提取照片中军旗棋子，供文本识别使用
本质：提取矩形四个角落，并进行透视变换；
检测矩形步骤：
图像预处理
边缘检测
提取轮廓
检测凸包
角点检测
实例：
原始图像
"""
#!/usr/bin/env python
# -*- coding:utf-8 -*-
@desc: 用手机拍摄军旗棋子照片，将这两个棋子内容从照片中提取出来，供下一步文本识别使用
坐标返回顺序
[左上，右上，右下，左下]
"""
import cv2
import numpy as np
import math
class Config(object):
def __init__(self):
pass
src = r"D:workplacedataopencv20190905214945506.png"
resize_rate = 0.5
min_area = 5000
min_contours = 8
threshold_thresh = 50
epsilon_start = 10
epsilon_step = 10
def order_points(pts):
"""
点集排序
:param pts:
:return:
"""
rect = np.zeros((4,2), dtype="float32")
s = pts.sum(axis=1)
rect[0] = pts[np.argmin(s)]
rect[2] = pts[np.argmax(s)]
diff = np.diff(pts, axis=1)
rect[1] = pts[np.argmin(diff)]
rect[3] = pts[np.argmax(diff)]
return rect
def point_distance(a, b):
"""
求两点之间的距离
:param a:
:param b:
:return:
"""
return int(np.sqrt(np.sum(np.square(a-b))))
def bounding_box(idx, c):
"""
找出外接四边形
:param idx:
:param c: 轮廓坐标数组
:return:
"""
if len(c) < Config.min_contours:
return None
epsilon = Config.epsilon_start
while True:
approx_box = cv2.approxPolyDP(c, epsilon, True)
# 求出拟合得到的多边形
the_area = math.fabs(cv2.contourArea(approx_box))
# 输出拟合信息
print("contour idx: %d, contour_len: %d,"
" epsilon: %d, approx_len: %d, approve_area: %s"%(idx, len(c), epsilon, len(approx_box),the_area))
if (len(approx_box) < 4):
return None
if the_area > Config.min_area:
if (len(approx_box) > 4):
epsilon += Config.epsilon_step
continue
else: # approx 的长度为4，表明已经拟合成矩形了
# 转换成4*2的数组
approx_box = approx_box.reshape((4,2))
return approx_box
else:
print("failed to find boundingBox, idx = %d area = %f"%(idx, the_area))
return None
def main():
img = cv2.imread(Config.src)
# cv2.imshow("origin", img)
# 获取原始图像大小
src_height, src_width, channels = img.shape
# 对图像进行缩放
img = cv2.resize(img, (int(src_width*Config.resize_rate), int(src_height * Config.resize_rate)))
# cv2.imshow("resize_img", img)
# 转灰度图像
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 中值滤波，消除噪声
binary = cv2.medianBlur(gray,3)
# 转换为二值图像
ret, binary = cv2.threshold(binary, Config.threshold_thresh, 255, cv2.THRESH_BINARY)
# 显示转换后的二值图像
cv2.imshow("binary", binary)
# 进行二次腐蚀操作
# 腐蚀操作会腐蚀图像中白色像素， 可以将断开的线段连接起来
binary = cv2.erode(binary, None, iterations=2)
cv2.imshow("erode", binary)
# canny 边缘检测
binary = cv2.Canny(binary, 0, 60, apertureSize=3)
cv2.imshow("Canny", binary)
# 提取轮廓
_, contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# 输出轮廓数目
print("the count of contour is %d
"%(len(contours)))
# 针对每个轮廓，拟合外接四边形，如果成功，则将改区域切割出来， 做透视变换， 并保存图片文件
for idx, c in enumerate(contours):
approx_box = bounding_box(idx, c)
if approx_box is None:
continue
# 获取最小矩形包络
rect = cv2.minAreaRect(approx_box)
box = cv2.boxPoints(rect)
box = np.int0(box)
box = box.reshape(4,2)
box = order_points(box)
print("bounding_box:
", box)
# 切割区域的原始位置
src_rect = order_points(approx_box)
print("src_rect:
", src_rect)
w, h = point_distance(box[0], box[1]), point_distance(box[1], box[2])
print("w = %d, h = %d"%(w, h))
# 生成透视变换矩阵
dst_rect = np.array([
[0,0],
[w-1,0],
[w-1,h-1],
[0,h-1]
], dtype="float32")
# 透视变换
M = cv2.getPerspectiveTransform(src_rect, dst_rect)
# 得到透视后的图像
warped = cv2.warpPerspective(img, M, (w,h))
# 将变换后的结果图像写入png文件
cv2.imwrite(r"D:workplacedataopencv20190905214945507_%d.png"%(idx),
warped, [int(cv2.IMWRITE_PNG_COMPRESSION), 9])
print("END")
cv2.waitKey(0)
cv2.destroyAllWindows()
if __name__ == '__main__':
main()
结果示例：
提取结果：
