def get_request():
img = request.data.decode('utf-8')
img = json.loads(img)
image_string = img["image"]
angle = int(img["angle"])
base = str(image_string).replace("data:image/jpeg;base64,", "")
imgdata = base64.b64decode(base)
imgdata = cv2.imdecode(np.frombuffer(imgdata, np.uint8), -1)
imgdata = cv2.cvtColor(imgdata, cv2.COLOR_BGR2RGB)
imgdata = crop_largest_rectangle(imgdata, angle, *imgdata.shape[:2])
imgdata = Image.fromarray(imgdata)
path_file = app.config['UPLOAD_FILE']
imgdata.save(path_file)
buffered = BytesIO()
rotated_image, predicted_angle = predict(model_ver_1, path_file)
rotated_image = Image.fromarray(rotated_image, 'RGB')
rotated_image.save(buffered, format="JPEG")
return jsonify({
'angle':
360 - int(predicted_angle),
'image':
"data:image/jpeg;base64," +
str(base64.b64encode(buffered.getvalue()).decode("ascii"))
}), 200
def process_images(model, input_path, output_path,
batch_size=64, crop=True):
extensions = ['.jpg', '.jpeg', '.bmp', '.png']
output_is_image = False
if os.path.isfile(input_path):
image_paths = [input_path]
if os.path.splitext(output_path)[1].lower() in extensions:
output_is_image = True
output_filename = output_path
output_path = os.path.dirname(output_filename)
else:
image_paths = [os.path.join(input_path, f)
for f in os.listdir(input_path)
if os.path.splitext(f)[1].lower() in extensions]
if os.path.splitext(output_path)[1].lower() in extensions:
print('Output must be a directory!')
predictions = model.predict_generator(
RotNetDataGenerator(
image_paths,
input_shape=(224, 224, 3),
batch_size=64,
one_hot=True,
preprocess_func=preprocess_input,
rotate=False,
crop_largest_rect=True,
crop_center=True
),
val_samples=len(image_paths)
)
predicted_angles = np.argmax(predictions, axis=1)
if output_path == '':
output_path = '.'
if not os.path.exists(output_path):
os.makedirs(output_path)
for path, predicted_angle in zip(image_paths, predicted_angles):
image = cv2.imread(path)
rotated_image = rotate(image, -predicted_angle)
if crop:
size = (image.shape[0], image.shape[1])
rotated_image = crop_largest_rectangle(rotated_image, -predicted_angle, *size)
if not output_is_image:
output_filename = os.path.join(output_path, os.path.basename(path))
cv2.imwrite(output_filename, rotated_image)
def predict(model, input_path, batch_size=64, crop=True):
predictions = model.predict_generator(RotNetDataGenerator(
[input_path],
input_shape=(224, 224, 3),
batch_size=64,
one_hot=True,
preprocess_func=preprocess_input,
rotate=False,
crop_largest_rect=True,
crop_center=True),
val_samples=1)
predicted_angle = np.argmax(predictions, axis=1)[0]
image = cv2.imread(input_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
rotated_image = rotate(image, -predicted_angle)
if crop:
size = (image.shape[0], image.shape[1])
rotated_image = crop_largest_rectangle(rotated_image, -predicted_angle,
*size)
return rotated_image, predicted_angle