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update ch22
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‎ch21-轮廓Contours/21.4.3-形状匹配.py‎

Lines changed: 25 additions & 12 deletions
Original file line numberDiff line numberDiff line change
@@ -14,18 +14,31 @@
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import cv2
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import numpy as np
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img1 = cv2.imread('star.jpg', 0)
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img2 = cv2.imread('star2.jpg', 0)
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ret, thresh = cv2.threshold(img1, 127, 255, 0)
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ret, thresh2 = cv2.threshold(img2, 127, 255, 0)
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image,contours, hierarchy = cv2.findContours(thresh, 2, 1)
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cnt1 = contours[0]
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image,contours, hierarchy = cv2.findContours(thresh2, 2, 1)
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cnt2 = contours[0]
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ret = cv2.matchShapes(cnt1, cnt2, 1, 0.0)
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img_a = cv2.imread('star_a.jpg', 0)
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img_b = cv2.imread('star_b.jpg', 0)
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img_c = cv2.imread('star_c.jpg', 0)
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img = cv2.imread('star.jpg', 0)
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ret, thresh = cv2.threshold(img, 127, 255, 0)
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ret, thresh_a = cv2.threshold(img_a, 127, 255, 0)
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ret, thresh_b = cv2.threshold(img_b, 127, 255, 0)
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ret, thresh_c = cv2.threshold(img_c, 127, 255, 0)
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contours, hierarchy = cv2.findContours(thresh_a, 2, 1)
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cnt_a = contours[0]
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contours, hierarchy = cv2.findContours(thresh_b, 2, 1)
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cnt_b = contours[0]
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contours, hierarchy = cv2.findContours(thresh_c, 2, 1)
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cnt_c = contours[0]
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# ret = cv2.matchShapes(cnt1, cnt2, 1, 0.0)
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# print(ret)
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ret = cv2.matchShapes(cnt_a, cnt_b, 1, 0.0)
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print(ret)
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ret = cv2.matchShapes(cnt_a, cnt_c, 1, 0.0)
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print(ret)
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ret = cv2.matchShapes(cnt_b, cnt_c, 1, 0.0)
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print(ret)
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#Hu 矩是归一化中心矩的线性组合

‎ch21-轮廓Contours/minAreaRect-旋转矩形.py‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -14,7 +14,7 @@
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img = cv2.imread('../data/lightning.png')
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imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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image, contours, hierarchy = cv2.findContours(imgray, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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contours, hierarchy = cv2.findContours(imgray, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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contours = [cnt for cnt in contours if cv2.contourArea(cnt) > 100]
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print('len(contours)', len(contours))
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cnt = contours[0]

‎ch21-轮廓Contours/star_a.jpg‎

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‎ch21-轮廓Contours/star_b.jpg‎

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‎ch21-轮廓Contours/star_c.jpg‎

2.34 KB
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‎ch21-轮廓Contours/凸包-凸性检测-边界矩形-最小外接圆-拟合.py‎

Lines changed: 4 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -12,19 +12,19 @@
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img=cv2.imread('../data/lightning.png',0)
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image, contours, hierarchy = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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contours, hierarchy = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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cnt=contours[0]
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'''
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函数 cv2.convexHull() 可以用来检测一个曲线是否具有凸性缺 并能纠 正缺 。一般来 凸性曲线总是凸出来的 至少是平的。如果有地方凹 去 了就 叫做凸性缺
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函数 cv2.convexHull() 可以用来检测一个曲线是否具有凸性缺 并能纠 正缺 。一般来 凸性曲线总是凸出来的 至少是平的。如果有地方凹 去 了就 叫做凸性缺
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例如下图中的手。红色曲线显示了手的凸包 凸性缺 双箭头标出来了。
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'''
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# convexHull(points, hull=None, clockwise=None, returnPoints=None)
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hull = cv2.convexHull(points, hull, clockwise, returnPoints)
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# hull = cv2.convexHull(points, hull, clockwise, returnPoints)
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'''
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points 我们 传入的 廓
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• hull 输出 通常不需要
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• hull 输出 通常不需要
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• clockwise 方向标志。如果 置为 True 出的凸包是顺时针 方向的。 否则为逆时针 方向。
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• returnPoints 值为 True。它会 回凸包上点的坐标。如果 置 为 False 就会 回与凸包点对应的 廓上的点。
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'''

‎ch22-直方图/22.1.2-绘制直方图-matplotlib.py‎

Lines changed: 7 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -9,6 +9,13 @@
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from matplotlib import pyplot as plt
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img = cv2.imread('../data/home.jpg', 0)
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plt.subplots(1, 2)
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plt.subplot(121)
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plt.imshow(img)
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plt.subplot(122)
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# img.ravel() 将图像转成一维数组 没有中括号
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plt.hist(img.ravel(), 256, [0, 256])
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plt.show()
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‎ch22-直方图/22.3-2D直方图.py‎

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Original file line numberDiff line numberDiff line change
@@ -37,4 +37,3 @@
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hist, xbins, ybins = np.histogram2d(h.ravel(), s.ravel(), [180, 256], [[0, 180], [0, 256]])
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pass

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