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https://git.datalinker.icu/comfyanonymous/ComfyUI
synced 2026-08-17 14:03:31 +08:00
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Yousef Rafat
parent
db06eebbd8
commit
170c3e0352
@ -1,295 +0,0 @@
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import torch
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import torch.nn as nn
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import numpy as np
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from PIL import Image
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import torch.nn.functional as F
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def to_tensor(image_pt):
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image_pt = image_pt / 255 * 2 - 1
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return image_pt
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def resize_nearest(img: torch.Tensor, size: int) -> torch.Tensor:
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batched = (img.ndim == 4)
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if img.ndim == 3:
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img = img.unsqueeze(0)
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img = img.permute(0, 3, 1, 2)
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out = F.interpolate(img, size=size, mode='nearest')
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if not batched:
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out = out.squeeze(0)
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return out
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def cubic_kernel(x, a: float = -0.75):
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absx = x.abs()
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absx2 = absx ** 2
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absx3 = absx ** 3
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w = (a + 2) * absx3 - (a + 3) * absx2 + 1
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w2 = a * absx3 - 5*a * absx2 + 8*a * absx - 4*a
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return torch.where(absx <= 1, w, torch.where(absx < 2, w2, torch.zeros_like(x)))
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def get_indices_weights(in_size, out_size, scale):
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# OpenCV-style half-pixel mapping
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x = torch.arange(out_size, dtype=torch.float32)
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x = (x + 0.5) / scale - 0.5
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x0 = x.floor().long()
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dx = x.unsqueeze(1) - (x0.unsqueeze(1) + torch.arange(-1, 3))
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weights = cubic_kernel(dx)
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weights = weights / weights.sum(dim=1, keepdim=True)
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indices = x0.unsqueeze(1) + torch.arange(-1, 3)
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indices = indices.clamp(0, in_size - 1)
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return indices, weights
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def resize_cubic_1d(x, out_size, dim):
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b, c, h, w = x.shape
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in_size = h if dim == 2 else w
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scale = out_size / in_size
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indices, weights = get_indices_weights(in_size, out_size, scale)
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if dim == 2:
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x = x.permute(0, 1, 3, 2)
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x = x.reshape(-1, h)
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else:
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x = x.reshape(-1, w)
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gathered = x[:, indices]
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out = (gathered * weights.unsqueeze(0)).sum(dim=2)
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if dim == 2:
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out = out.reshape(b, c, w, out_size).permute(0, 1, 3, 2)
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else:
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out = out.reshape(b, c, h, out_size)
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return out
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def resize_cubic(img: torch.Tensor, size: tuple) -> torch.Tensor:
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"""
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Resize image using OpenCV-equivalent INTER_CUBIC interpolation.
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Implemented in pure PyTorch
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"""
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if img.ndim == 3:
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img = img.unsqueeze(0)
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img = img.permute(0, 3, 1, 2)
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out_h, out_w = size
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img = resize_cubic_1d(img, out_h, dim=2)
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img = resize_cubic_1d(img, out_w, dim=3)
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return img
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def resize_area(img: torch.Tensor, size: tuple) -> torch.Tensor:
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# vectorized implementation for OpenCV's INTER_AREA using pure PyTorch
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original_shape = img.shape
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is_hwc = False
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if img.ndim == 3:
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if img.shape[0] <= 4:
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img = img.unsqueeze(0)
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else:
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is_hwc = True
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img = img.permute(2, 0, 1).unsqueeze(0)
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elif img.ndim == 4:
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pass
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else:
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raise ValueError("Expected image with 3 or 4 dims.")
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B, C, H, W = img.shape
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out_h, out_w = size
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scale_y = H / out_h
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scale_x = W / out_w
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device = img.device
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# compute the grid boundries
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y_start = torch.arange(out_h, device=device).float() * scale_y
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y_end = y_start + scale_y
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x_start = torch.arange(out_w, device=device).float() * scale_x
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x_end = x_start + scale_x
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# for each output pixel, we will compute the range for it
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y_start_int = torch.floor(y_start).long()
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y_end_int = torch.ceil(y_end).long()
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x_start_int = torch.floor(x_start).long()
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x_end_int = torch.ceil(x_end).long()
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# We will build the weighted sums by iterating over contributing input pixels once
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output = torch.zeros((B, C, out_h, out_w), dtype=torch.float32, device=device)
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area = torch.zeros((out_h, out_w), dtype=torch.float32, device=device)
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max_kernel_h = int(torch.max(y_end_int - y_start_int).item())
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max_kernel_w = int(torch.max(x_end_int - x_start_int).item())
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for dy in range(max_kernel_h):
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for dx in range(max_kernel_w):
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# compute the weights for this offset for all output pixels
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y_idx = y_start_int.unsqueeze(1) + dy
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x_idx = x_start_int.unsqueeze(0) + dx
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# clamp indices to image boundaries
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y_idx_clamped = torch.clamp(y_idx, 0, H - 1)
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x_idx_clamped = torch.clamp(x_idx, 0, W - 1)
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# compute weights by broadcasting
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y_weight = (torch.min(y_end.unsqueeze(1), y_idx_clamped.float() + 1.0) - torch.max(y_start.unsqueeze(1), y_idx_clamped.float())).clamp(min=0)
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x_weight = (torch.min(x_end.unsqueeze(0), x_idx_clamped.float() + 1.0) - torch.max(x_start.unsqueeze(0), x_idx_clamped.float())).clamp(min=0)
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weight = (y_weight * x_weight)
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y_expand = y_idx_clamped.expand(out_h, out_w)
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x_expand = x_idx_clamped.expand(out_h, out_w)
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pixels = img[:, :, y_expand, x_expand]
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# unsqueeze to broadcast
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w = weight.unsqueeze(0).unsqueeze(0)
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output += pixels * w
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area += weight
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# Normalize by area
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output /= area.unsqueeze(0).unsqueeze(0)
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if is_hwc:
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return output[0].permute(1, 2, 0)
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elif img.shape[0] == 1 and original_shape[0] <= 4:
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return output[0]
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else:
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return output
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class ImageProcessorV2(nn.Module):
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def __init__(self, size: int = 512, border_ratio: float = None):
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super().__init__()
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self.size = size
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self.border_ratio = border_ratio
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def load_image(self, pic, border_ratio: float = 0.15) -> torch.Tensor:
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if isinstance(pic, str):
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img = Image.open(pic)
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img = np.array(img)
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elif isinstance(pic, Image.Image):
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img = np.array(pic)
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if img.ndim == 2: # grayscale
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img = img[:, :, None]
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img = torch.from_numpy(img)
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img, mask = self.recenter(img, border_ratio = border_ratio)
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img = resize_cubic(img, size = (self.size, self.size))
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mask = resize_nearest(mask.float(), size = self.size)
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mask = mask[..., torch.newaxis]
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img = to_tensor(img)
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mask = to_tensor(mask)
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mask = mask.permute(0, 3, 1, 2)
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return img, mask
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@staticmethod
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def recenter(image, border_ratio: float = 0.2):
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if image.shape[-1] == 4:
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mask = image[..., 3]
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else:
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mask = torch.ones_like(image[..., 0:1]) * 255
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image = torch.concatenate([image, mask], axis=-1)
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mask = mask[..., 0]
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H, W, C = image.shape
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size = max(H, W)
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result = torch.zeros((size, size, C), dtype = torch.uint8)
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# as_tuple to match numpy behaviour
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x_coords, y_coords = torch.nonzero(mask, as_tuple=True)
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y_min, y_max = y_coords.min(), y_coords.max()
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x_min, x_max = x_coords.min(), x_coords.max()
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h = x_max - x_min
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w = y_max - y_min
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if h == 0 or w == 0:
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raise ValueError('input image is empty')
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desired_size = int(size * (1 - border_ratio))
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scale = desired_size / max(h, w)
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h2 = int(h * scale)
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w2 = int(w * scale)
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x2_min = (size - h2) // 2
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x2_max = x2_min + h2
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y2_min = (size - w2) // 2
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y2_max = y2_min + w2
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# note: opencv takes columns first (opposite to pytorch and numpy that take the row first)
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result[x2_min:x2_max, y2_min:y2_max] = resize_area(image[x_min:x_max, y_min:y_max], (h2, w2))
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bg = torch.ones((result.shape[0], result.shape[1], 3), dtype = torch.uint8) * 255
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mask = result[..., 3:].to(torch.float32) / 255
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result = result[..., :3] * mask + bg * (1 - mask)
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mask = mask * 255
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result = result.clip(0, 255).to(torch.uint8)
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mask = mask.clip(0, 255).to(torch.uint8)
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return result, mask
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def __call__(self, image, border_ratio = 0.15, **kwargs):
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if self.border_ratio is not None:
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border_ratio = self.border_ratio
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image, mask = self.load_image(image, border_ratio = border_ratio)
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outputs = {
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'image': image,
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'mask': mask
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}
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return outputs
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def test_image_processor():
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"""
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implementation speed: 0.24465346336364746
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reference speed: 2.046062469482422
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atol = 4e-2: True
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"""
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import time
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import matplotlib.pyplot as plt
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image_processor = ImageProcessorV2(size = 224)
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start = time.time()
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outputs = image_processor(image = r"C:\Users\yrafa\Work\Hunyuan 3D\cat.jpg")
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print(time.time() - start)
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image = outputs["image"]
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print(image.shape)
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plt.imshow(image.squeeze().permute(1, 2, 0).numpy())
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plt.axis("off")
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plt.show()
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@ -27,5 +27,4 @@ spandrel
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soundfile
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av>=14.2.0
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pydantic~=2.0
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pydantic-settings~=2.0
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scikit-image # for 3D mesh generation (marching cubes)
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pydantic-settings~=2.0
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