505 lines
22 KiB
Python
505 lines
22 KiB
Python
from random import sample, shuffle
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import cv2
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import numpy as np
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import torch
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from PIL import Image
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from torch.utils.data.dataset import Dataset
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from utils.utils import cvtColor, preprocess_input
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class YoloDataset(Dataset):
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def __init__(self, annotation_lines, input_shape, num_classes, anchors, anchors_mask, epoch_length, \
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mosaic, mixup, mosaic_prob, mixup_prob, train, special_aug_ratio = 0.7):
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super(YoloDataset, self).__init__()
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self.annotation_lines = annotation_lines
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self.input_shape = input_shape
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self.num_classes = num_classes
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self.anchors = anchors
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self.anchors_mask = anchors_mask
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self.epoch_length = epoch_length
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self.mosaic = mosaic
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self.mosaic_prob = mosaic_prob
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self.mixup = mixup
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self.mixup_prob = mixup_prob
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self.train = train
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self.special_aug_ratio = special_aug_ratio
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self.epoch_now = -1
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self.length = len(self.annotation_lines)
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self.bbox_attrs = 5 + num_classes
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self.threshold = 4
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def __len__(self):
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return self.length
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def __getitem__(self, index):
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index = index % self.length
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#---------------------------------------------------#
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# 训练时进行数据的随机增强
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# 验证时不进行数据的随机增强
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#---------------------------------------------------#
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if self.mosaic and self.rand() < self.mosaic_prob and self.epoch_now < self.epoch_length * self.special_aug_ratio:
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lines = sample(self.annotation_lines, 3)
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lines.append(self.annotation_lines[index])
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shuffle(lines)
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image, box = self.get_random_data_with_Mosaic(lines, self.input_shape)
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if self.mixup and self.rand() < self.mixup_prob:
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lines = sample(self.annotation_lines, 1)
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image_2, box_2 = self.get_random_data(lines[0], self.input_shape, random = self.train)
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image, box = self.get_random_data_with_MixUp(image, box, image_2, box_2)
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else:
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image, box = self.get_random_data(self.annotation_lines[index], self.input_shape, random = self.train)
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image = np.transpose(preprocess_input(np.array(image, dtype=np.float32)), (2, 0, 1))
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box = np.array(box, dtype=np.float32)
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if len(box) != 0:
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#---------------------------------------------------#
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# 对真实框进行归一化，调整到0-1之间
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#---------------------------------------------------#
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box[:, [0, 2]] = box[:, [0, 2]] / self.input_shape[1]
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box[:, [1, 3]] = box[:, [1, 3]] / self.input_shape[0]
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#---------------------------------------------------#
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# 序号为0、1的部分，为真实框的中心
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# 序号为2、3的部分，为真实框的宽高
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# 序号为4的部分，为真实框的种类
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#---------------------------------------------------#
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box[:, 2:4] = box[:, 2:4] - box[:, 0:2]
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box[:, 0:2] = box[:, 0:2] + box[:, 2:4] / 2
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y_true = self.get_target(box)
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return image, box, y_true
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def rand(self, a=0, b=1):
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return np.random.rand()*(b-a) + a
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def get_random_data(self, annotation_line, input_shape, jitter=.3, hue=.1, sat=0.7, val=0.4, random=True):
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line = annotation_line.split()
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#------------------------------#
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# 读取图像并转换成RGB图像
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#------------------------------#
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image = Image.open(line[0])
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image = cvtColor(image)
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#------------------------------#
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# 获得图像的高宽与目标高宽
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#------------------------------#
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iw, ih = image.size
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h, w = input_shape
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#------------------------------#
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# 获得预测框
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#------------------------------#
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box = np.array([np.array(list(map(int,box.split(',')))) for box in line[1:]])
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if not random:
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scale = min(w/iw, h/ih)
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nw = int(iw*scale)
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nh = int(ih*scale)
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dx = (w-nw)//2
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dy = (h-nh)//2
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#---------------------------------#
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# 将图像多余的部分加上灰条
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#---------------------------------#
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image = image.resize((nw,nh), Image.BICUBIC)
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new_image = Image.new('RGB', (w,h), (128,128,128))
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new_image.paste(image, (dx, dy))
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image_data = np.array(new_image, np.float32)
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#---------------------------------#
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# 对真实框进行调整
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#---------------------------------#
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if len(box)>0:
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np.random.shuffle(box)
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box[:, [0,2]] = box[:, [0,2]]*nw/iw + dx
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box[:, [1,3]] = box[:, [1,3]]*nh/ih + dy
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box[:, 0:2][box[:, 0:2]<0] = 0
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box[:, 2][box[:, 2]>w] = w
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box[:, 3][box[:, 3]>h] = h
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box_w = box[:, 2] - box[:, 0]
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box_h = box[:, 3] - box[:, 1]
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box = box[np.logical_and(box_w>1, box_h>1)] # discard invalid box
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return image_data, box
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#------------------------------------------#
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# 对图像进行缩放并且进行长和宽的扭曲
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#------------------------------------------#
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new_ar = iw/ih * self.rand(1-jitter,1+jitter) / self.rand(1-jitter,1+jitter)
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scale = self.rand(.25, 2)
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if new_ar < 1:
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nh = int(scale*h)
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nw = int(nh*new_ar)
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else:
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nw = int(scale*w)
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nh = int(nw/new_ar)
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image = image.resize((nw,nh), Image.BICUBIC)
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#------------------------------------------#
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# 将图像多余的部分加上灰条
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#------------------------------------------#
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dx = int(self.rand(0, w-nw))
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dy = int(self.rand(0, h-nh))
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new_image = Image.new('RGB', (w,h), (128,128,128))
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new_image.paste(image, (dx, dy))
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image = new_image
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#------------------------------------------#
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# 翻转图像
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#------------------------------------------#
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flip = self.rand()<.5
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if flip: image = image.transpose(Image.FLIP_LEFT_RIGHT)
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image_data = np.array(image, np.uint8)
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#---------------------------------#
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# 对图像进行色域变换
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# 计算色域变换的参数
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#---------------------------------#
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r = np.random.uniform(-1, 1, 3) * [hue, sat, val] + 1
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#---------------------------------#
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# 将图像转到HSV上
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#---------------------------------#
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hue, sat, val = cv2.split(cv2.cvtColor(image_data, cv2.COLOR_RGB2HSV))
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dtype = image_data.dtype
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#---------------------------------#
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# 应用变换
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#---------------------------------#
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x = np.arange(0, 256, dtype=r.dtype)
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lut_hue = ((x * r[0]) % 180).astype(dtype)
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lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)
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lut_val = np.clip(x * r[2], 0, 255).astype(dtype)
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image_data = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))
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image_data = cv2.cvtColor(image_data, cv2.COLOR_HSV2RGB)
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#---------------------------------#
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# 对真实框进行调整
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#---------------------------------#
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if len(box)>0:
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np.random.shuffle(box)
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box[:, [0,2]] = box[:, [0,2]]*nw/iw + dx
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box[:, [1,3]] = box[:, [1,3]]*nh/ih + dy
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if flip: box[:, [0,2]] = w - box[:, [2,0]]
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box[:, 0:2][box[:, 0:2]<0] = 0
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box[:, 2][box[:, 2]>w] = w
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box[:, 3][box[:, 3]>h] = h
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box_w = box[:, 2] - box[:, 0]
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box_h = box[:, 3] - box[:, 1]
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box = box[np.logical_and(box_w>1, box_h>1)]
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return image_data, box
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def merge_bboxes(self, bboxes, cutx, cuty):
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merge_bbox = []
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for i in range(len(bboxes)):
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for box in bboxes[i]:
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tmp_box = []
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x1, y1, x2, y2 = box[0], box[1], box[2], box[3]
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if i == 0:
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if y1 > cuty or x1 > cutx:
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continue
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if y2 >= cuty and y1 <= cuty:
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y2 = cuty
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if x2 >= cutx and x1 <= cutx:
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x2 = cutx
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if i == 1:
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if y2 < cuty or x1 > cutx:
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continue
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if y2 >= cuty and y1 <= cuty:
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y1 = cuty
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if x2 >= cutx and x1 <= cutx:
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x2 = cutx
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if i == 2:
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if y2 < cuty or x2 < cutx:
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continue
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if y2 >= cuty and y1 <= cuty:
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y1 = cuty
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if x2 >= cutx and x1 <= cutx:
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x1 = cutx
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if i == 3:
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if y1 > cuty or x2 < cutx:
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continue
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if y2 >= cuty and y1 <= cuty:
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y2 = cuty
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if x2 >= cutx and x1 <= cutx:
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x1 = cutx
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tmp_box.append(x1)
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tmp_box.append(y1)
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tmp_box.append(x2)
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tmp_box.append(y2)
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tmp_box.append(box[-1])
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merge_bbox.append(tmp_box)
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return merge_bbox
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def get_random_data_with_Mosaic(self, annotation_line, input_shape, jitter=0.3, hue=.1, sat=0.7, val=0.4):
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h, w = input_shape
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min_offset_x = self.rand(0.3, 0.7)
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min_offset_y = self.rand(0.3, 0.7)
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image_datas = []
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box_datas = []
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index = 0
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for line in annotation_line:
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#---------------------------------#
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# 每一行进行分割
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#---------------------------------#
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line_content = line.split()
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#---------------------------------#
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# 打开图片
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#---------------------------------#
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image = Image.open(line_content[0])
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image = cvtColor(image)
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#---------------------------------#
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# 图片的大小
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#---------------------------------#
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iw, ih = image.size
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#---------------------------------#
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# 保存框的位置
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#---------------------------------#
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box = np.array([np.array(list(map(int,box.split(',')))) for box in line_content[1:]])
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#---------------------------------#
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# 是否翻转图片
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#---------------------------------#
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flip = self.rand()<.5
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if flip and len(box)>0:
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image = image.transpose(Image.FLIP_LEFT_RIGHT)
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box[:, [0,2]] = iw - box[:, [2,0]]
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#------------------------------------------#
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# 对图像进行缩放并且进行长和宽的扭曲
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#------------------------------------------#
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new_ar = iw/ih * self.rand(1-jitter,1+jitter) / self.rand(1-jitter,1+jitter)
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scale = self.rand(.4, 1)
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if new_ar < 1:
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nh = int(scale*h)
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nw = int(nh*new_ar)
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else:
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nw = int(scale*w)
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nh = int(nw/new_ar)
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image = image.resize((nw, nh), Image.BICUBIC)
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#-----------------------------------------------#
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# 将图片进行放置，分别对应四张分割图片的位置
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#-----------------------------------------------#
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if index == 0:
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dx = int(w*min_offset_x) - nw
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dy = int(h*min_offset_y) - nh
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elif index == 1:
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dx = int(w*min_offset_x) - nw
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dy = int(h*min_offset_y)
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elif index == 2:
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dx = int(w*min_offset_x)
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dy = int(h*min_offset_y)
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elif index == 3:
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dx = int(w*min_offset_x)
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dy = int(h*min_offset_y) - nh
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new_image = Image.new('RGB', (w,h), (128,128,128))
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new_image.paste(image, (dx, dy))
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image_data = np.array(new_image)
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index = index + 1
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box_data = []
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#---------------------------------#
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# 对box进行重新处理
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#---------------------------------#
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if len(box)>0:
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np.random.shuffle(box)
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box[:, [0,2]] = box[:, [0,2]]*nw/iw + dx
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box[:, [1,3]] = box[:, [1,3]]*nh/ih + dy
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box[:, 0:2][box[:, 0:2]<0] = 0
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box[:, 2][box[:, 2]>w] = w
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box[:, 3][box[:, 3]>h] = h
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box_w = box[:, 2] - box[:, 0]
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box_h = box[:, 3] - box[:, 1]
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box = box[np.logical_and(box_w>1, box_h>1)]
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box_data = np.zeros((len(box),5))
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box_data[:len(box)] = box
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image_datas.append(image_data)
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box_datas.append(box_data)
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#---------------------------------#
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# 将图片分割，放在一起
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#---------------------------------#
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cutx = int(w * min_offset_x)
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cuty = int(h * min_offset_y)
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new_image = np.zeros([h, w, 3])
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new_image[:cuty, :cutx, :] = image_datas[0][:cuty, :cutx, :]
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new_image[cuty:, :cutx, :] = image_datas[1][cuty:, :cutx, :]
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new_image[cuty:, cutx:, :] = image_datas[2][cuty:, cutx:, :]
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new_image[:cuty, cutx:, :] = image_datas[3][:cuty, cutx:, :]
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new_image = np.array(new_image, np.uint8)
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#---------------------------------#
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# 对图像进行色域变换
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# 计算色域变换的参数
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#---------------------------------#
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r = np.random.uniform(-1, 1, 3) * [hue, sat, val] + 1
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#---------------------------------#
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# 将图像转到HSV上
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#---------------------------------#
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hue, sat, val = cv2.split(cv2.cvtColor(new_image, cv2.COLOR_RGB2HSV))
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dtype = new_image.dtype
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#---------------------------------#
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# 应用变换
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#---------------------------------#
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x = np.arange(0, 256, dtype=r.dtype)
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lut_hue = ((x * r[0]) % 180).astype(dtype)
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lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)
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lut_val = np.clip(x * r[2], 0, 255).astype(dtype)
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new_image = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))
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new_image = cv2.cvtColor(new_image, cv2.COLOR_HSV2RGB)
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#---------------------------------#
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# 对框进行进一步的处理
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#---------------------------------#
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new_boxes = self.merge_bboxes(box_datas, cutx, cuty)
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return new_image, new_boxes
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def get_random_data_with_MixUp(self, image_1, box_1, image_2, box_2):
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new_image = np.array(image_1, np.float32) * 0.5 + np.array(image_2, np.float32) * 0.5
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if len(box_1) == 0:
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new_boxes = box_2
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elif len(box_2) == 0:
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new_boxes = box_1
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else:
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new_boxes = np.concatenate([box_1, box_2], axis=0)
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return new_image, new_boxes
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def get_near_points(self, x, y, i, j):
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sub_x = x - i
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sub_y = y - j
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if sub_x > 0.5 and sub_y > 0.5:
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return [[0, 0], [1, 0], [0, 1]]
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elif sub_x < 0.5 and sub_y > 0.5:
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return [[0, 0], [-1, 0], [0, 1]]
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elif sub_x < 0.5 and sub_y < 0.5:
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return [[0, 0], [-1, 0], [0, -1]]
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else:
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return [[0, 0], [1, 0], [0, -1]]
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def get_target(self, targets):
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#-----------------------------------------------------------#
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# 一共有三个特征层数
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#-----------------------------------------------------------#
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num_layers = len(self.anchors_mask)
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input_shape = np.array(self.input_shape, dtype='int32')
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grid_shapes = [input_shape // {0:32, 1:16, 2:8, 3:4}[l] for l in range(num_layers)]
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y_true = [np.zeros((len(self.anchors_mask[l]), grid_shapes[l][0], grid_shapes[l][1], self.bbox_attrs), dtype='float32') for l in range(num_layers)]
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box_best_ratio = [np.zeros((len(self.anchors_mask[l]), grid_shapes[l][0], grid_shapes[l][1]), dtype='float32') for l in range(num_layers)]
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if len(targets) == 0:
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return y_true
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for l in range(num_layers):
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in_h, in_w = grid_shapes[l]
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anchors = np.array(self.anchors) / {0:32, 1:16, 2:8, 3:4}[l]
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batch_target = np.zeros_like(targets)
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#-------------------------------------------------------#
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# 计算出正样本在特征层上的中心点
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#-------------------------------------------------------#
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batch_target[:, [0,2]] = targets[:, [0,2]] * in_w
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batch_target[:, [1,3]] = targets[:, [1,3]] * in_h
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batch_target[:, 4] = targets[:, 4]
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#-------------------------------------------------------#
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# wh : num_true_box, 2
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# np.expand_dims(wh, 1) : num_true_box, 1, 2
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# anchors : 9, 2
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# np.expand_dims(anchors, 0) : 1, 9, 2
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#
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# ratios_of_gt_anchors代表每一个真实框和每一个先验框的宽高的比值
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# ratios_of_gt_anchors : num_true_box, 9, 2
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# ratios_of_anchors_gt代表每一个先验框和每一个真实框的宽高的比值
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# ratios_of_anchors_gt : num_true_box, 9, 2
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#
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# ratios : num_true_box, 9, 4
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# max_ratios代表每一个真实框和每一个先验框的宽高的比值的最大值
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# max_ratios : num_true_box, 9
|
||
#-------------------------------------------------------#
|
||
ratios_of_gt_anchors = np.expand_dims(batch_target[:, 2:4], 1) / np.expand_dims(anchors, 0)
|
||
ratios_of_anchors_gt = np.expand_dims(anchors, 0) / np.expand_dims(batch_target[:, 2:4], 1)
|
||
ratios = np.concatenate([ratios_of_gt_anchors, ratios_of_anchors_gt], axis = -1)
|
||
max_ratios = np.max(ratios, axis = -1)
|
||
|
||
for t, ratio in enumerate(max_ratios):
|
||
#-------------------------------------------------------#
|
||
# ratio : 9
|
||
#-------------------------------------------------------#
|
||
over_threshold = ratio < self.threshold
|
||
over_threshold[np.argmin(ratio)] = True
|
||
for k, mask in enumerate(self.anchors_mask[l]):
|
||
if not over_threshold[mask]:
|
||
continue
|
||
#----------------------------------------#
|
||
# 获得真实框属于哪个网格点
|
||
# x 1.25 => 1
|
||
# y 3.75 => 3
|
||
#----------------------------------------#
|
||
i = int(np.floor(batch_target[t, 0]))
|
||
j = int(np.floor(batch_target[t, 1]))
|
||
|
||
offsets = self.get_near_points(batch_target[t, 0], batch_target[t, 1], i, j)
|
||
for offset in offsets:
|
||
local_i = i + offset[0]
|
||
local_j = j + offset[1]
|
||
|
||
if local_i >= in_w or local_i < 0 or local_j >= in_h or local_j < 0:
|
||
continue
|
||
|
||
if box_best_ratio[l][k, local_j, local_i] != 0:
|
||
if box_best_ratio[l][k, local_j, local_i] > ratio[mask]:
|
||
y_true[l][k, local_j, local_i, :] = 0
|
||
else:
|
||
continue
|
||
|
||
#----------------------------------------#
|
||
# 取出真实框的种类
|
||
#----------------------------------------#
|
||
c = int(batch_target[t, 4])
|
||
|
||
#----------------------------------------#
|
||
# tx、ty代表中心调整参数的真实值
|
||
#----------------------------------------#
|
||
y_true[l][k, local_j, local_i, 0] = batch_target[t, 0]
|
||
y_true[l][k, local_j, local_i, 1] = batch_target[t, 1]
|
||
y_true[l][k, local_j, local_i, 2] = batch_target[t, 2]
|
||
y_true[l][k, local_j, local_i, 3] = batch_target[t, 3]
|
||
y_true[l][k, local_j, local_i, 4] = 1
|
||
y_true[l][k, local_j, local_i, c + 5] = 1
|
||
#----------------------------------------#
|
||
# 获得当前先验框最好的比例
|
||
#----------------------------------------#
|
||
box_best_ratio[l][k, local_j, local_i] = ratio[mask]
|
||
|
||
return y_true
|
||
|
||
# DataLoader中collate_fn使用
|
||
def yolo_dataset_collate(batch):
|
||
images = []
|
||
bboxes = []
|
||
y_trues = [[] for _ in batch[0][2]]
|
||
for img, box, y_true in batch:
|
||
images.append(img)
|
||
bboxes.append(box)
|
||
for i, sub_y_true in enumerate(y_true):
|
||
y_trues[i].append(sub_y_true)
|
||
|
||
images = torch.from_numpy(np.array(images)).type(torch.FloatTensor)
|
||
bboxes = [torch.from_numpy(ann).type(torch.FloatTensor) for ann in bboxes]
|
||
y_trues = [torch.from_numpy(np.array(ann, np.float32)).type(torch.FloatTensor) for ann in y_trues]
|
||
return images, bboxes,y_trues
|