return correct file ext/mimetype

lama_cleaner/helper.py

@@ -30,8 +30,8 @@ def ceil_modulo(x, mod):
     return (x // mod + 1) * mod
 
 
-def numpy_to_bytes(image_numpy: np.ndarray) -> bytes:
+def numpy_to_bytes(image_numpy: np.ndarray, ext: str) -> bytes:
-    data = cv2.imencode(".jpg", image_numpy)[1]
+    data = cv2.imencode(f".{ext}", image_numpy)[1]
     image_bytes = data.tobytes()
     return image_bytes
 
@@ -92,7 +92,9 @@ def boxes_from_mask(mask: np.ndarray) -> List[np.ndarray]:
 
     """
     height, width = mask.shape[1:]
-    _, thresh = cv2.threshold((mask.transpose(1, 2, 0) * 255).astype(np.uint8), 127, 255, 0)
+    _, thresh = cv2.threshold(
+        (mask.transpose(1, 2, 0) * 255).astype(np.uint8), 127, 255, 0
+    )
     contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
 
     boxes = []

main.py

@@ -44,8 +44,7 @@ os.environ["NUMEXPR_NUM_THREADS"] = NUM_THREADS
 if os.environ.get("CACHE_DIR"):
     os.environ["TORCH_HOME"] = os.environ["CACHE_DIR"]
 
-BUILD_DIR = os.environ.get("LAMA_CLEANER_BUILD_DIR",
+BUILD_DIR = os.environ.get("LAMA_CLEANER_BUILD_DIR", "./lama_cleaner/app/build")
-                           "./lama_cleaner/app/build")
 
 app = Flask(__name__, static_folder=os.path.join(BUILD_DIR, "static"))
 app.config["JSON_AS_ASCII"] = False
@@ -56,11 +55,20 @@ device = None
 input_image_path: str = None
 
 
+def get_image_ext(img_bytes):
+    w = imghdr.what("", img_bytes)
+    if w is None:
+        w = "jpeg"
+    return w
+
+
 @app.route("/inpaint", methods=["POST"])
 def process():
     input = request.files
     # RGB
-    image = load_img(input["image"].read())
+    origin_image_bytes = input["image"].read()
+
+    image = load_img(origin_image_bytes)
     original_shape = image.shape
     interpolation = cv2.INTER_CUBIC
 
@@ -71,14 +79,12 @@ def process():
         size_limit = int(size_limit)
 
     print(f"Origin image shape: {original_shape}")
-    image = resize_max_size(image, size_limit=size_limit,
+    image = resize_max_size(image, size_limit=size_limit, interpolation=interpolation)
-                            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,
+    mask = resize_max_size(mask, size_limit=size_limit, interpolation=interpolation)
-                           interpolation=interpolation)
     mask = norm_img(mask)
 
     start = time.time()
@@ -87,11 +93,10 @@ def process():
 
     torch.cuda.empty_cache()
 
+    ext = get_image_ext(origin_image_bytes)
     return send_file(
-        io.BytesIO(numpy_to_bytes(res_np_img)),
+        io.BytesIO(numpy_to_bytes(res_np_img, ext)),
-        mimetype="image/jpeg",
+        mimetype=f"image/{ext}",
-        as_attachment=True,
-        attachment_filename="result.jpeg",
     )
 
 
@@ -100,29 +105,42 @@ def index():
     return send_file(os.path.join(BUILD_DIR, "index.html"))
 
 
-@app.route('/inputimage')
+@app.route("/inputimage")
 def set_input_photo():
     if input_image_path:
-        with open(input_image_path, 'rb') as f:
+        with open(input_image_path, "rb") as f:
             image_in_bytes = f.read()
-        return send_file(io.BytesIO(image_in_bytes), mimetype='image/jpeg')
+        return send_file(
+            io.BytesIO(image_in_bytes),
+            mimetype=f"image/{get_image_ext(image_in_bytes)}",
+        )
     else:
-        return 'No Input Image'
+        return "No Input Image"
 
 
 def get_args_parser():
     parser = argparse.ArgumentParser()
     parser.add_argument(
-        "--input", type=str, help="Path to image you want to load by default")
+        "--input", type=str, help="Path to image you want to load by default"
+    )
     parser.add_argument("--port", default=8080, type=int)
     parser.add_argument("--model", default="lama", choices=["lama", "ldm"])
-    parser.add_argument("--crop-trigger-size", default=[2042, 2042], nargs=2, type=int,
+    parser.add_argument(
-                        help="If image size large then crop-trigger-size, "
+        "--crop-trigger-size",
-                             "crop each area from original image to do inference."
+        default=[2042, 2042],
-                             "Mainly for performance and memory reasons"
+        nargs=2,
-                             "Only for lama")
+        type=int,
-    parser.add_argument("--crop-margin", type=int, default=256,
+        help="If image size large then crop-trigger-size, "
-                        help="Margin around bounding box of painted stroke when crop mode triggered")
+             "crop each area from original image to do inference."
+             "Mainly for performance and memory reasons"
+             "Only for lama",
+    )
+    parser.add_argument(
+        "--crop-margin",
+        type=int,
+        default=256,
+        help="Margin around bounding box of painted stroke when crop mode triggered",
+    )
     parser.add_argument(
         "--ldm-steps",
         default=50,
@@ -131,10 +149,14 @@ def get_args_parser():
              "The larger the value, the better the result, but it will be more time-consuming",
     )
     parser.add_argument("--device", default="cuda", type=str)
-    parser.add_argument("--gui", action="store_true",
+    parser.add_argument("--gui", action="store_true", help="Launch as desktop app")
-                        help="Launch as desktop app")
+    parser.add_argument(
-    parser.add_argument("--gui-size", default=[1600, 1000], nargs=2, type=int,
+        "--gui-size",
-                        help="Set window size for GUI")
+        default=[1600, 1000],
+        nargs=2,
+        type=int,
+        help="Set window size for GUI",
+    )
     parser.add_argument("--debug", action="store_true")
 
     args = parser.parse_args()
@@ -157,8 +179,11 @@ def main():
     input_image_path = args.input
 
     if args.model == "lama":
-        model = LaMa(crop_trigger_size=args.crop_trigger_size,
+        model = LaMa(
-                     crop_margin=args.crop_margin, device=device)
+            crop_trigger_size=args.crop_trigger_size,
+            crop_margin=args.crop_margin,
+            device=device,
+        )
     elif args.model == "ldm":
         model = LDM(device, steps=args.ldm_steps)
     else: