import os, io, csv, math, random import numpy as np from einops import rearrange import torch from decord import VideoReader import cv2 from scipy.ndimage import distance_transform_edt import torchvision.transforms as transforms from torch.utils.data.dataset import Dataset # from utils.util import zero_rank_print #from torchvision.io import read_image from PIL import Image def pil_image_to_numpy(image, is_maks = False, index = 1): """Convert a PIL image to a NumPy array.""" if is_maks: # index = 1 image = image.resize((256, 256)) # image = (np.array(image)==index)*1 # image = cv2.cvtColor(image.astype(np.uint8), cv2.COLOR_GRAY2RGB) return np.array(image) else: if image.mode != 'RGB': image = image.convert('RGB') image = image.resize((256, 256)) return np.array(image) def numpy_to_pt(images: np.ndarray, is_mask=False) -> torch.FloatTensor: """Convert a NumPy image to a PyTorch tensor.""" if images.ndim == 3: images = images[..., None] images = torch.from_numpy(images.transpose(0, 3, 1, 2)) if is_mask: return images.float() else: return images.float() / 255 def find_largest_inner_rectangle_coordinates(mask_gray): # 识别轮廓 contours, _ = cv2.findContours(mask_gray.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) xx,yy,ww,hh = 0,0,0,0 contours_r = contours[0] for contour in contours: x, y, w, h = cv2.boundingRect(contour) if w*h > ww*hh: xx,yy,ww,hh = x, y, w, h contours_r = contour # 计算到轮廓的距离 raw_dist = np.empty(mask_gray.shape, dtype=np.float32) for i in range(mask_gray.shape[0]): for j in range(mask_gray.shape[1]): raw_dist[i, j] = cv2.pointPolygonTest(contours_r, (j, i), True) # 获取最大值即内接圆半径，中心点坐标 minVal, maxVal, _, maxDistPt = cv2.minMaxLoc(raw_dist) minVal = abs(minVal) maxVal = abs(maxVal) return maxDistPt, int(maxVal) class YoutubeVos(Dataset): def __init__( self,video_folder,ann_folder,motion_folder, sample_size=256, sample_stride=4, sample_n_frames=14, ): self.dataset = [i for i in os.listdir(video_folder)] self.length = len(self.dataset) print(f"data scale: {self.length}") random.shuffle(self.dataset) self.video_folder = video_folder self.sample_stride = sample_stride self.sample_n_frames = sample_n_frames self.ann_folder = ann_folder self.heatmap = self.gen_gaussian_heatmap() self.motion_values_folder=motion_folder self.sample_size = sample_size print("length",len(self.dataset)) sample_size = tuple(sample_size) if not isinstance(sample_size, int) else (sample_size, sample_size) print("sample size",sample_size) self.pixel_transforms = transforms.Compose([ # transforms.RandomHorizontalFlip(), transforms.Resize(sample_size), # transforms.CenterCrop(sample_size), transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True), ]) # self.idtransform = transforms.Compose([ # transforms.ToTensor(), # transforms.Resize((196, 196)), # # transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)) # ]) def center_crop(self,img): h, w = img.shape[-2:] # Assuming img shape is [C, H, W] or [B, C, H, W] min_dim = min(h, w) top = (h - min_dim) // 2 left = (w - min_dim) // 2 return img[..., top:top+min_dim, left:left+min_dim] def gen_gaussian_heatmap(self,imgSize=200): circle_img = np.zeros((imgSize, imgSize), np.float32) circle_mask = cv2.circle(circle_img, (imgSize//2, imgSize//2), imgSize//2, 1, -1) # print(circle_mask) isotropicGrayscaleImage = np.zeros((imgSize, imgSize), np.float32) # Guass Map for i in range(imgSize): for j in range(imgSize): isotropicGrayscaleImage[i, j] = 1 / 2 / np.pi / (40 ** 2) * np.exp( -1 / 2 * ((i - imgSize / 2) ** 2 / (40 ** 2) + (j - imgSize / 2) ** 2 / (40 ** 2))) isotropicGrayscaleImage = isotropicGrayscaleImage * circle_mask isotropicGrayscaleImage = (isotropicGrayscaleImage / np.max(isotropicGrayscaleImage)).astype(np.float32) isotropicGrayscaleImage = (isotropicGrayscaleImage / np.max(isotropicGrayscaleImage)*255).astype(np.uint8) # isotropicGrayscaleImage = cv2.resize(isotropicGrayscaleImage, (40, 40)) return isotropicGrayscaleImage def calculate_center_coordinates(self,masks,ids, side=20): center_coordinates = [] masks_list = [] ids = random.choice(ids[1:]) for index_mask, mask in enumerate(masks): new_img = np.zeros((self.sample_size, self.sample_size), np.float32) # 计算坐标的平均值，即中心坐标 # non_zero_coordinates = np.column_stack(np.where(mask_array > 0)) # center_coordinate = np.mean(non_zero_coordinates, axis=0)[:2].astype(np.uint8) for index in [ids]: mask_array = (np.array(mask)==index)*1 # 找到最大距离的索引 center_coordinate,side = find_largest_inner_rectangle_coordinates(mask_array) # center_coordinate = np.unravel_index(np.argmax(distance_transform), distance_transform.shape) x1 = max(center_coordinate[0]-side,0) x2 = min(center_coordinate[0]+side,self.sample_size-1) y1 = max(center_coordinate[1]-side,0) y2 = min(center_coordinate[1]+side,self.sample_size-1) # y1 = max(y,0) # y2 = min(y+h,self.sample_size-1) # x1 = max(x,0) # x2 = min(x+w,self.sample_size-1) need_map = cv2.resize(self.heatmap, (x2-x1, y2-y1)) new_img[y1:y2,x1:x2] = need_map # if index_mask == 0: # new_img = new_img + mask_array*55 new_img = cv2.cvtColor(new_img.astype(np.uint8), cv2.COLOR_GRAY2RGB) center_coordinates.append(new_img) masks_list.append(mask_array) return center_coordinates,masks_list def get_ID(self,images_list,masks_list): ID_images = [] image = images_list[0] mask = masks_list[0] # 使用 findContours 函数找到轮廓 try: contours, _ = cv2.findContours(mask.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) x, y, w, h = cv2.boundingRect(contours[0]) mask = cv2.cvtColor(mask.astype(np.uint8), cv2.COLOR_GRAY2RGB) image = image * mask image = image[y:y+h,x:x+w] except: pass # Id_Images = self.idtransform(Id_Images) image = cv2.resize(image, (196, 196)) for i,m in zip(images_list,masks_list): # image = self.idtransform(Image.fromarray(image)) # cv2.imwrite("./vis/test.jpg", image) ID_images.append(image) return ID_images def get_batch(self, idx): def sort_frames(frame_name): return int(frame_name.split('.')[0]) while True: videoid = self.dataset[idx] # videoid = video_dict['videoid'] preprocessed_dir = os.path.join(self.video_folder, videoid) ann_folder = os.path.join(self.ann_folder, videoid) motion_values_file = os.path.join(self.motion_values_folder, videoid, videoid + "_average_motion.txt") if not os.path.exists(ann_folder): idx = random.randint(0, len(self.dataset) - 1) continue # Sort and limit the number of image and depth files to 14 image_files = sorted(os.listdir(preprocessed_dir), key=sort_frames)[:14] depth_files = sorted(os.listdir(ann_folder), key=sort_frames)[:14] # Check if there are enough frames for both image and depth # if len(image_files) < 14 or len(depth_files) < 14: # idx = random.randint(0, len(self.dataset) - 1) # continue # Load image frames numpy_images = np.array([pil_image_to_numpy(Image.open(os.path.join(preprocessed_dir, img))) for img in image_files]) pixel_values = numpy_to_pt(numpy_images) # Load depth frames mask = Image.open(os.path.join(ann_folder, depth_files[0])).convert('P') ids = [i for i in np.unique(mask)] if len(ids)==1: idx = random.randint(0, len(self.dataset) - 1) continue # ids = random.choice(ids[1:]) numpy_depth_images = np.array([pil_image_to_numpy(Image.open(os.path.join(ann_folder, df)).convert('P'),True,ids) for df in depth_files]) try: heatmap_pixel_values, masks_list = self.calculate_center_coordinates(numpy_depth_images,ids) except: idx = random.randint(0, len(self.dataset) - 1) continue heatmap_pixel_values = np.array(heatmap_pixel_values) # Id_Images = self.get_ID(numpy_images,masks_list) mask_pixel_values = numpy_to_pt(numpy_depth_images,True) heatmap_pixel_values = numpy_to_pt(heatmap_pixel_values,True) # Id_Images = numpy_to_pt(np.array(Id_Images)) Id_Images = 0 # Load motion values motion_values = 180 # with open(motion_values_file, 'r') as file: # motion_values = float(file.read().strip()) return pixel_values, mask_pixel_values, motion_values, heatmap_pixel_values, Id_Images def __len__(self): return self.length def coordinates_normalize(self,center_coordinates): first_point = center_coordinates[0] center_coordinates = [one-first_point for one in center_coordinates] return center_coordinates def normalize(self, images): """ Normalize an image array to [-1,1]. """ return 2.0 * images - 1.0 def normalize_sam(self, images): """ Normalize an image array to [-1,1]. """ return (images - torch.tensor([0.485, 0.456, 0.406]).unsqueeze(0).unsqueeze(-1).unsqueeze(-1))/torch.tensor([0.229, 0.224, 0.225]).unsqueeze(0).unsqueeze(-1).unsqueeze(-1) def __getitem__(self, idx): pixel_values, depth_pixel_values,motion_values,heatmap_pixel_values,Id_Images = self.get_batch(idx) pixel_values = self.normalize(pixel_values) # Id_Images = self.normalize_sam(Id_Images) sample = dict(pixel_values=pixel_values, depth_pixel_values=depth_pixel_values, motion_values=motion_values,heatmap_pixel_values=heatmap_pixel_values,Id_Images=Id_Images) return sample if __name__ == "__main__": from util import save_videos_grid dataset = YoutubeVos( video_folder = "/mmu-ocr/weijiawu/MovieDiffusion/svd-temporal-controlnet/data/ref-youtube-vos/train/JPEGImages", ann_folder = "/mmu-ocr/weijiawu/MovieDiffusion/svd-temporal-controlnet/data/ref-youtube-vos/train/Annotations", motion_folder = "", sample_size=256, sample_stride=1, sample_n_frames=16 ) # import pdb # pdb.set_trace() inverse_process = transforms.Compose([ transforms.Normalize(mean=[-0.485/0.229, -0.456/0.224, -0.406/0.225], std=[1/0.229, 1/0.224, 1/0.225]), ]) dataloader = torch.utils.data.DataLoader(dataset, batch_size=1, num_workers=16,) for idx, batch in enumerate(dataloader): images = ((batch["pixel_values"][0].permute(0,2,3,1)+1)/2)*255 masks = batch["depth_pixel_values"][0].permute(0,2,3,1)*255 heatmaps = batch["heatmap_pixel_values"][0].permute(0,2,3,1) # Id_Images = ((batch["Id_Images"][0])*torch.tensor([0.229, 0.224, 0.225]).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)+torch.tensor([0.485, 0.456, 0.406]).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)).permute(0,2,3,1)*255 # center_coordinates = batch["center_coordinates"] print(batch["pixel_values"].shape) # print(Id_Images.shape) for i in range(images.shape[0]): image = images[i].numpy().astype(np.uint8) # print(Id_Images[i].shape) # Id_Image = inverse_process(Id_Images[i]).permute(1,2,0).numpy().astype(np.uint8) # Id_Image = Id_Images[i].numpy().astype(np.uint8) # print(Id_Image.shape) mask = masks[i].numpy() heatmap = heatmaps[i].numpy() # center_coordinate = center_coordinates[i][0][:2].numpy().astype(np.uint8) # print(mask.shape) # print(center_coordinate) # mask[center_coordinate[0]:center_coordinate[0]+10,center_coordinate[1]:center_coordinate[1]+10]=125 print(np.unique(mask)) # print(Id_Image.shape) cv2.imwrite("./vis/image_{}.jpg".format(i), image) # cv2.imwrite("./vis/Id_Image_{}.jpg".format(i), Id_Image) cv2.imwrite("./vis/mask_{}.jpg".format(i), mask.astype(np.uint8)) cv2.imwrite("./vis/heatmap_{}.jpg".format(i), heatmap.astype(np.uint8)) cv2.imwrite("./vis/{}.jpg".format(i), heatmap.astype(np.uint8)*0.5+image*0.5) # save_videos_grid(batch["pixel_values"][i:i+1].permute(0,2,1,3,4), os.path.join(".", f"{idx}-{i}.mp4"), rescale=True) break