import torch import cv2 import numpy as np from PIL import Image, ImageDraw, ImageFilter LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS) def pil2tensor(image): return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0) def tensor2pil(image): return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)) def center_of_bbox(bbox): w, h = bbox[2] - bbox[0], bbox[3] - bbox[1] return bbox[0] + w/2, bbox[1] + h/2 def combine_masks(masks): if len(masks) == 0: return None else: initial_cv2_mask = np.array(masks[0][1]) combined_cv2_mask = initial_cv2_mask for i in range(1, len(masks)): cv2_mask = np.array(masks[i][1]) if combined_cv2_mask.shape == cv2_mask.shape: combined_cv2_mask = cv2.bitwise_or(combined_cv2_mask, cv2_mask) else: # do nothing - incompatible mask pass mask = torch.from_numpy(combined_cv2_mask) return mask def combine_masks2(masks): if len(masks) == 0: return None else: initial_cv2_mask = np.array(masks[0]).astype(np.uint8) combined_cv2_mask = initial_cv2_mask for i in range(1, len(masks)): cv2_mask = np.array(masks[i]).astype(np.uint8) if combined_cv2_mask.shape == cv2_mask.shape: combined_cv2_mask = cv2.bitwise_or(combined_cv2_mask, cv2_mask) else: # do nothing - incompatible mask pass mask = torch.from_numpy(combined_cv2_mask) return mask def bitwise_and_masks(mask1, mask2): mask1 = mask1.cpu() mask2 = mask2.cpu() cv2_mask1 = np.array(mask1) cv2_mask2 = np.array(mask2) if cv2_mask1.shape == cv2_mask2.shape: cv2_mask = cv2.bitwise_and(cv2_mask1, cv2_mask2) return torch.from_numpy(cv2_mask) else: # do nothing - incompatible mask shape: mostly empty mask return mask1 def to_binary_mask(mask, threshold=0): mask = mask.clone().cpu() mask[mask > threshold] = 1. mask[mask <= threshold] = 0. return mask def dilate_mask(mask, dilation_factor, iter=1): if dilation_factor == 0: return mask kernel = np.ones((dilation_factor,dilation_factor), np.uint8) return cv2.dilate(mask, kernel, iter) def dilate_masks(segmasks, dilation_factor, iter=1): if dilation_factor == 0: return segmasks dilated_masks = [] kernel = np.ones((dilation_factor,dilation_factor), np.uint8) for i in range(len(segmasks)): cv2_mask = segmasks[i][1] dilated_mask = cv2.dilate(cv2_mask, kernel, iter) item = (segmasks[i][0], dilated_mask, segmasks[i][2]) dilated_masks.append(item) return dilated_masks def feather_mask(mask, thickness): pil_mask = Image.fromarray(np.uint8(mask * 255)) # Create a feathered mask by applying a Gaussian blur to the mask blurred_mask = pil_mask.filter(ImageFilter.GaussianBlur(thickness)) feathered_mask = Image.new("L", pil_mask.size, 0) feathered_mask.paste(blurred_mask, (0, 0), blurred_mask) return feathered_mask def subtract_masks(mask1, mask2): mask1 = mask1.cpu() mask2 = mask2.cpu() cv2_mask1 = np.array(mask1) * 255 cv2_mask2 = np.array(mask2) * 255 if cv2_mask1.shape == cv2_mask2.shape: cv2_mask = cv2.subtract(cv2_mask1, cv2_mask2) return torch.from_numpy(cv2_mask) / 255.0 else: # do nothing - incompatible mask shape: mostly empty mask return mask1 def add_masks(mask1, mask2): mask1 = mask1.cpu() mask2 = mask2.cpu() cv2_mask1 = np.array(mask1) * 255 cv2_mask2 = np.array(mask2) * 255 if cv2_mask1.shape == cv2_mask2.shape: cv2_mask = cv2.add(cv2_mask1, cv2_mask2) return torch.from_numpy(cv2_mask) / 255.0 else: # do nothing - incompatible mask shape: mostly empty mask return mask1 def normalize_region(limit, startp, size): if startp < 0: new_endp = min(limit, size) new_startp = 0 elif startp + size > limit: new_startp = max(0, limit - size) new_endp = limit else: new_startp = startp new_endp = min(limit, startp+size) return int(new_startp), int(new_endp) def make_crop_region(w, h, bbox, crop_factor): x1 = bbox[0] y1 = bbox[1] x2 = bbox[2] y2 = bbox[3] bbox_w = x2 - x1 bbox_h = y2 - y1 crop_w = bbox_w * crop_factor crop_h = bbox_h * crop_factor kernel_x = x1 + bbox_w / 2 kernel_y = y1 + bbox_h / 2 new_x1 = int(kernel_x - crop_w / 2) new_y1 = int(kernel_y - crop_h / 2) # make sure position in (w,h) new_x1, new_x2 = normalize_region(w, new_x1, crop_w) new_y1, new_y2 = normalize_region(h, new_y1, crop_h) return [new_x1, new_y1, new_x2, new_y2] def crop_ndarray4(npimg, crop_region): x1 = crop_region[0] y1 = crop_region[1] x2 = crop_region[2] y2 = crop_region[3] cropped = npimg[:, y1:y2, x1:x2, :] return cropped def crop_ndarray2(npimg, crop_region): x1 = crop_region[0] y1 = crop_region[1] x2 = crop_region[2] y2 = crop_region[3] cropped = npimg[y1:y2, x1:x2] return cropped def crop_image(image, crop_region): return crop_ndarray4(np.array(image), crop_region) def to_latent_image(pixels, vae): x = pixels.shape[1] y = pixels.shape[2] if pixels.shape[1] != x or pixels.shape[2] != y: pixels = pixels[:, :x, :y, :] t = vae.encode(pixels[:, :, :, :3]) return {"samples": t} def scale_tensor(w, h, image): image = tensor2pil(image) scaled_image = image.resize((w, h), resample=LANCZOS) return pil2tensor(scaled_image) def scale_tensor_and_to_pil(w, h, image): image = tensor2pil(image) return image.resize((w, h), resample=LANCZOS) def empty_pil_tensor(w=64, h=64): image = Image.new("RGB", (w, h)) draw = ImageDraw.Draw(image) draw.rectangle((0, 0, w-1, h-1), fill=(0, 0, 0)) return pil2tensor(image) class NonListIterable: def __init__(self, data): self.data = data def __getitem__(self, index): return self.data[index]