0') # Extract detection classes classes = detection_graph.get_tensor_by_name('detection_classes:0') # Extract number of detectionsd 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)
