def worker_super_resolution_one(image,
new_heigth=None,
new_width=None,
save_filename="huey_SR_test"):
current_heigth, current_width = image.shape[:2]
resized_image = image
if new_heigth is not None:
if new_width is not None:
resized_image = cv2.resize(image, (new_width, new_heigth))
else:
resized_image = cv2.resize(image, (current_width, new_heigth))
else:
if new_width is not None:
resized_image = cv2.resize(image, (new_width, current_heigth))
logging.info("Image has been resized from {} to {}".format(
image.shape, resized_image.shape))
processor = ImageProcessor()
model = get_model_by_name("super_resolution_model")
new_image = processor.process_image(image=resized_image,
model=model,
overlay=20)
cv2.imwrite("./processed_images/{}.png".format(save_filename), new_image)
return new_image
def worker_general(image, model_name, save_filename="bad_filename"):
model = get_model_by_name(model_name)
processor = ImageProcessor()
new_image = processor.process_image(image=image, model=model, overlay=20)
cv2.imwrite("./processed_images/{}.png".format(save_filename), new_image)
return new_image
def test_image_processing(self):
test_image = cv2.imread("./test_data/test_image.jpg")[:224, :224, :]
proc = ImageProcessor()
name = "super_resolution_model"
with open(join(ROOT_DIR, 'GAN/Models/{}.json'.format(name)),
'r') as json_file:
model = model_from_json(json_file.read())
model.load_weights(join(ROOT_DIR, "GAN/Models/{}.h5".format(name)))
model._make_predict_function()
result = proc.process_image(image=test_image, model=model, overlay=20)
processed_test_image = cv2.imread(
"./test_data/test_image_processed.png")
self.assertListEqual(list(result[100][100]),
list(processed_test_image[100][100]))
'r') as json_file:
loaded_model = model_from_json(json_file.read())
loaded_model.load_weights(
"E:/Licenta/Licenta/GAN//Models/super_resolution_model.h5")
# loaded_model.summary()
global model_graph
model_graph = tf.get_default_graph()
# Test on image
processor = ImageProcessor()
image = cv2.imread("E:/AiDatasets/Licenta/Test/data/test1.jpg")
cv2.imwrite("E:/AiDatasets/Licenta/Test/results/test1_3_original.jpg",
image)
img_h, img_w, _ = image.shape
image = cv2.resize(image, (img_w // 4, img_h // 4))
image = cv2.resize(image, (img_w, img_h))
cv2.imwrite("E:/AiDatasets/Licenta/Test/results/test1_1_downRes.jpg",
image)
new_image = processor.process_image(image=image,
model=loaded_model,
model_graph=model_graph,
overlay=0)
cv2.imwrite("E:/AiDatasets/Licenta/Test/results/test1_2_generated.jpg",
new_image)