171 lines
8.6 KiB
Python
171 lines
8.6 KiB
Python
|
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, train):
|
|||
|
|
super(YoloDataset, self).__init__()
|
|||
|
|
self.annotation_lines = annotation_lines # 记录训练集或测试集的文件的路径，这个是可以全部载入的
|
|||
|
|
self.input_shape = input_shape # 这里是 [416, 416]
|
|||
|
|
self.num_classes = num_classes # 这里是20
|
|||
|
|
self.length = len(self.annotation_lines) # 数据的数量
|
|||
|
|
self.train = train # 是否是训练集的标记
|
|||
|
|
|
|||
|
|
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
|
|||
|
|
# ---------------------------------------------------#
|
|||
|
|
image, box = self.get_random_data(self.annotation_lines[index], self.input_shape[0:2],
|
|||
|
|
random=self.train) # 自定义的数据增强
|
|||
|
|
image = np.transpose(preprocess_input(np.array(image, dtype=np.float32)), (2, 0, 1)) # 像素值归到0~1之间，然后变换坐标轴
|
|||
|
|
box = np.array(box, dtype=np.float32) # 转为numpy。np中常用的是创建新类型的array。
|
|||
|
|
if len(box) != 0:
|
|||
|
|
box[:, [0, 2]] = box[:, [0, 2]] / self.input_shape[1] # 把框的坐标归一化
|
|||
|
|
box[:, [1, 3]] = box[:, [1, 3]] / self.input_shape[0]
|
|||
|
|
|
|||
|
|
box[:, 2:4] = box[:, 2:4] - box[:, 0:2] # box第0，1维记录中心点 box第2，3维记录宽高
|
|||
|
|
box[:, 0:2] = box[:, 0:2] + box[:, 2:4] / 2 # box第0，1维记录中心点
|
|||
|
|
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() # 以空格、回车等分隔字符串
|
|||
|
|
# ------------------------------#
|
|||
|
|
# 读取图像并转换成RGB图像
|
|||
|
|
# ------------------------------#
|
|||
|
|
image = Image.open(line[0]) # line[0] 是图片的地址
|
|||
|
|
image = cvtColor(image) # 这里啥也没干
|
|||
|
|
# ------------------------------#
|
|||
|
|
# 获得图像的高宽与目标高宽
|
|||
|
|
# ------------------------------#
|
|||
|
|
iw, ih = image.size # 获取图像的原始尺寸
|
|||
|
|
h, w = input_shape
|
|||
|
|
# ------------------------------#
|
|||
|
|
# 获得预测框
|
|||
|
|
# ------------------------------#
|
|||
|
|
box = np.array([np.array(list(map(int, box.split(',')))) for box in line[1:]]) # 从python二维矩阵转到 numpy二维矩阵
|
|||
|
|
|
|||
|
|
if not random: # 没进入这里面
|
|||
|
|
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
|
|||
|
|
|
|||
|
|
# ------------------------------------------#
|
|||
|
|
# 对原始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) # (iw*随机) / (ih*随机)
|
|||
|
|
scale = self.rand(.25, 2) # 随机一个缩放比例
|
|||
|
|
if new_ar < 1: # 原图高大
|
|||
|
|
nh = int(scale * h) # 新图先缩放高
|
|||
|
|
nw = int(nh * new_ar)
|
|||
|
|
else: # 原图宽大
|
|||
|
|
nw = int(scale * w) # 新的宽从 预期宽中 乘以随机的比例
|
|||
|
|
nh = int(nw / new_ar) # 新的宽、高比，也是 new_ar, 也就是也是宽大
|
|||
|
|
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)) # 在(0, w - nw)找一个点作为新图的放置点
|
|||
|
|
dy = int(self.rand(0, h - nh))
|
|||
|
|
new_image = Image.new('RGB', (w, h), (128, 128, 128)) # 画一个 412, 412大小的灰图
|
|||
|
|
new_image.paste(image, (dx, dy)) # 在这里看看两者的区别
|
|||
|
|
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)
|
|||
|
|
# LUT是look-up table查找表的意思,cv2.LUT(src, lut, dst=None)的作用是对输入的src执行查找表lut转换
|
|||
|
|
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) # image_data在这里还是unit8类型
|
|||
|
|
|
|||
|
|
# ---------------------------------#
|
|||
|
|
# 对真实框进行调整
|
|||
|
|
# ---------------------------------#
|
|||
|
|
if len(box) > 0: # 如果有box
|
|||
|
|
np.random.shuffle(box)
|
|||
|
|
box[:, [0, 2]] = box[:, [0, 2]] * nw / iw + dx # 所有行的第0列和2列，也就是 x 坐标， 除以iw找到占原图的比例，再乘以nw，是新图的比例，再加dx是新图中的偏移
|
|||
|
|
box[:, [1, 3]] = box[:, [1, 3]] * nh / ih + dy
|
|||
|
|
if flip:
|
|||
|
|
box[:, [0, 2]] = w - box[:, [2, 0]] # 如果有水平翻转，则x坐标变换为416-x，并且x0 和 x1的位置互换一下
|
|||
|
|
box[:, 0:2][box[:, 0:2] < 0] = 0 # 对于左上角的点在图像外（小于0），则把对应的位置的坐标置为0 # 右下角的点不会小于0吗？
|
|||
|
|
box[:, 2][box[:, 2] > w] = w # 对于右下角的横坐标点超出图的，则置为w # 右下角不会超出图吗？
|
|||
|
|
box[:, 3][box[:, 3] > h] = h # 对于右下角的纵坐标点超出图的，则置为h
|
|||
|
|
box_w = box[:, 2] - box[:, 0]
|
|||
|
|
box_h = box[:, 3] - box[:, 1]
|
|||
|
|
box = box[np.logical_and(box_w > 1, box_h > 1)] # 多余的检查？如果宽、高大于至少1，则保留下来
|
|||
|
|
|
|||
|
|
return image_data, box # box依然是左上角和右下角的形式
|
|||
|
|
|
|||
|
|
|
|||
|
|
# DataLoader中collate_fn使用
|
|||
|
|
def yolo_dataset_collate(batch):
|
|||
|
|
images = [] # 这是是一个batch大小的列表，每一项是 image_data, box。需要把image放一堆，box放一堆
|
|||
|
|
bboxes = []
|
|||
|
|
for img, box in batch:
|
|||
|
|
images.append(img) # images在这里已经是0~1的float32类型了
|
|||
|
|
bboxes.append(box)
|
|||
|
|
images = torch.from_numpy(np.array(images)).type(torch.FloatTensor) # 转换为 batch_size, C, H, W 的数据
|
|||
|
|
bboxes = [torch.from_numpy(ann).type(torch.FloatTensor) for ann in bboxes] # 转换为一个列表，每个元素是一组二维Tensor
|
|||
|
|
return images, bboxes
|