add mask_area method in CropByMask and MaskBoxDetect nodes.

py/crop_by_mask.py

@@ -9,7 +9,7 @@ class CropByMask:
 
     @classmethod
     def INPUT_TYPES(self):
-        detect_mode = ['min_bounding_rect', 'max_inscribed_rect']
+        detect_mode = ['min_bounding_rect', 'max_inscribed_rect', 'mask_area']
         return {
             "required": {
                 "image": ("IMAGE", ),  #
@@ -61,8 +61,11 @@ class CropByMask:
         height = 0
         if detect == "min_bounding_rect":
             (x, y, width, height) = min_bounding_rect(bluredmask)
-        if detect == "max_inscribed_rect":
+        elif detect == "max_inscribed_rect":
             (x, y, width, height) = max_inscribed_rect(bluredmask)
+        else:
+            (x, y, width, height) = mask_area(_mask)
+        log(f"{NODE_NAME}: Box detected. x={x},y={y},width={width},height={height}")
         canvas_width, canvas_height = tensor2pil(torch.unsqueeze(image[0], 0)).convert('RGB').size
         x1 = x - left_reserve if x - left_reserve > 0 else 0
         y1 = y - top_reserve if y - top_reserve > 0 else 0

py/image_auto_crop.py

@@ -11,7 +11,7 @@ class ImageAutoCrop:
     @classmethod
     def INPUT_TYPES(self):
         matting_method_list = ['RMBG 1.4', 'SegmentAnything']
-        detect_mode = ['min_bounding_rect', 'max_inscribed_rect']
+        detect_mode = ['min_bounding_rect', 'max_inscribed_rect', 'mask_area']
         ratio_list = ['1:1', '3:2', '4:3', '16:9', '2:3', '3:4', '9:16', 'custom', 'detect_mask']
         return {
             "required": {
@@ -97,8 +97,10 @@ class ImageAutoCrop:
             y_offset = 0
             if detect == "min_bounding_rect":
                 (x, y, width, height) = min_bounding_rect(bluredmask)
-            if detect == "max_inscribed_rect":
+            elif detect == "max_inscribed_rect":
                 (x, y, width, height) = max_inscribed_rect(bluredmask)
+            else:
+                (x, y, width, height) = mask_area(bluredmask)
             canvas_width, canvas_height = _image.size
             x1 = x - border_reserve
             y1 = y - border_reserve

py/imagefunc.py

@@ -974,6 +974,18 @@ def RGB2RGBA(image:Image, mask:Image) -> Image:
     (R, G, B) = image.convert('RGB').split()
     return Image.merge('RGBA', (R, G, B, mask.convert('L')))
 
+def mask_area(image:Image) -> tuple:
+    cv2_image = pil2cv2(image.convert('RGBA'))
+    gray = cv2.cvtColor(cv2_image, cv2.COLOR_BGR2GRAY)
+    _, thresh = cv2.threshold(gray, 127, 255, 0)
+    locs = np.where(thresh == 255)
+    x1 = np.min(locs[1])
+    x2 = np.max(locs[1])
+    y1 = np.min(locs[0])
+    y2 = np.max(locs[0])
+    x1, y1, x2, y2 = min(x1, x2), min(y1, y2), max(x1, x2), max(y1, y2)
+    return (x1, y1, x2 - x1, y2 - y1)
+
 def min_bounding_rect(image:Image) -> tuple:
     cv2_image = pil2cv2(image)
     gray = cv2.cvtColor(cv2_image, cv2.COLOR_BGR2GRAY)

py/mask_box_detect.py

@@ -9,7 +9,7 @@ class MaskBoxDetect:
 
     @classmethod
     def INPUT_TYPES(self):
-        detect_mode = ['min_bounding_rect', 'max_inscribed_rect']
+        detect_mode = ['min_bounding_rect', 'max_inscribed_rect', 'mask_area']
         return {
             "required": {
                 "mask": ("MASK", ),
@@ -46,8 +46,11 @@ class MaskBoxDetect:
 
         if detect == "min_bounding_rect":
             (x, y, width, height) = min_bounding_rect(_mask)
-        if detect == "max_inscribed_rect":
+        elif detect == "max_inscribed_rect":
             (x, y, width, height) = max_inscribed_rect(_mask)
+        else:
+            (x, y, width, height) = mask_area(_mask)
+        log(f"{NODE_NAME}: Box detected. x={x},y={y},width={width},height={height}")
         _width = width
         _height = height
         if scale_adjust != 1.0:

py/mask_edge_ultrl_detail.py

@@ -52,13 +52,12 @@ class MaskEdgeUltraDetail:
             _mask = l_masks[i]
             if mask_grow != 0:
                 _mask = expand_mask(_mask, mask_grow, mask_grow//2)
+            if fix_gap:
+                _mask = mask_fix(_mask, 1, fix_gap, fix_threshold, fix_threshold)
             if method == 'OpenCV-GuidedFilter':
-                if fix_gap:
-                    _mask = mask_fix(_mask, 1, fix_gap, fix_threshold, fix_threshold)
                 _mask = guided_filter_alpha(_image, _mask, detail_range)
                 _mask = tensor2pil(histogram_remap(_mask, black_point, white_point))
             else:
-                _mask = mask_fix(_mask, 1, fix_gap, fix_threshold, fix_threshold)
                 _mask = tensor2pil(mask_edge_detail(_image, _mask, detail_range, black_point, white_point))
 
             ret_image = RGB2RGBA(orig_image, _mask.convert('L'))

py/mask_gradient.py

@@ -33,6 +33,7 @@ class MaskGradient:
 
     def mask_gradient(self, mask, invert_mask, gradient_side, gradient_scale, gradient_offset, opacity, ):
 
+
         if mask.dim() == 2:
             mask = torch.unsqueeze(mask, 0)
 
@@ -50,8 +51,10 @@ class MaskGradient:
             width = _mask.width
             height = _mask.height
             _gradient = gradient('#000000', '#FFFFFF',
-                                 _mask.width, _mask.height, 0)
-            (box_x, box_y, box_width, box_height) = min_bounding_rect(_mask)
+                                 1024, 1024, 0)
+            # (box_x, box_y, box_width, box_height) = min_bounding_rect(_mask)
+            (box_x, box_y, box_width, box_height) = mask_area(_mask)
+            log(f"{NODE_NAME}: Box detected. x={box_x},y={box_y},width={box_width},height={box_height}")
             if box_width < 1 or box_height < 1:
                 log(f"Error: {NODE_NAME} skipped, because the mask is does'nt have valid area", message_type='error')
                 return (mask,)