Implementation conditions:
1.win7
2.python
3. Libraries required for operation: matplotlib, lxml, pillow, Cython
Specific reference: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md
4. object_detection package, download address: https://github.com/tensorflow/models
After downloading, unzip the object_detection location in the research folder
5. The compiled protos file, the files in object_detection are not compiled, the compiled file download address: https://github.com/1529591487/Object-Detection
Just replace the protos folder in object_detection directly.
Code:
1.
import numpy as np import os import six.moves.urllib as urllib import sys import tarfile import tensorflow as tf import zipfile from collections import defaultdict from io import StringIO from matplotlib import pyplot as plt from PIL import Image #Change here to the location of the object_detection package you downloaded sys.path.append(r " E:\learning materials\artificial intelligence\models-master\research " ) from object_detection.utils import ops as utils_ops if tf.__version__ < '1.4.0': raise ImportError('Please upgrade your tensorflow installation to v1.4.* or later!')
2.
%matplotlib inline
3.
from object_detection.utils import label_map_util from object_detection.utils import visualization_utils as vis_util
There will be a warning here, but it will not affect
4.
# What model to download. MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17' MODEL_FILE = MODEL_NAME + '.tar.gz' DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/' # 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' #The path here also needs to be modified PATH_TO_LABELS = os.path.join(r ' E:\learning materials\artificial intelligence\models-master\research\object_detection\data ' , ' mscoco_label_map.pbtxt ' ) NUM_CLASSES = 90
5.
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())
6.
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='')
7.
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)
8.
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)
9.
def run_inference_for_single_image(image, graph): with graph.as_default(): with tf.Session() as sess: # Get handles to input and output tensors ops = tf.get_default_graph().get_operations() all_tensor_names = {output.name for op in ops for output in op.outputs} tensor_dict = {} for key in [ 'num_detections', 'detection_boxes', 'detection_scores', 'detection_classes', 'detection_masks' ]: tensor_name = key + ':0' if tensor_name in all_tensor_names: tensor_dict[key] = tf.get_default_graph().get_tensor_by_name( tensor_name) if 'detection_masks' in tensor_dict: # The following processing is only for single image detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0]) detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0]) # Reframe is required to translate mask from box coordinates to image coordinates and fit the image size. real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32) detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1]) detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1]) detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks( detection_masks, detection_boxes, image.shape[0], image.shape[1]) detection_masks_reframed = tf.cast( tf.greater(detection_masks_reframed, 0.5), tf.uint8) # Follow the convention by adding back the batch dimension tensor_dict['detection_masks'] = tf.expand_dims( detection_masks_reframed, 0) image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0') # Run inference output_dict = sess.run(tensor_dict, feed_dict={image_tensor: np.expand_dims(image, 0)}) # all outputs are float32 numpy arrays, so convert types as appropriate output_dict['num_detections'] = int(output_dict['num_detections'][0]) output_dict['detection_classes'] = output_dict[ 'detection_classes'][0].astype(np.uint8) output_dict['detection_boxes'] = output_dict['detection_boxes'][0] output_dict['detection_scores'] = output_dict['detection_scores'][0] if 'detection_masks' in output_dict: output_dict['detection_masks'] = output_dict['detection_masks'][0] return output_dict
10.
IMAGE_SIZE = (36, 24 ) #set the image path here mydir=r ' E:\learning materials\artificial intelligence\models-master\research\object_detection\test_images ' # mydir = 'G : \wallpaper' for filename in os.listdir (mydir): if os.path.splitext(filename)[1] == ' .jpg ' : filepath=os.path.join(mydir, filename) print(filepath) image = Image.open(filepath) # 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. output_dict = run_inference_for_single_image(image_np, detection_graph) # Visualization of the results of a detection. vis_util.visualize_boxes_and_labels_on_image_array( image_np, output_dict['detection_boxes'], output_dict['detection_classes'], output_dict['detection_scores'], category_index, instance_masks=output_dict.get('detection_masks'), use_normalized_coordinates=True, line_thickness=8) fig1 = plt.gcf() plt.figure(figsize=IMAGE_SIZE) plt.imshow(image_np)
operation result:
Code reference: https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb
Some image recognition will fail, and I have not figured it out yet. Welcome to communicate.