def test(filename):
"""
Test function: runs pipeline for a small set of input images and input
brands.
"""
yolo = YOLO(**{"model_path": 'keras_yolo3/yolo_weights_logos.h5',
"anchors_path": 'keras_yolo3/model_data/yolo_anchors.txt',
"classes_path": 'data_classes.txt',
"score" : 0.05,
"gpu_num" : 1,
"model_image_size" : (416, 416),
}
)
save_img_logo, save_img_match = True, True
test_dir = os.path.join(os.path.dirname(__file__), os.path.pardir, 'data/test')
# get Inception/VGG16 model and flavor from filename
model_name, flavor = model_flavor_from_name(filename)
## load pre-processed features database
features, brand_map, input_shape = load_features(filename)
## load inception model
model, preprocess_input, input_shape = load_extractor_model(model_name, flavor)
my_preprocess = lambda x: preprocess_input(utils.pad_image(x, input_shape).astype(np.float32))
## load sample images of logos to test against
input_paths = ['test_batman.jpg', 'test_robin.png', 'test_lexus.png', 'test_champions.jpg',
'test_duff.jpg', 'test_underarmour.jpg', 'test_golden_state.jpg']
input_labels = [ s.split('test_')[-1].split('.')[0] for s in input_paths]
input_paths = [os.path.join(test_dir, 'test_brands/', p) for p in input_paths]
# compute cosine similarity between input brand images and all LogosInTheWild logos
( img_input, feat_input, sim_cutoff, (bins, cdf_list)
) = load_brands_compute_cutoffs(input_paths, (model, my_preprocess), features, sim_threshold, timing=True)
images = [ p for p in os.listdir(os.path.join(test_dir, 'sample_in/')) if p.endswith('.jpg')]
images_path = [ os.path.join(test_dir, 'sample_in/',p) for p in images]
start = timer()
times_list = []
img_size_list = []
candidate_len_list = []
for i, img_path in enumerate(images_path):
outtxt = img_path
## find candidate logos in image
prediction, image = detect_logo(yolo, img_path, save_img = True,
save_img_path = test_dir, postfix='_logo')
## match candidate logos to input
outtxt, times = match_logo(image, prediction, (model, my_preprocess),
outtxt, (feat_input, sim_cutoff, bins, cdf_list, input_labels),
save_img = save_img_match, save_img_path=test_dir, timing=True)
img_size_list.append(np.sqrt(np.prod(image.size)))
candidate_len_list.append(len(prediction))
times_list.append(times)
end = timer()
print('Processed {} images in {:.1f}sec - {:.1f}FPS'.format(
len(images_path), end-start, len(images_path)/(end-start)
))
fig, axes = plt.subplots(1,2, figsize=(9,4))
for iax in range(2):
for i in range(len(times_list[0])):
axes[iax].scatter([candidate_len_list, img_size_list][iax], np.array(times_list)[:,i])
axes[iax].legend(['read img','get box','get features','match','draw','save'])
axes[iax].set(xlabel=['number of candidates', 'image size'][iax], ylabel='Time [sec]')
plt.savefig(os.path.join(test_dir, 'timing_test.png'))
features, sim_threshold)
start = timer()
# cycle trough input images, look for logos and then match them against inputs
text_out = ''
for i, img_path in enumerate(FLAGS.input_images):
text = img_path
prediction, image = detect_logo(yolo,
img_path,
save_img=save_img_logo,
save_img_path=FLAGS.output,
postfix='_logo')
text = match_logo(
image,
prediction, (model, my_preprocess),
text, (feat_input, sim_cutoff, bins, cdf_list, input_labels),
save_img=save_img_match,
save_img_path=FLAGS.output)
print(text)
text_out += text
if FLAGS.save_to_txt:
with open(output_txt, 'w') as txtfile:
txtfile.write(text_out)
end = timer()
print('Processed {} images in {:.1f}sec - {:.1f}FPS'.format(
len(FLAGS.input_images), end - start,
len(FLAGS.input_images) / (end - start)))
# video mode