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detection_images.py
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detection_images.py
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from object_detection.utils import visualization_utils as vis_util
from object_detection.utils import label_map_util
from object_detection.utils import ops as utils_ops
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
import os
import glob
# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = './graphs/frozen_inference_graph.pb'
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = './graphs/label_map.pbtxt'
# Path to the images you want to infer
PATH_TO_TEST_IMAGES_DIR = './images'
assert os.path.isfile('./graphs/frozen_inference_graph.pb')
assert os.path.isfile(PATH_TO_LABELS)
TEST_IMAGE_PATHS = glob.glob(os.path.join(PATH_TO_TEST_IMAGES_DIR, "*.*"))
assert len(TEST_IMAGE_PATHS) > 0, 'No image found in `{}`.'.format(
PATH_TO_TEST_IMAGES_DIR)
try:
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=36, 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)
def get_files_on_directory(path):
directory = os.path.basename(path)
file_list = os.listdir(directory)
return file_list
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
count = 1
for image_path in TEST_IMAGE_PATHS:
image = Image.open(image_path)
print(image_path)
if '.jpg' not in image_path:
continue
if sys.platform == 'win32':
image_name = image_path.split('\\')[1].split('.')[0]
else:
image_name = image_path.split('/')[2].split('.')[0]
im_width, im_height = image.size
im_width_inche = im_width // 77
im_height_inche = im_height // 77 #redimensioning the image resolution
IMAGE_SIZE = (im_width_inche, im_height_inche)
# 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 e:[1 shap, 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=10,
file_name=image_name
)
plt.figure(figsize=IMAGE_SIZE)
plt.axis('off')
plt.imshow(image_np)
plt.savefig('./results/image_' + image_name + '.jpg', bbox_inches='tight')
count += 1
except Exception as error:
print(error)