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more consistent rounding

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kijai 2023-11-25 11:14:12 +02:00
parent f6ea14cc01
commit e285ea31b2
1 changed files with 26 additions and 26 deletions
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52
nodes.py
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@ -1385,7 +1385,7 @@ class BatchCropFromMask:
"required": { "required": {
"original_images": ("IMAGE",), "original_images": ("IMAGE",),
"masks": ("MASK",), "masks": ("MASK",),
"crop_size_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), "crop_size_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}),
"bbox_smooth_alpha": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), "bbox_smooth_alpha": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
}, },
} }
@ -1410,14 +1410,14 @@ class BatchCropFromMask:
def smooth_bbox_size(self, prev_bbox_size, curr_bbox_size, alpha): def smooth_bbox_size(self, prev_bbox_size, curr_bbox_size, alpha):
if alpha == 0: if alpha == 0:
return prev_bbox_size return prev_bbox_size
return int(alpha * curr_bbox_size + (1 - alpha) * prev_bbox_size) return round(alpha * curr_bbox_size + (1 - alpha) * prev_bbox_size)
def smooth_center(self, prev_center, curr_center, alpha=0.5): def smooth_center(self, prev_center, curr_center, alpha=0.5):
if alpha == 0: if alpha == 0:
return prev_center return prev_center
return ( return (
int(alpha * curr_center[0] + (1 - alpha) * prev_center[0]), round(alpha * curr_center[0] + (1 - alpha) * prev_center[0]),
int(alpha * curr_center[1] + (1 - alpha) * prev_center[1]) round(alpha * curr_center[1] + (1 - alpha) * prev_center[1])
) )
def crop(self, masks, original_images, crop_size_mult, bbox_smooth_alpha): def crop(self, masks, original_images, crop_size_mult, bbox_smooth_alpha):
@ -1446,8 +1446,8 @@ class BatchCropFromMask:
self.max_bbox_height = self.smooth_bbox_size(self.max_bbox_height, curr_max_bbox_height, bbox_smooth_alpha) self.max_bbox_height = self.smooth_bbox_size(self.max_bbox_height, curr_max_bbox_height, bbox_smooth_alpha)
# Apply the crop size multiplier # Apply the crop size multiplier
self.max_bbox_width = int(self.max_bbox_width * crop_size_mult) self.max_bbox_width = round(self.max_bbox_width * crop_size_mult)
self.max_bbox_height = int(self.max_bbox_height * crop_size_mult) self.max_bbox_height = round(self.max_bbox_height * crop_size_mult)
bbox_aspect_ratio = self.max_bbox_width / self.max_bbox_height bbox_aspect_ratio = self.max_bbox_width / self.max_bbox_height
# # Make sure max_bbox_size is divisible by 32, if not, round it upwards so it is # # Make sure max_bbox_size is divisible by 32, if not, round it upwards so it is
@ -1464,7 +1464,7 @@ class BatchCropFromMask:
# Calculate center of bounding box # Calculate center of bounding box
center_x = np.mean(non_zero_indices[1]) center_x = np.mean(non_zero_indices[1])
center_y = np.mean(non_zero_indices[0]) center_y = np.mean(non_zero_indices[0])
curr_center = (int(center_x), int(center_y)) curr_center = (round(center_x), round(center_y))
# If this is the first frame, initialize prev_center with curr_center # If this is the first frame, initialize prev_center with curr_center
if not hasattr(self, 'prev_center'): if not hasattr(self, 'prev_center'):
@ -1495,7 +1495,7 @@ class BatchCropFromMask:
# Calculate the new dimensions while maintaining the aspect ratio # Calculate the new dimensions while maintaining the aspect ratio
new_height = min(cropped_img.shape[0], self.max_bbox_height) new_height = min(cropped_img.shape[0], self.max_bbox_height)
new_width = int(new_height * bbox_aspect_ratio) new_width = round(new_height * bbox_aspect_ratio)
# Resize the image # Resize the image
resize_transform = Resize((new_height, new_width)) resize_transform = Resize((new_height, new_width))
@ -1587,10 +1587,10 @@ class BatchUncrop:
scale_y = crop_rescale scale_y = crop_rescale
# scaled paste_region # scaled paste_region
paste_region = (int(paste_region[0]*scale_x), int(paste_region[1]*scale_y), int(paste_region[2]*scale_x), int(paste_region[3]*scale_y)) paste_region = (round(paste_region[0]*scale_x), round(paste_region[1]*scale_y), round(paste_region[2]*scale_x), round(paste_region[3]*scale_y))
# rescale the crop image to fit the paste_region # rescale the crop image to fit the paste_region
crop = crop.resize((int(paste_region[2]-paste_region[0]), int(paste_region[3]-paste_region[1]))) crop = crop.resize((round(paste_region[2]-paste_region[0]), round(paste_region[3]-paste_region[1])))
crop_img = crop.convert("RGB") crop_img = crop.convert("RGB")
if border_blending > 1.0: if border_blending > 1.0:
@ -1604,7 +1604,7 @@ class BatchUncrop:
mask = Image.new("L", img.size, 0) mask = Image.new("L", img.size, 0)
mask_block = Image.new("L", (paste_region[2]-paste_region[0], paste_region[3]-paste_region[1]), 255) mask_block = Image.new("L", (paste_region[2]-paste_region[0], paste_region[3]-paste_region[1]), 255)
mask_block = inset_border(mask_block, int(blend_ratio / 2), (0), border_top, border_bottom, border_left, border_right) mask_block = inset_border(mask_block, round(blend_ratio / 2), (0), border_top, border_bottom, border_left, border_right)
mask.paste(mask_block, paste_region) mask.paste(mask_block, paste_region)
blend.paste(crop_img, paste_region) blend.paste(crop_img, paste_region)
@ -1657,11 +1657,11 @@ class BatchCropFromMaskAdvanced:
CATEGORY = "KJNodes/masking" CATEGORY = "KJNodes/masking"
def smooth_bbox_size(self, prev_bbox_size, curr_bbox_size, alpha): def smooth_bbox_size(self, prev_bbox_size, curr_bbox_size, alpha):
return int(alpha * curr_bbox_size + (1 - alpha) * prev_bbox_size) return round(alpha * curr_bbox_size + (1 - alpha) * prev_bbox_size)
def smooth_center(self, prev_center, curr_center, alpha=0.5): def smooth_center(self, prev_center, curr_center, alpha=0.5):
return (int(alpha * curr_center[0] + (1 - alpha) * prev_center[0]), return (round(alpha * curr_center[0] + (1 - alpha) * prev_center[0]),
int(alpha * curr_center[1] + (1 - alpha) * prev_center[1])) round(alpha * curr_center[1] + (1 - alpha) * prev_center[1]))
def crop(self, masks, original_images, crop_size_mult, bbox_smooth_alpha): def crop(self, masks, original_images, crop_size_mult, bbox_smooth_alpha):
bounding_boxes = [] bounding_boxes = []
@ -1687,11 +1687,11 @@ class BatchCropFromMaskAdvanced:
new_min_x, new_max_x, new_min_y, new_max_y, combined_bbox_size = calculate_bbox(_mask) new_min_x, new_max_x, new_min_y, new_max_y, combined_bbox_size = calculate_bbox(_mask)
center_x = (new_min_x + new_max_x) / 2 center_x = (new_min_x + new_max_x) / 2
center_y = (new_min_y + new_max_y) / 2 center_y = (new_min_y + new_max_y) / 2
half_box_size = int(combined_bbox_size // 2) half_box_size = round(combined_bbox_size // 2)
new_min_x = max(0, int(center_x - half_box_size)) new_min_x = max(0, round(center_x - half_box_size))
new_max_x = min(original_images[0].shape[1], int(center_x + half_box_size)) new_max_x = min(original_images[0].shape[1], round(center_x + half_box_size))
new_min_y = max(0, int(center_y - half_box_size)) new_min_y = max(0, round(center_y - half_box_size))
new_max_y = min(original_images[0].shape[0], int(center_y + half_box_size)) new_max_y = min(original_images[0].shape[0], round(center_y + half_box_size))
combined_bounding_box.append((new_min_x, new_min_y, new_max_x - new_min_x, new_max_y - new_min_y)) combined_bounding_box.append((new_min_x, new_min_y, new_max_x - new_min_x, new_max_y - new_min_y))
@ -1702,9 +1702,9 @@ class BatchCropFromMaskAdvanced:
# Smooth the changes in the bounding box size # Smooth the changes in the bounding box size
self.max_bbox_size = self.smooth_bbox_size(self.max_bbox_size, curr_max_bbox_size, bbox_smooth_alpha) self.max_bbox_size = self.smooth_bbox_size(self.max_bbox_size, curr_max_bbox_size, bbox_smooth_alpha)
# Apply the crop size multiplier # Apply the crop size multiplier
self.max_bbox_size = int(self.max_bbox_size * crop_size_mult) self.max_bbox_size = round(self.max_bbox_size * crop_size_mult)
# Make sure max_bbox_size is divisible by 32, if not, round it upwards so it is # Make sure max_bbox_size is divisible by 16, if not, round it upwards so it is
self.max_bbox_size = math.ceil(self.max_bbox_size / 32) * 32 self.max_bbox_size = math.ceil(self.max_bbox_size / 16) * 16
# Then, for each mask and corresponding image... # Then, for each mask and corresponding image...
for i, (mask, img) in enumerate(zip(masks, original_images)): for i, (mask, img) in enumerate(zip(masks, original_images)):
@ -1716,7 +1716,7 @@ class BatchCropFromMaskAdvanced:
# Calculate center of bounding box # Calculate center of bounding box
center_x = np.mean(non_zero_indices[1]) center_x = np.mean(non_zero_indices[1])
center_y = np.mean(non_zero_indices[0]) center_y = np.mean(non_zero_indices[0])
curr_center = (int(center_x), int(center_y)) curr_center = (round(center_x), round(center_y))
# If this is the first frame, initialize prev_center with curr_center # If this is the first frame, initialize prev_center with curr_center
if not hasattr(self, 'prev_center'): if not hasattr(self, 'prev_center'):
@ -1848,10 +1848,10 @@ class BatchUncropAdvanced:
# scale paste_region # scale paste_region
scale_x = scale_y = crop_rescale scale_x = scale_y = crop_rescale
paste_region = (int(paste_region[0]*scale_x), int(paste_region[1]*scale_y), int(paste_region[2]*scale_x), int(paste_region[3]*scale_y)) paste_region = (round(paste_region[0]*scale_x), round(paste_region[1]*scale_y), round(paste_region[2]*scale_x), round(paste_region[3]*scale_y))
# rescale the crop image to fit the paste_region # rescale the crop image to fit the paste_region
crop = crop.resize((int(paste_region[2]-paste_region[0]), int(paste_region[3]-paste_region[1]))) crop = crop.resize((round(paste_region[2]-paste_region[0]), round(paste_region[3]-paste_region[1])))
crop_img = crop.convert("RGB") crop_img = crop.convert("RGB")
#border blending #border blending
@ -1866,7 +1866,7 @@ class BatchUncropAdvanced:
if use_square_mask: if use_square_mask:
mask = Image.new("L", img.size, 0) mask = Image.new("L", img.size, 0)
mask_block = Image.new("L", (paste_region[2]-paste_region[0], paste_region[3]-paste_region[1]), 255) mask_block = Image.new("L", (paste_region[2]-paste_region[0], paste_region[3]-paste_region[1]), 255)
mask_block = inset_border(mask_block, int(blend_ratio / 2), (0)) mask_block = inset_border(mask_block, round(blend_ratio / 2), (0))
mask.paste(mask_block, paste_region) mask.paste(mask_block, paste_region)
else: else:
original_mask = tensor2pil(mask)[0] original_mask = tensor2pil(mask)[0]