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