目标检测：数据预处理——图像增广
作者：互联网
文章目录
一、数据增多（图像增广）
在计算机视觉中通常对图像做一些随机的变化，产生相似但又不完全相同的样本。
主要作用：
- 扩大训练数据集，抑制过拟合，提高模型泛化能力。
数据预处理
在计算机视觉中，通常会对图像做一些随机的变化，产生相似但又不完全相同的样本。主要作用是扩大训练数据集，抑制过拟合，提升模型的泛化能力，常用的方法主要有以下几种：
- 随机改变亮暗、对比度和颜色
- 随机填充
- 随机裁剪
- 随机缩放
- 随机翻转
- 随机打乱真实框排列顺序
下面我们分别使用numpy 实现这些数据增强方法。
随机改变亮暗、对比度和颜色等
import numpy as np
import cv2
from PIL import Image, ImageEnhance
import random
# 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):
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)
img = ops[0](img)
img = ops[1](img)
img = ops[2](img)
img = np.asarray(img)
return img
# 定义可视化函数，用于对比原图和图像增强的效果
import matplotlib.pyplot as plt
def visualize(srcimg, img_enhance):
# 图像可视化
plt.figure(num=2, figsize=(6,12))
plt.subplot(1,2,1)
plt.title('Src Image', color='#0000FF')
plt.axis('off') # 不显示坐标轴
plt.imshow(srcimg) # 显示原图片
# 对原图做 随机改变亮暗、对比度和颜色等 数据增强
srcimg_gtbox = records[0]['gt_bbox']
srcimg_label = records[0]['gt_class']
plt.subplot(1,2,2)
plt.title('Enhance Image', color='#0000FF')
plt.axis('off') # 不显示坐标轴
plt.imshow(img_enhance)
image_path = records[0]['im_file']
print("read image from file {}".format(image_path))
srcimg = Image.open(image_path)
# 将PIL读取的图像转换成array类型
srcimg = np.array(srcimg)
# 对原图做 随机改变亮暗、对比度和颜色等 数据增强
img_enhance = random_distort(srcimg)
visualize(srcimg, img_enhance)
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
# 对原图做 随机改变亮暗、对比度和颜色等 数据增强
srcimg_gtbox = records[0]['gt_bbox']
img_enhance, new_gtbox = random_expand(srcimg, srcimg_gtbox)
visualize(srcimg, img_enhance)
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 = np.minimum(b1_y2, b2_y2)
inter_w = inter_x2 - inter_x1
inter_h = inter_y2 - inter_y1
inter_w = np.clip(inter_w, a_min=0., a_max=None)
inter_h = np.clip(inter_h, a_min=0., a_max=None)
inter_area = inter_w * inter_h
b1_area = (b1_x2 - b1_x1) * (b1_y2 - b1_y1)
b2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1)
return inter_area / (b1_area + b2_area - inter_area)
def box_crop(boxes, labels, crop, img_shape):
x, y, w, h = map(float, crop)
im_w, im_h = map(float, img_shape)
boxes = boxes.copy()
boxes[:, 0], boxes[:, 2] = (boxes[:, 0] - boxes[:, 2] / 2) * im_w, (
boxes[:, 0] + boxes[:, 2] / 2) * im_w
boxes[:, 1], boxes[:, 3] = (boxes[:, 1] - boxes[:, 3] / 2) * im_h, (
boxes[:, 1] + boxes[:, 3] / 2) * im_h
crop_box = np.array([x, y, x + w, y + h])
centers = (boxes[:, :2] + boxes[:, 2:]) / 2.0
mask = np.logical_and(crop_box[:2] <= centers, centers <= crop_box[2:]).all(
axis=1)
boxes[:, :2] = np.maximum(boxes[:, :2], crop_box[:2])
boxes[:, 2:] = np.minimum(boxes[:, 2:], crop_box[2:])
boxes[:, :2] -= crop_box[:2]
boxes[:, 2:] -= crop_box[:2]
mask = np.logical_and(mask, (boxes[:, :2] < boxes[:, 2:]).all(axis=1))
boxes = boxes * np.expand_dims(mask.astype('float32'), axis=1)
labels = labels * mask.astype('float32')
boxes[:, 0], boxes[:, 2] = (boxes[:, 0] + boxes[:, 2]) / 2 / w, (
boxes[:, 2] - boxes[:, 0]) / w
boxes[:, 1], boxes[:, 3] = (boxes[:, 1] + boxes[:, 3]) / 2 / h, (
boxes[:, 3] - boxes[:, 1]) / h
return boxes, labels, mask.sum()
# 随机裁剪
def random_crop(img,
boxes,
labels,
scales=[0.3, 1.0],
max_ratio=2.0,
constraints=None,
max_trial=50):
if len(boxes) == 0:
return img, boxes
if not constraints:
constraints = [(0.1, 1.0), (0.3, 1.0), (0.5, 1.0), (0.7, 1.0),
(0.9, 1.0), (0.0, 1.0)]
img = Image.fromarray(img)
w, h = img.size
crops = [(0, 0, w, h)]
for min_iou, max_iou in constraints:
for _ in range(max_trial):
scale = random.uniform(scales[0], scales[1])
aspect_ratio = random.uniform(max(1 / max_ratio, scale * scale), \
min(max_ratio, 1 / scale / scale))
crop_h = int(h * scale / np.sqrt(aspect_ratio))
crop_w = int(w * scale * np.sqrt(aspect_ratio))
crop_x = random.randrange(w - crop_w)
crop_y = random.randrange(h - crop_h)
crop_box = np.array([[(crop_x + crop_w / 2.0) / w,
(crop_y + crop_h / 2.0) / h,
crop_w / float(w), crop_h / float(h)]])
iou = multi_box_iou_xywh(crop_box, boxes)
if min_iou <= iou.min() and max_iou >= iou.max():
crops.append((crop_x, crop_y, crop_w, crop_h))
break
while crops:
crop = crops.pop(np.random.randint(0, len(crops)))
crop_boxes, crop_labels, box_num = box_crop(boxes, labels, crop, (w, h))
if box_num < 1:
continue
img = img.crop((crop[0], crop[1], crop[0] + crop[2],
crop[1] + crop[3])).resize(img.size, Image.LANCZOS)
img = np.asarray(img)
return img, crop_boxes, crop_labels
img = np.asarray(img)
return img, boxes, labels
# 对原图做 随机改变亮暗、对比度和颜色等 数据增强
srcimg_gtbox = records[0]['gt_bbox']
srcimg_label = records[0]['gt_class']
img_enhance, new_labels, mask = random_crop(srcimg, srcimg_gtbox, srcimg_label)
visualize(srcimg, img_enhance)
随机缩放
# 随机缩放
def random_interp(img, size, interp=None):
interp_method = [
cv2.INTER_NEAREST,
cv2.INTER_LINEAR,
cv2.INTER_AREA,
cv2.INTER_CUBIC,
cv2.INTER_LANCZOS4,
]
if not interp or interp not in interp_method:
interp = interp_method[random.randint(0, len(interp_method) - 1)]
h, w, _ = img.shape
im_scale_x = size / float(w)
im_scale_y = size / float(h)
img = cv2.resize(
img, None, None, fx=im_scale_x, fy=im_scale_y, interpolation=interp)
return img
# 对原图做 随机改变亮暗、对比度和颜色等 数据增强
img_enhance = random_interp(srcimg, 640)
visualize(srcimg, img_enhance)
随机翻转
# 随机翻转
def random_flip(img, gtboxes, thresh=0.5):
if random.random() > thresh:
img = img[:, ::-1, :]
gtboxes[:, 0] = 1.0 - gtboxes[:, 0]
return img, gtboxes
# 对原图做 随机改变亮暗、对比度和颜色等 数据增强
img_enhance, box_enhance = random_flip(srcimg, srcimg_gtbox)
visualize(srcimg, img_enhance)
随机打乱真实框排列顺序
# 随机打乱真实框排列顺序
def shuffle_gtbox(gtbox, gtlabel):
gt = np.concatenate(
[gtbox, gtlabel[:, np.newaxis]], axis=1)
idx = np.arange(gt.shape[0])
np.random.shuffle(idx)
gt = gt[idx, :]
return gt[:, :4], gt[:, 4]
图像增广方法汇总
# 图像增广方法汇总
def image_augment(img, gtboxes, gtlabels, size, means=None):
# 随机改变亮暗、对比度和颜色等
img = random_distort(img)
# 随机填充
img, gtboxes = random_expand(img, gtboxes, fill=means)
# 随机裁剪
img, gtboxes, gtlabels, = random_crop(img, gtboxes, gtlabels)
# 随机缩放
img = random_interp(img, size)
# 随机翻转
img, gtboxes = random_flip(img, gtboxes)
# 随机打乱真实框排列顺序
gtboxes, gtlabels = shuffle_gtbox(gtboxes, gtlabels)
return img.astype('float32'), gtboxes.astype('float32'), gtlabels.astype('int32')
img_enhance, img_box, img_label = image_augment(srcimg, srcimg_gtbox, srcimg_label, size=320)
visualize(srcimg, img_enhance)
总结
标签：srcimg,img,增广,random,crop,随机,图像,np,预处理 来源： https://blog.csdn.net/pidzhengding/article/details/122728391