Created
February 15, 2018 15:52
-
-
Save AbhishekAshokDubey/7a18f031b23b98bb40fe3b29ad3f96f3 to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
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 | |
od_folder = "/home/adubey4/tf_models/models/object_detection" | |
model_folder = "/home/adubey4/tf_models/models" | |
sys.path.append(od_folder) | |
sys.path.append(model_folder) | |
from utils import label_map_util | |
from utils import visualization_utils as vis_util | |
MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017' | |
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' | |
# List of the strings that is used to add correct label for each box. | |
PATH_TO_LABELS = os.path.join(od_folder, 'data', 'mscoco_label_map.pbtxt') | |
NUM_CLASSES = 90 | |
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()) | |
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) | |
# For the sake of simplicity we will use only 2 images: | |
# image1.jpg | |
# image2.jpg | |
# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS. | |
PATH_TO_TEST_IMAGES_DIR = os.path.join(od_folder,'test_images') | |
#PATH_TO_TEST_IMAGES_DIR = 'test_images' | |
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 3) ] | |
# Size, in inches, of the output images. | |
IMAGE_SIZE = (12, 8) | |
from PIL import Image | |
#im = Image.fromarray(A) | |
#im.save("your_file.jpeg") | |
with detection_graph.as_default(): | |
with tf.Session(graph=detection_graph) as sess: | |
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) | |
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. | |
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. | |
scores = detection_graph.get_tensor_by_name('detection_scores:0') | |
classes = detection_graph.get_tensor_by_name('detection_classes:0') | |
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) | |
#plt.figure(figsize=IMAGE_SIZE) | |
im = Image.fromarray(image_np) | |
#im.save(os.path.join(od_folder,image_path)) | |
print(image_path) | |
im.save(image_path.split("/")[-1]) | |
#plt.imshow(image_np) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment