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December 29, 2017 07:21
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Extracting detection features from tensorflow object detection API.
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"""This file extracts faster-rcnn features and bounding box coordinates""" | |
import pdb | |
import argparse | |
import numpy as np | |
import tensorflow as tf | |
import PIL.Image as PILI | |
def session(sess, feat_conv, feat_avg, boxes, classes, scores, image_tensor, image): | |
feat_conv_out, feat_avg_out, boxes_out, classes_out, scores_out = sess.run([ | |
feat_conv, feat_avg, boxes, classes, scores], feed_dict={image_tensor: image}) | |
feat_conv_out = feat_conv_out.squeeze() | |
feat_avg_out = feat_avg_out.squeeze() | |
boxes_out = boxes_out.squeeze() | |
classes_out = classes_out.squeeze().astype(np.int32) | |
scores_out = scores_out.squeeze() | |
return feat_conv_out, feat_avg_out, boxes_out, classes_out, scores_out | |
def load_graph(graph, ckpt_path): | |
with graph.as_default(): | |
od_graph_def = tf.GraphDef() | |
with tf.gfile.GFile(ckpt_path, 'rb') as fid: | |
serialized_graph = fid.read() | |
od_graph_def.ParseFromString(serialized_graph) | |
tf.import_graph_def(od_graph_def, name='') | |
if __name__ == '__main__': | |
parser = argparse.ArgumentParser() | |
parser.add_argument('--img', metavar='', type=str, default=None, help='Image path.') | |
parser.add_argument('--model', metavar='', type=str, default='frcnn_res101', help='frcnn_incresv2 or frcnn_res101.') | |
args, unparsed = parser.parse_known_args() | |
if len(unparsed) != 0: raise SystemExit('Unknown argument: {}'.format(unparsed)) | |
graph = tf.Graph() | |
if args.model == 'frcnn_incresv2': | |
ckpt_path = './faster_rcnn_inception_resnet_v2_atrous_coco_2017_11_08/frozen_inference_graph.pb' | |
load_graph(graph, ckpt_path) | |
# (1, ?, ?, 3) | |
image_tensor = graph.get_tensor_by_name('image_tensor:0') | |
# (100, 8, 8, 1536) | |
feat_conv = graph.get_tensor_by_name('SecondStageFeatureExtractor/InceptionResnetV2/Conv2d_7b_1x1/Relu:0') | |
# (100, 1, 1, 1536) | |
feat_avg = graph.get_tensor_by_name('SecondStageBoxPredictor/AvgPool:0') | |
elif args.model == 'frcnn_res101': | |
ckpt_path = './faster_rcnn_resnet101_coco_2017_11_08/frozen_inference_graph.pb' | |
load_graph(graph, ckpt_path) | |
# (1, ?, ?, 3) | |
image_tensor = graph.get_tensor_by_name('image_tensor:0') | |
# (100, 7, 7, 2048) | |
feat_conv = graph.get_tensor_by_name('SecondStageFeatureExtractor/resnet_v1_101/block4/unit_3/bottleneck_v1/Relu:0') | |
# (100, 1, 1, 2048) | |
feat_avg = graph.get_tensor_by_name('SecondStageBoxPredictor/AvgPool:0') | |
else: | |
raise SystemExit('Unknown model: {}'.format(args.model)) | |
boxes = graph.get_tensor_by_name('detection_boxes:0') | |
scores = graph.get_tensor_by_name('detection_scores:0') | |
classes = graph.get_tensor_by_name('detection_classes:0') | |
print ('model: {}'.format(args.model)) | |
# Load tf model into memory | |
config = tf.ConfigProto() | |
config.gpu_options.allow_growth = True | |
sess = tf.Session(config=config, graph=graph) | |
print ('Detect a single image') | |
# Load image | |
image = PILI.open(args.img) | |
image = np.asarray(image) | |
# Run session | |
feat_conv, feat_avg, boxes, classes, scores = session( | |
sess, feat_conv, feat_avg, boxes, classes, scores, image_tensor, np.expand_dims(image, 0)) | |
print ('Done') |
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I was using "get_tensor_by_name('SecondStageBoxPredictor/AvgPool:0')" and achieve to feature vector with dim=2048 for each box but I don't know this feature vectors are discriminative in which space? euclidean space or cosine space or another space?