4.2 目标检测YOLO-V3算法--数据预处理&数据增广(百度架构师手把手带你零基础实践深度学习原版笔记系列)

import numpy as np
import cv2
from PIL import Image, ImageEnhance
import random

#也是一种数据增强

# 随机改变亮暗、对比度和颜色等
def random_distort(img):
    # 随机改变亮度
    def random_brightness(img, lower=0.5, upper=1.5):
        #随机产生定义域范围内的数
        e = np.random.uniform(lower, upper)
        return ImageEnhance.Brightness(img).enhance(e)
    # 随机改变对比度
    def random_contrast(img, lower=0.5, upper=1.5):
        e = np.random.uniform(lower, upper)
        return ImageEnhance.Contrast(img).enhance(e)
    # 随机改变颜色
    def random_color(img, lower=0.5, upper=1.5):
        e = np.random.uniform(lower, upper)
        return ImageEnhance.Color(img).enhance(e)

    ops = [random_brightness, random_contrast, random_color]
    np.random.shuffle(ops)

    img = Image.fromarray(img)
    #这种去函数的方法就很离谱
    img = ops[0](img)
    img = ops[1](img)
    img = ops[2](img)
    img = np.asarray(img)

    return img

随机填充

# 随机填充
def random_expand(img,
                  gtboxes,
                  max_ratio=4.,
                  fill=None,
                  keep_ratio=True,
                  thresh=0.5):
    if random.random() > thresh:
        return img, gtboxes

    if max_ratio < 1.0:
        return img, gtboxes

    h, w, c = img.shape
    ratio_x = random.uniform(1, max_ratio)
    if keep_ratio:
        ratio_y = ratio_x
    else:
        ratio_y = random.uniform(1, max_ratio)
    oh = int(h * ratio_y)
    ow = int(w * ratio_x)
    off_x = random.randint(0, ow - w)
    off_y = random.randint(0, oh - h)

    out_img = np.zeros((oh, ow, c))
    if fill and len(fill) == c:
        for i in range(c):
            out_img[:, :, i] = fill[i] * 255.0

    out_img[off_y:off_y + h, off_x:off_x + w, :] = img
    gtboxes[:, 0] = ((gtboxes[:, 0] * w) + off_x) / float(ow)
    gtboxes[:, 1] = ((gtboxes[:, 1] * h) + off_y) / float(oh)
    gtboxes[:, 2] = gtboxes[:, 2] / ratio_x
    gtboxes[:, 3] = gtboxes[:, 3] / ratio_y

    return out_img.astype('uint8'), gtboxes

随机裁剪

随机裁剪之前需要先定义两个函数，multi_box_iou_xywh和box_crop这两个函数将被保存在box_utils.py文件中。

import numpy as np

def multi_box_iou_xywh(box1, box2):
    """
    In this case, box1 or box2 can contain multi boxes.
    Only two cases can be processed in this method:
       1, box1 and box2 have the same shape, box1.shape == box2.shape
       2, either box1 or box2 contains only one box, len(box1) == 1 or len(box2) == 1
    If the shape of box1 and box2 does not match, and both of them contain multi boxes, it will be wrong.
    """
    assert box1.shape[-1] == 4, "Box1 shape[-1] should be 4."
    assert box2.shape[-1] == 4, "Box2 shape[-1] should be 4."


    b1_x1, b1_x2 = box1[:, 0] - box1[:, 2] / 2, box1[:, 0] + box1[:, 2] / 2
    b1_y1, b1_y2 = box1[:, 1] - box1[:, 3] / 2, box1[:, 1] + box1[:, 3] / 2
    b2_x1, b2_x2 = box2[:, 0] - box2[:, 2] / 2, box2[:, 0] + box2[:, 2] / 2
    b2_y1, b2_y2 = box2[:, 1] - box2[:, 3] / 2, box2[:, 1] + box2[:, 3] / 2

    inter_x1 = np.maximum(b1_x1, b2_x1)
    inter_x2 = np.minimum(b1_x2, b2_x2)
    inter_y1 = np.maximum(b1_y1, b2_y1)
    inter_y2 =