from random import sample, shuffle import cv2 import numpy as np import torch from PIL import Image from torch.utils.data.dataset import Dataset from utils.utils import cvtColor, preprocess_input class YoloDataset(Dataset): def __init__(self, annotation_lines, input_shape, num_classes, epoch_length, mosaic, train, mosaic_ratio = 0.7): super(YoloDataset, self).__init__() self.annotation_lines = annotation_lines self.input_shape = input_shape self.num_classes = num_classes self.epoch_length = epoch_length self.mosaic = mosaic self.train = train self.mosaic_ratio = mosaic_ratio self.epoch_now = -1 self.length = len(self.annotation_lines) def __len__(self): return self.length def __getitem__(self, index): index = index % self.length #---------------------------------------------------# # 训练时进行数据的随机增强 # 验证时不进行数据的随机增强 #---------------------------------------------------# if self.mosaic: if self.rand() < 0.5 and self.epoch_now < self.epoch_length * self.mosaic_ratio: lines = sample(self.annotation_lines, 3) lines.append(self.annotation_lines[index]) shuffle(lines) image, box = self.get_random_data_with_Mosaic(lines, self.input_shape) else: image, box = self.get_random_data(self.annotation_lines[index], self.input_shape, random = self.train) else: image, box = self.get_random_data(self.annotation_lines[index], self.input_shape, random = self.train) image = np.transpose(preprocess_input(np.array(image, dtype=np.float32)), (2, 0, 1)) box = np.array(box, dtype=np.float32) if len(box) != 0: box[:, [0, 2]] = box[:, [0, 2]] / self.input_shape[1] box[:, [1, 3]] = box[:, [1, 3]] / self.input_shape[0] box[:, 2:4] = box[:, 2:4] - box[:, 0:2] box[:, 0:2] = box[:, 0:2] + box[:, 2:4] / 2 return image, box def rand(self, a=0, b=1): return np.random.rand()*(b-a) + a def get_random_data(self, annotation_line, input_shape, jitter=.3, hue=.1, sat=0.7, val=0.4, random=True): line = annotation_line.split() #------------------------------# # 读取图像并转换成RGB图像 #------------------------------# image = Image.open(line[0]) image = cvtColor(image) #------------------------------# # 获得图像的高宽与目标高宽 #------------------------------# iw, ih = image.size h, w = input_shape #------------------------------# # 获得预测框 #------------------------------# box = np.array([np.array(list(map(int,box.split(',')))) for box in line[1:]]) if not random: scale = min(w/iw, h/ih) nw = int(iw*scale) nh = int(ih*scale) dx = (w-nw)//2 dy = (h-nh)//2 #---------------------------------# # 将图像多余的部分加上灰条 #---------------------------------# image = image.resize((nw,nh), Image.BICUBIC) new_image = Image.new('RGB', (w,h), (128,128,128)) new_image.paste(image, (dx, dy)) image_data = np.array(new_image, np.float32) #---------------------------------# # 对真实框进行调整 #---------------------------------# if len(box)>0: np.random.shuffle(box) box[:, [0,2]] = box[:, [0,2]]*nw/iw + dx box[:, [1,3]] = box[:, [1,3]]*nh/ih + dy box[:, 0:2][box[:, 0:2]<0] = 0 box[:, 2][box[:, 2]>w] = w box[:, 3][box[:, 3]>h] = h box_w = box[:, 2] - box[:, 0] box_h = box[:, 3] - box[:, 1] box = box[np.logical_and(box_w>1, box_h>1)] # discard invalid box return image_data, box #------------------------------------------# # 对图像进行缩放并且进行长和宽的扭曲 #------------------------------------------# new_ar = iw/ih * self.rand(1-jitter,1+jitter) / self.rand(1-jitter,1+jitter) scale = self.rand(.25, 2) if new_ar < 1: nh = int(scale*h) nw = int(nh*new_ar) else: nw = int(scale*w) nh = int(nw/new_ar) image = image.resize((nw,nh), Image.BICUBIC) #------------------------------------------# # 将图像多余的部分加上灰条 #------------------------------------------# dx = int(self.rand(0, w-nw)) dy = int(self.rand(0, h-nh)) new_image = Image.new('RGB', (w,h), (128,128,128)) new_image.paste(image, (dx, dy)) image = new_image #------------------------------------------# # 翻转图像 #------------------------------------------# flip = self.rand()<.5 if flip: image = image.transpose(Image.FLIP_LEFT_RIGHT) image_data = np.array(image, np.uint8) #---------------------------------# # 对图像进行色域变换 # 计算色域变换的参数 #---------------------------------# r = np.random.uniform(-1, 1, 3) * [hue, sat, val] + 1 #---------------------------------# # 将图像转到HSV上 #---------------------------------# hue, sat, val = cv2.split(cv2.cvtColor(image_data, cv2.COLOR_RGB2HSV)) dtype = image_data.dtype #---------------------------------# # 应用变换 #---------------------------------# x = np.arange(0, 256, dtype=r.dtype) lut_hue = ((x * r[0]) % 180).astype(dtype) lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) lut_val = np.clip(x * r[2], 0, 255).astype(dtype) image_data = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) image_data = cv2.cvtColor(image_data, cv2.COLOR_HSV2RGB) #---------------------------------# # 对真实框进行调整 #---------------------------------# if len(box)>0: np.random.shuffle(box) box[:, [0,2]] = box[:, [0,2]]*nw/iw + dx box[:, [1,3]] = box[:, [1,3]]*nh/ih + dy if flip: box[:, [0,2]] = w - box[:, [2,0]] box[:, 0:2][box[:, 0:2]<0] = 0 box[:, 2][box[:, 2]>w] = w box[:, 3][box[:, 3]>h] = h box_w = box[:, 2] - box[:, 0] box_h = box[:, 3] - box[:, 1] box = box[np.logical_and(box_w>1, box_h>1)] return image_data, box def merge_bboxes(self, bboxes, cutx, cuty): merge_bbox = [] for i in range(len(bboxes)): for box in bboxes[i]: tmp_box = [] x1, y1, x2, y2 = box[0], box[1], box[2], box[3] if i == 0: if y1 > cuty or x1 > cutx: continue if y2 >= cuty and y1 <= cuty: y2 = cuty if x2 >= cutx and x1 <= cutx: x2 = cutx if i == 1: if y2 < cuty or x1 > cutx: continue if y2 >= cuty and y1 <= cuty: y1 = cuty if x2 >= cutx and x1 <= cutx: x2 = cutx if i == 2: if y2 < cuty or x2 < cutx: continue if y2 >= cuty and y1 <= cuty: y1 = cuty if x2 >= cutx and x1 <= cutx: x1 = cutx if i == 3: if y1 > cuty or x2 < cutx: continue if y2 >= cuty and y1 <= cuty: y2 = cuty if x2 >= cutx and x1 <= cutx: x1 = cutx tmp_box.append(x1) tmp_box.append(y1) tmp_box.append(x2) tmp_box.append(y2) tmp_box.append(box[-1]) merge_bbox.append(tmp_box) return merge_bbox def get_random_data_with_Mosaic(self, annotation_line, input_shape, jitter=0.3, hue=.1, sat=0.7, val=0.4): h, w = input_shape min_offset_x = self.rand(0.3, 0.7) min_offset_y = self.rand(0.3, 0.7) image_datas = [] box_datas = [] index = 0 for line in annotation_line: #---------------------------------# # 每一行进行分割 #---------------------------------# line_content = line.split() #---------------------------------# # 打开图片 #---------------------------------# image = Image.open(line_content[0]) image = cvtColor(image) #---------------------------------# # 图片的大小 #---------------------------------# iw, ih = image.size #---------------------------------# # 保存框的位置 #---------------------------------# box = np.array([np.array(list(map(int,box.split(',')))) for box in line_content[1:]]) #---------------------------------# # 是否翻转图片 #---------------------------------# flip = self.rand()<.5 if flip and len(box)>0: image = image.transpose(Image.FLIP_LEFT_RIGHT) box[:, [0,2]] = iw - box[:, [2,0]] #------------------------------------------# # 对图像进行缩放并且进行长和宽的扭曲 #------------------------------------------# new_ar = iw/ih * self.rand(1-jitter,1+jitter) / self.rand(1-jitter,1+jitter) scale = self.rand(.4, 1) if new_ar < 1: nh = int(scale*h) nw = int(nh*new_ar) else: nw = int(scale*w) nh = int(nw/new_ar) image = image.resize((nw, nh), Image.BICUBIC) #-----------------------------------------------# # 将图片进行放置，分别对应四张分割图片的位置 #-----------------------------------------------# if index == 0: dx = int(w*min_offset_x) - nw dy = int(h*min_offset_y) - nh elif index == 1: dx = int(w*min_offset_x) - nw dy = int(h*min_offset_y) elif index == 2: dx = int(w*min_offset_x) dy = int(h*min_offset_y) elif index == 3: dx = int(w*min_offset_x) dy = int(h*min_offset_y) - nh new_image = Image.new('RGB', (w,h), (128,128,128)) new_image.paste(image, (dx, dy)) image_data = np.array(new_image) index = index + 1 box_data = [] #---------------------------------# # 对box进行重新处理 #---------------------------------# if len(box)>0: np.random.shuffle(box) box[:, [0,2]] = box[:, [0,2]]*nw/iw + dx box[:, [1,3]] = box[:, [1,3]]*nh/ih + dy box[:, 0:2][box[:, 0:2]<0] = 0 box[:, 2][box[:, 2]>w] = w box[:, 3][box[:, 3]>h] = h box_w = box[:, 2] - box[:, 0] box_h = box[:, 3] - box[:, 1] box = box[np.logical_and(box_w>1, box_h>1)] box_data = np.zeros((len(box),5)) box_data[:len(box)] = box image_datas.append(image_data) box_datas.append(box_data) #---------------------------------# # 将图片分割，放在一起 #---------------------------------# cutx = int(w * min_offset_x) cuty = int(h * min_offset_y) new_image = np.zeros([h, w, 3]) new_image[:cuty, :cutx, :] = image_datas[0][:cuty, :cutx, :] new_image[cuty:, :cutx, :] = image_datas[1][cuty:, :cutx, :] new_image[cuty:, cutx:, :] = image_datas[2][cuty:, cutx:, :] new_image[:cuty, cutx:, :] = image_datas[3][:cuty, cutx:, :] new_image = np.array(new_image, np.uint8) #---------------------------------# # 对图像进行色域变换 # 计算色域变换的参数 #---------------------------------# r = np.random.uniform(-1, 1, 3) * [hue, sat, val] + 1 #---------------------------------# # 将图像转到HSV上 #---------------------------------# hue, sat, val = cv2.split(cv2.cvtColor(new_image, cv2.COLOR_RGB2HSV)) dtype = new_image.dtype #---------------------------------# # 应用变换 #---------------------------------# x = np.arange(0, 256, dtype=r.dtype) lut_hue = ((x * r[0]) % 180).astype(dtype) lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) lut_val = np.clip(x * r[2], 0, 255).astype(dtype) new_image = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) new_image = cv2.cvtColor(new_image, cv2.COLOR_HSV2RGB) #---------------------------------# # 对框进行进一步的处理 #---------------------------------# new_boxes = self.merge_bboxes(box_datas, cutx, cuty) return new_image, new_boxes # DataLoader中collate_fn使用 def yolo_dataset_collate(batch): images = [] bboxes = [] for img, box in batch: images.append(img) bboxes.append(box) images = torch.from_numpy(np.array(images)).type(torch.FloatTensor) bboxes = [torch.from_numpy(ann).type(torch.FloatTensor) for ann in bboxes] return images, bboxes