commit

Author: JimmyHHua

README.md

@@ -1,7 +1,28 @@
 # OpenCV 4.0 Tutorial
+
+## Introduction
+
 This repository contains source code of OpenCV Tutorial application, the environment is python3.0 and opencv4.0
 
-## Learning Road
+## Sample
+- Image load
+```python
+import cv2
+
+src = cv2.imread("test.png")
+cv2.namedWindow("input", cv2.WINDOW_AUTOSIZE)
+cv2.imshow("input", src)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
+```
+- Gray Image
+```python
+gray = cv2.cvtColor(src, cv.COLOR_BGR2GRAY)
+```
+---
+**More opencv4.0 tutorials plese flow the learning road as below** 👇👇👇
+
+## Learning Road ⛳️⛳️⛳️
 - ✔️ : **Basic**
 - ✏️ : **Attention**
 - ❣️ : **Important**
@@ -63,3 +84,8 @@ code_052 | [OpenCV之几何矩计算中心](python/code_052/opencv_052.py)   | 
 code_053 | [OpenCV之使用Hu矩阵实现轮廓匹配](python/code_053/opencv_053.py)   | ✔️
 code_054 | [OpenCV之轮廓圆与椭圆拟合](python/code_054/opencv_054.py)   | ✔️
 code_055 | [OpenCV之凸包检测](python/code_055/opencv_055.py)   | ✏️
+code_056 | [OpenCV之直线拟合与极值点寻找](python/code_056/opencv_056.py)   | ✔️
+code_057 | [OpenCV之点多边形测试](python/code_057/opencv_057.py)   | ✔️
+code_058 | [OpenCV之寻找最大内接圆](python/code_058/opencv_058.py)   | ✔️
+code_059 | [OpenCV之霍夫曼直线检测](python/code_059/opencv_059.py)   | ✔️
+code_060 | [OpenCV之概率霍夫曼直线检测](python/code_060/opencv_060.py)   | ❣️

python/code_056/canny_output.png

@@ -0,0 +1,56 @@
+import cv2 as cv
+import numpy as np
+
+
+def canny_demo(image):
+    t = 80
+    canny_output = cv.Canny(image, t, t * 2)
+    cv.imwrite("canny_output.png", canny_output)
+    return canny_output
+
+
+src = cv.imread("twolines.png")
+cv.namedWindow("input", cv.WINDOW_AUTOSIZE)
+cv.imshow("input", src)
+
+binary = canny_demo(src)
+k = np.ones((3, 3), dtype=np.uint8)
+binary = cv.morphologyEx(binary, cv.MORPH_DILATE, k)
+cv.imshow("binary", binary)
+
+# 轮廓发现
+out, contours, hierarchy = cv.findContours(binary, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
+
+# 直线拟合与极值点寻找
+for c in range(len(contours)):
+    x, y, w, h = cv.boundingRect(contours[c])
+    m = max(w, h)
+    # if m < 30:
+    #     continue
+    vx, vy, x0, y0 = cv.fitLine(contours[c], cv.DIST_L1, 0, 0.01, 0.01)
+    k = vy/vx
+    b = y0 - k*x0
+    maxx = 0
+    maxy = 0
+    miny = 100000
+    minx = 0
+    for pt in contours[c]:
+        px, py = pt[0]
+        if maxy < py:
+            maxy = py
+        if miny > py:
+            miny = py
+    maxx = (maxy - b) / k
+    minx = (miny - b) / k
+    cv.line(src, (np.int32(maxx), np.int32(maxy)),
+            (np.int32(minx), np.int32(miny)), (0, 0, 255), 2, 8, 0)
+
+
+# 显示
+cv.imshow("contours_analysis", src)
+cv.imwrite("contours_analysis.png", src)
+
+cv.waitKey(0)
+cv.destroyAllWindows()
+
+

python/code_056/contours_analysis.png

@@ -0,0 +1,34 @@
+import cv2 as cv
+import numpy as np
+
+src = cv.imread("mask.png")
+cv.namedWindow("input", cv.WINDOW_AUTOSIZE)
+cv.imshow("input", src)
+gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
+ret, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)
+cv.imshow("binary", binary)
+
+# 轮廓发现
+image = np.zeros(src.shape, dtype=np.float32)
+out, contours, hierarchy = cv.findContours(binary, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
+h, w = src.shape[:2]
+for row in range(h):
+    for col in range(w):
+        dist = cv.pointPolygonTest(contours[0], (col, row), True)  # True的话返回点到轮廓的距离，False则返回+1，0，-1三个值，其中+1表示点在轮廓内部，0表示点在轮廓上，-1表示点在轮廓外
+        if dist == 0:
+            image[row, col] = (255, 255, 255)
+        if dist > 0:
+            image[row, col] = (255-dist, 0, 0)
+        if dist < 0:
+            image[row, col] = (0, 0, 255+dist)
+
+dst = cv.convertScaleAbs(image)  # 将像素点进行绝对值计算
+dst = np.uint8(dst)
+
+# 显示
+cv.imshow("contours_analysis", dst)
+cv.imwrite("contours_analysis.png", dst)
+cv.waitKey(0)
+cv.destroyAllWindows()
+
+

python/code_056/opencv_056.py

@@ -0,0 +1,53 @@
+import cv2 as cv
+import numpy as np
+# Create an image
+r = 100
+src = np.zeros((4*r, 4*r), dtype=np.uint8)
+# Create a sequence of points to make a contour
+vert = [None]*6
+vert[0] = (3*r//2, int(1.34*r))
+vert[1] = (1*r, 2*r)
+vert[2] = (3*r//2, int(2.866*r))
+vert[3] = (5*r//2, int(2.866*r))
+vert[4] = (3*r, 2*r)
+vert[5] = (5*r//2, int(1.34*r))
+# Draw it in src
+for i in range(6):
+    #cv.putText(src,str(i),vert[i],cv.FONT_HERSHEY_SIMPLEX, .8,( 255 ), 2)
+    cv.line(src, vert[i],  vert[(i+1)%6], ( 255 ), 3)
+# Get the contours
+_, contours, _ = cv.findContours(src, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
+
+# Calculate the distances to the contour
+raw_dist = np.empty(src.shape, dtype=np.float32)
+for i in range(src.shape[0]):
+    for j in range(src.shape[1]):
+        raw_dist[i,j] = cv.pointPolygonTest(contours[0], (j,i), True)
+
+# 获取最大值即内接圆半径，中心点坐标
+minVal, maxVal, _, maxDistPt = cv.minMaxLoc(raw_dist)
+minVal = abs(minVal)
+maxVal = abs(maxVal)
+
+# Depicting the  distances graphically
+drawing = np.zeros((src.shape[0], src.shape[1], 3), dtype=np.uint8)
+for i in range(src.shape[0]):
+    for j in range(src.shape[1]):
+        if raw_dist[i,j] < 0:
+            drawing[i,j,0] = 255 - abs(raw_dist[i,j]) * 255 / minVal
+        elif raw_dist[i,j] > 0:
+            drawing[i,j,2] = 255 - raw_dist[i,j] * 255 / maxVal
+        else:
+            drawing[i,j,0] = 255
+            drawing[i,j,1] = 255
+            drawing[i,j,2] = 255
+
+src_t = np.stack((src,)*3,axis=2)
+# max inner circle
+cv.circle(drawing,maxDistPt, np.int(maxVal),(255,255,255), 1, cv.LINE_8, 0)
+cv.imshow('Source', src)
+cv.imshow('Distance and inscribed circle', drawing)
+cv.imwrite('drawing.jpg', np.hstack((src_t,drawing)))
+
+cv.waitKey(0)
+cv.destroyAllWindows()

python/code_056/twolines.png

@@ -0,0 +1,39 @@
+import cv2 as cv
+import numpy as np
+
+
+def canny_demo(image):
+    t = 80
+    canny_output = cv.Canny(image, t, t * 2)
+    cv.imwrite("canny_output.png", canny_output)
+    return canny_output
+
+
+src = cv.imread("sudoku.png")
+cv.namedWindow("input", cv.WINDOW_AUTOSIZE)
+cv.imshow("input", src)
+
+binary = canny_demo(src)
+cv.imshow("binary", binary)
+
+lines = cv.HoughLines(binary, 1, np.pi / 180, 150, None, 0, 0)
+if lines is not None:
+    for i in range(0, len(lines)):
+        rho = lines[i][0][0]
+        theta = lines[i][0][1]
+        a = np.cos(theta)
+        b = np.sin(theta)
+        x0 = a * rho
+        y0 = b * rho
+        pt1 = (int(x0 + 1000 * (-b)), int(y0 + 1000 * (a)))
+        pt2 = (int(x0 - 1000 * (-b)), int(y0 - 1000 * (a)))
+        cv.line(src, pt1, pt2, (0, 0, 255), 3, cv.LINE_AA)
+
+
+# 显示
+cv.imshow("hough line demo", src)
+cv.imwrite("contours_analysis.png", src)
+cv.waitKey(0)
+cv.destroyAllWindows()
+
+

python/code_057/contours_analysis.png

@@ -0,0 +1,31 @@
+import cv2 as cv
+import numpy as np
+
+
+def canny_demo(image):
+    t = 80
+    canny_output = cv.Canny(image, t, t * 2)
+    cv.imwrite("canny_output.png", canny_output)
+    return canny_output
+
+
+src = cv.imread("morph01.png")
+cv.namedWindow("input", cv.WINDOW_AUTOSIZE)
+cv.imshow("input", src)
+binary = canny_demo(src)
+cv.imshow("binary", binary)
+
+linesP = cv.HoughLinesP(binary, 1, np.pi / 180, 55, None, 50, 10)
+cv.HoughLinesP
+if linesP is not None:
+    for i in range(0, len(linesP)):
+        l = linesP[i][0]
+        cv.line(src, (l[0], l[1]), (l[2], l[3]), (255, 0, 0), 1, cv.LINE_AA)
+
+# 显示
+cv.imshow("hough line demo", src)
+cv.imwrite("contours_analysis.png", src)
+cv.waitKey(0)
+cv.destroyAllWindows()
+
+