resize image using backend;add resize radio button

frontend resize image will reduce image quality

main.py

@@ -4,6 +4,8 @@ import io
 import os
 import time
 import argparse
+from distutils.util import strtobool
+from typing import Union
 import cv2
 import numpy as np
 import torch
@@ -13,6 +15,8 @@ from flask_cors import CORS
 from lama_cleaner.helper import (
     download_model,
     load_img,
+    norm_img,
+    resize_max_size,
     numpy_to_bytes,
     pad_img_to_modulo,
 )
@@ -43,13 +47,38 @@ device = None
 def process():
     input = request.files
     image = load_img(input["image"].read())
+    original_shape = image.shape
+    interpolation = cv2.INTER_CUBIC
+
+    size_limit: Union[int, str] = request.form.get("sizeLimit", "1080")
+    if size_limit == "Original":
+        size_limit = max(image.shape)
+    else:
+        size_limit = int(size_limit)
+
+    print(f"Origin image shape: {original_shape}")
+    image = resize_max_size(image, size_limit=size_limit, interpolation=interpolation)
+    print(f"Resized image shape: {image.shape}")
+    image = norm_img(image)
+
     mask = load_img(input["mask"].read(), gray=True)
+    mask = resize_max_size(mask, size_limit=size_limit, interpolation=interpolation)
+    mask = norm_img(mask)
+
     res_np_img = run(image, mask)
+
+    # resize to original size
+    res_np_img = cv2.resize(
+        res_np_img,
+        dsize=(original_shape[1], original_shape[0]),
+        interpolation=interpolation,
+    )
+
     return send_file(
         io.BytesIO(numpy_to_bytes(res_np_img)),
-        mimetype="image/png",
+        mimetype="image/jpeg",
         as_attachment=True,
-        attachment_filename="result.png",
+        attachment_filename="result.jpeg",
     )
 
 
@@ -61,6 +90,8 @@ def index():
 def run(image, mask):
     """
     image: [C, H, W]
+    mask: [1, H, W]
+    return: BGR IMAGE
     """
     origin_height, origin_width = image.shape[1:]
     image = pad_img_to_modulo(image, mod=8)
@@ -73,13 +104,11 @@ def run(image, mask):
     start = time.time()
     inpainted_image = model(image, mask)
 
-    print(
-        f"inpainted image shape: {inpainted_image.shape} process time: {(time.time() - start)*1000}ms"
-    )
+    print(f"process time: {(time.time() - start)*1000}ms")
     cur_res = inpainted_image[0].permute(1, 2, 0).detach().cpu().numpy()
     cur_res = cur_res[0:origin_height, 0:origin_width, :]
     cur_res = np.clip(cur_res * 255, 0, 255).astype("uint8")
-    cur_res = cv2.cvtColor(cur_res, cv2.COLOR_RGB2BGR)
+    cur_res = cv2.cvtColor(cur_res, cv2.COLOR_BGR2RGB)
     return cur_res
 
 
@@ -87,6 +116,7 @@ def get_args_parser():
     parser = argparse.ArgumentParser()
     parser.add_argument("--port", default=8080, type=int)
     parser.add_argument("--device", default="cuda", type=str)
+    parser.add_argument("--debug", action="store_true")
     return parser.parse_args()
 
 
@@ -98,7 +128,7 @@ def main():
     model_path = download_model()
     model = torch.jit.load(model_path, map_location="cpu")
     model = model.to(device)
-    app.run(host="0.0.0.0", port=args.port, debug=False)
+    app.run(host="0.0.0.0", port=args.port, debug=args.debug)
 
 
 if __name__ == "__main__":