實驗條件：

1. 從1張影象隨機裁剪100張影象
2. 裁剪出影象的大小為 60 x 60
3. IoU 大於等於 th=0.6 的裁剪框用紅色標出，其它裁剪框用藍色標出
4. IoU 比對原始區域用綠框標出
   

實驗程式碼：

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),(0,255),1)
   label = 1
  else:
   cv.rectangle(img,(255,0),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,Crop_N=100,th=0.6)

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

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

實驗結果：

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