add crop infor for lama

README.md

@@ -20,6 +20,10 @@ Install requirements: `pip3 install -r requirements.txt`
 python3 main.py --device=cuda --port=8080 --model=lama
 ```
 
+- `--crop-trigger-size`: If image size large then crop-trigger-size, crop each area from original image to do inference.
+  Mainly for performance and memory reasons on **very** large image.Default is 2042,2042
+- `--crop-size`: Crop size for `--crop-trigger-size`. Default is 512,512.
+
 ### Start server with LDM model
 
 ```bash
@@ -35,7 +39,6 @@ results than LaMa.
 |--------------|------|----|
 |![photo-1583445095369-9c651e7e5d34](https://user-images.githubusercontent.com/3998421/156923525-d6afdec3-7b98-403f-ad20-88ebc6eb8d6d.jpg)|![photo-1583445095369-9c651e7e5d34_cleanup_lama](https://user-images.githubusercontent.com/3998421/156923620-a40cc066-fd4a-4d85-a29f-6458711d1247.png)|![photo-1583445095369-9c651e7e5d34_cleanup_ldm](https://user-images.githubusercontent.com/3998421/156923652-0d06c8c8-33ad-4a42-a717-9c99f3268933.png)|
 
-
 Blogs about diffusion models:
 
 - https://lilianweng.github.io/posts/2021-07-11-diffusion-models/

lama_cleaner/helper.py

@@ -1,5 +1,6 @@
 import os
 import sys
+from typing import List
 
 from urllib.parse import urlparse
 import cv2
@@ -80,3 +81,27 @@ def pad_img_to_modulo(img, mod):
         ((0, 0), (0, out_height - height), (0, out_width - width)),
         mode="symmetric",
     )
+
+
+def boxes_from_mask(mask: np.ndarray) -> List[np.ndarray]:
+    """
+    Args:
+        mask: (1, h, w)  0~1
+
+    Returns:
+
+    """
+    height, width = mask.shape[1:]
+    _, 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 = []
+    for cnt in contours:
+        x, y, w, h = cv2.boundingRect(cnt)
+        box = np.array([x, y, x + w, y + h]).astype(np.int)
+
+        box[::2] = np.clip(box[::2], 0, width)
+        box[1::2] = np.clip(box[1::2], 0, height)
+        boxes.append(box)
+
+    return boxes

lama_cleaner/lama/__init__.py

@@ -1,10 +1,11 @@
 import os
+from typing import List
 
 import cv2
 import torch
 import numpy as np
 
-from lama_cleaner.helper import pad_img_to_modulo, download_model
+from lama_cleaner.helper import pad_img_to_modulo, download_model, boxes_from_mask
 
 LAMA_MODEL_URL = os.environ.get(
     "LAMA_MODEL_URL",
@@ -13,7 +14,16 @@ LAMA_MODEL_URL = os.environ.get(
 
 
 class LaMa:
-    def __init__(self, device):
+    def __init__(self, crop_trigger_size: List[int], crop_size: List[int], device):
+        """
+
+        Args:
+            crop_trigger_size: h, w
+            crop_size: h, w
+            device:
+        """
+        self.crop_trigger_size = crop_trigger_size
+        self.crop_size = crop_size
         self.device = device
 
         if os.environ.get("LAMA_MODEL"):
@@ -32,6 +42,63 @@ class LaMa:
 
     @torch.no_grad()
     def __call__(self, image, mask):
+        """
+        image: [C, H, W] RGB
+        mask: [1, H, W]
+        return: BGR IMAGE
+        """
+        area = image.shape[1] * image.shape[2]
+        if area < self.crop_trigger_size[0] * self.crop_trigger_size[1]:
+            return self._run(image, mask)
+
+        print("Trigger crop image")
+        boxes = boxes_from_mask(mask)
+        crop_result = []
+        for box in boxes:
+            crop_image, crop_box = self._run_box(image, mask, box)
+            crop_result.append((crop_image, crop_box))
+
+        image = (image.transpose(1, 2, 0) * 255).astype(np.uint8)[:, :, ::-1]
+        for crop_image, crop_box in crop_result:
+            x1, y1, x2, y2 = crop_box
+            image[y1:y2, x1:x2, :] = crop_image
+        return image
+
+    def _run_box(self, image, mask, box):
+        """
+
+        Args:
+            image: [C, H, W] RGB
+            mask: [1, H, W]
+            box: [left,top,right,bottom]
+
+        Returns:
+            BGR IMAGE
+        """
+        box_h = box[3] - box[1]
+        box_w = box[2] - box[0]
+        cx = (box[0] + box[2]) // 2
+        cy = (box[1] + box[3]) // 2
+        crop_h, crop_w = self.crop_size
+        img_h, img_w = image.shape[1:]
+
+        # TODO: when box_w > crop_w, add some margin around?
+        w = max(crop_w, box_w)
+        h = max(crop_h, box_h)
+
+        l = max(cx - w // 2, 0)
+        t = max(cy - h // 2, 0)
+        r = min(cx + w // 2, img_w)
+        b = min(cy + h // 2, img_h)
+
+        crop_img = image[:, t:b, l:r]
+        crop_mask = mask[:, t:b, l:r]
+
+        print(f"Apply zoom in size width x height: {crop_img.shape}")
+
+        return self._run(crop_img, crop_mask), [l, t, r, b]
+
+    def _run(self, image, mask):
         """
         image: [C, H, W] RGB
         mask: [1, H, W]
@@ -51,5 +118,5 @@ class LaMa:
         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_BGR2RGB)
+        cur_res = cv2.cvtColor(cur_res, cv2.COLOR_RGB2BGR)
         return cur_res

lama_cleaner/tests/test_boxes_from_mask.py

@@ -0,0 +1,15 @@
+import cv2
+import numpy as np
+
+from lama_cleaner.helper import boxes_from_mask
+
+
+def test_boxes_from_mask():
+    mask = cv2.imread("mask.jpg", cv2.IMREAD_GRAYSCALE)
+    mask = mask[:, :, np.newaxis]
+    mask = (mask / 255).transpose(2, 0, 1)
+    boxes = boxes_from_mask(mask)
+    print(boxes)
+
+
+test_boxes_from_mask()

main.py

@@ -97,12 +97,18 @@ def get_args_parser():
     parser = argparse.ArgumentParser()
     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",
+                        help="If image size large then crop-trigger-size, "
+                             "crop each area from original image to do inference."
+                             "Mainly for performance and memory reasons"
+                             "Only for lama")
+    parser.add_argument("--crop-size", default="512,512")
     parser.add_argument(
         "--ldm-steps",
         default=50,
         type=int,
         help="Steps for DDIM sampling process."
-        "The larger the value, the better the result, but it will be more time-consuming",
+             "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("--debug", action="store_true")
@@ -115,8 +121,11 @@ def main():
     args = get_args_parser()
     device = torch.device(args.device)
 
+    crop_trigger_size = [int(it) for it in args.crop_trigger_size.split(",")]
+    crop_size = [int(it) for it in args.crop_size.split(",")]
+
     if args.model == "lama":
-        model = LaMa(device)
+        model = LaMa(crop_trigger_size=crop_trigger_size, crop_size=crop_size, device=device)
     elif args.model == "ldm":
         model = LDM(device, steps=args.ldm_steps)
     else: