python实现图像随机裁剪的示例代码

实验条件：

1. 从1张图像随机裁剪100张图像
2. 裁剪出图像的大小为 60 x 60
3. IoU 大于等于 th=0.6 的裁剪框用红色标出，其它裁剪框用蓝色标出
4. IoU 比对原始区域用绿框标出

实验代码：

import cv2 as cv
import numpy as np

np.random.seed(0)

# get IoU overlap ratio
def iou(a, b):
	# get area of a
 area_a = (a[2] - a[0]) * (a[3] - a[1])
	# get area of b
 area_b = (b[2] - b[0]) * (b[3] - b[1])

	# get left top x of IoU
 iou_x1 = np.maximum(a[0], b[0])
	# get left top y of IoU
 iou_y1 = np.maximum(a[1], b[1])
	# get right bottom of IoU
 iou_x2 = np.minimum(a[2], b[2])
	# get right bottom of IoU
 iou_y2 = np.minimum(a[3], b[3])

	# get width of IoU
 iou_w = iou_x2 - iou_x1
	# get height of IoU
 iou_h = iou_y2 - iou_y1

	# get area of IoU
 area_iou = iou_w * iou_h
	# get overlap ratio between IoU and all area
 iou = area_iou / (area_a + area_b - area_iou)

 return iou

# crop and create database
def crop_bbox(img, gt, Crop_N=200, L=60, th=0.5):
 # get shape
 H, W, C = img.shape

 # each crop
 for i in range(Crop_N):
  # get left top x of crop bounding box
  x1 = np.random.randint(W - L)
  # get left top y of crop bounding box
  y1 = np.random.randint(H - L)
  # get right bottom x of crop bounding box
  x2 = x1 + L
  # get right bottom y of crop bounding box
  y2 = y1 + L

  # crop bounding box
  crop = np.array((x1, y1, x2, y2))

  # get IoU between crop box and gt
  _iou = iou(gt, crop)

  # assign label
  if _iou >= th:
   cv.rectangle(img, (x1, y1), (x2, y2), (0,0,255), 1)
   label = 1
  else:
   cv.rectangle(img, (x1, y1), (x2, y2), (255,0,0), 1)
   label = 0

 return img

# read image
img = cv.imread("../xiyi.jpg")
img1 = img.copy()
# gt bounding box
gt = np.array((87, 51, 169, 113), dtype=np.float32)

# get crop bounding box
img = crop_bbox(img, gt, Crop_N=100, L=60, th=0.6)

# draw gt
cv.rectangle(img, (gt[0], gt[1]), (gt[2], gt[3]), (0,255,0), 1)
cv.rectangle(img1,(gt[0], gt[1]), (gt[2], gt[3]), (0,255,0), 1)

cv.imshow("result1",img1)
cv.imshow("result", img)
cv.imwrite("out.jpg", img)
cv.waitKey(0)
cv.destroyAllWindows()

实验结果：

以上就是python实现图像随机裁剪的示例代码的详细内容，更多关于python 图像裁剪的资料请关注我们其它相关文章！