def process():
input_path = generate_random_filename(upload_directory,"jpg")
output_path = generate_random_filename(upload_directory,"jpg")
try:
url = request.json["url"]
# phone: iphone, blackberr or sony
phone = request.json["phone"]
# resolution: orig,high,medium,small,tiny
resolution = request.json["resolution"]
download(url, input_path)
# get the specified image resolution
IMAGE_HEIGHT, IMAGE_WIDTH, IMAGE_SIZE = utils.get_specified_res(res_sizes, phone, resolution)
# create placeholders for input images
x_ = tf.placeholder(tf.float32, [None, IMAGE_SIZE])
x_image = tf.reshape(x_, [-1, IMAGE_HEIGHT, IMAGE_WIDTH, 3])
# generate enhanced image
enhanced = resnet(x_image)
with tf.Session(config=config) as sess:
saver = tf.train.Saver()
saver.restore(sess, "models_orig/" + phone + "_orig")
image = np.float16(misc.imresize(misc.imread(filename), res_sizes[phone])) / 255
image_crop = utils.extract_crop(image, resolution, phone, res_sizes)
image_crop_2d = np.reshape(image_crop, [1, IMAGE_SIZE])
enhanced_2d = sess.run(enhanced, feed_dict={x_: image_crop_2d})
enhanced_image = np.reshape(enhanced_2d, [IMAGE_HEIGHT, IMAGE_WIDTH, 3])
misc.imsave(filename, enhanced_image)
callback = send_file(output_path, mimetype='image/jpeg')
return callback, 200
except:
traceback.print_exc()
return {'message': 'input error'}, 400
finally:
clean_all([
input_path,
output_path
])
# load pre-trained model
saver = tf.train.Saver()
saver.restore(sess, "models_orig/" + phone)
for photo in test_photos:
# load training image and crop it if necessary
print("Testing original " + phone.replace("_orig", "") +
" model, processing image " + photo)
image = np.float16(
misc.imresize(misc.imread(test_dir + photo),
res_sizes[phone])) / 255
image_crop = utils.extract_crop(image, resolution, phone,
res_sizes)
image_crop_2d = np.reshape(image_crop, [1, IMAGE_SIZE])
# get enhanced image
enhanced_2d = sess.run(enhanced, feed_dict={x_: image_crop_2d})
enhanced_image = np.reshape(enhanced_2d,
[IMAGE_HEIGHT, IMAGE_WIDTH, 3])
before_after = np.hstack((image_crop, enhanced_image))
photo_name = photo.rsplit(".", 1)[0]
# save the results as .png images
misc.imsave(
"visual_results/" + phone + "_" + photo_name + "_enhanced.png",
def input_generator(filesDIR):
batch_rois = []
batch_inds = []
batch_fmaps = []
image_counter = 0
batch_scores = []
batch_targets = []
scan_counter = 0
while 1:
print('scans: ', scan_counter)
for f in listdir(filesDIR):
scan_counter += 1
data = asarray(Image.open(filesDIR + f)) / 255.0
feature_map_for_RoIPool = CNN_model_RoI.predict(
data.reshape(-1, data.shape[0], data.shape[1], data.shape[2]))
feature_stride_for_ROIPool = int(image_size /
feature_map_for_RoIPool.shape[1])
# normalization
feature_map_mean = np.mean(feature_map_for_RoIPool)
feature_map_std = np.std(feature_map_for_RoIPool)
feature_map_for_RoIPool = (feature_map_for_RoIPool -
feature_map_mean) / feature_map_std
del data
true_bboxes = np.array([
row[3] / r for row in csv_data.values
if row[0] == f.replace('.jpg', '')
])
if len(true_bboxes) == 0:
continue
true_bboxes = xywh_xyXY(true_bboxes)
enlarged_bboxes = enlarge(true_bboxes, bbox_padding=15)
rois, targets, scores = produce_batch(filesDIR + f, true_bboxes)
if len(rois) <= 0:
continue
batch_fmaps.append(feature_map_for_RoIPool[0])
image_counter += 1
for i in range(len(rois)):
crop = extract_crop(rois[i])
batch_rois.append(crop)
batch_inds.append(int(image_counter - 1))
batch_scores.append(scores[i])
batch_targets.append(targets[i])
if (len(batch_rois) == BATCH_SIZE):
all_fmaps = np.zeros(
(len(batch_rois), feature_map_for_RoIPool.shape[1],
feature_map_for_RoIPool.shape[2],
feature_map_for_RoIPool.shape[3]))
# The input must share first dimension. We thus fill the batch of fmaps with zeros
for useful_ind in range(image_counter):
all_fmaps[useful_ind] = batch_fmaps[useful_ind]
batch_pooled_rois = tf.image.crop_and_resize(
np.asarray(all_fmaps), np.asarray(batch_rois),
np.asarray(batch_inds), (RoI_Pool_size, RoI_Pool_size))
if not a.any() or not b.any() or not c.any() or not d.any(
):
print("empty array found.")
yield batch_pooled_rois, [
np.asarray(batch_targets),
to_categorical(np.asarray(batch_scores))
]
batch_rois = []
batch_inds = []
batch_scores = []
batch_targets = []
batch_fmaps = []
image_counter = 0
# Scanning of rois in image continues if scan over rois hasnt ended. We need to keep the corresponding fmap!
if (i < len(rois) - 1):
batch_fmaps.append(feature_map_for_RoIPool[0])
image_counter += 1
