diff --git a/图像识别CV/pythoncv_learn/detection_chessboard_v2.py b/图像识别CV/pythoncv_learn/detection_chessboard_v2.py
new file mode 100644
index 0000000..20a0910
--- /dev/null
+++ b/图像识别CV/pythoncv_learn/detection_chessboard_v2.py
@@ -0,0 +1,387 @@
+import cv2
+import numpy as np
+import math
+
+img = cv2.imread('10069.jpg')
+
+img_shape = img.shape
+length = img_shape[0]
+height = img_shape[1]
+
+img = cv2.resize(img, (800, int(800 * length / height)))
+
+hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
+
+lower_skin = np.array([14,43,46])
+upper_skin = np.array([30,255,255])
+
+mask = cv2.inRange(hsv,lower_skin,upper_skin)
+image = cv2.bitwise_and(img,img,mask=mask)
+
+grayImage = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+cv2.imshow('grayImage', grayImage)
+
+edgeImage = grayImage
+
+# 直方图均衡
+edgeImage = cv2.equalizeHist(edgeImage)
+cv2.imshow('equalizeHist', edgeImage)
+
+'''
+# 自适应直方图均衡
+clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
+edgeImage = clahe.apply(edgeImage)
+cv2.imshow('createCLAHE', edgeImage)
+'''
+
+# 二值化
+h, w = edgeImage.shape[:2]
+m = np.reshape(edgeImage, [1, w*h])
+mean = m.sum()/(w*h)
+# binary = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,25,10)
+ret, binary = cv2.threshold(edgeImage, mean, 255, cv2.THRESH_BINARY)
+image_binary = 255 - binary
+cv2.imshow('image_binary', image_binary)
+
+'''
+# 双边滤波
+edgeImage = cv2.bilateralFilter(edgeImage,9,75,75)
+cv2.imshow('bilateralFilter', edgeImage)
+'''
+
+'''
+# 中位模糊
+edgeImage = cv2.medianBlur(edgeImage,5)
+cv2.imshow('medianBlur', edgeImage)
+'''
+
+# 高斯模糊
+edgeImage = cv2.GaussianBlur(grayImage, (3, 3), 1)
+cv2.imshow('GaussianBlur', edgeImage)
+
+'''
+# 平均
+edgeImage = cv2.blur(edgeImage,(5,5))
+cv2.imshow('blur', edgeImage)
+'''
+
+# 边缘检测
+canny = cv2.Canny(edgeImage, 20, 80)
+cv2.imshow('Canny', canny)
+
+kernel = np.ones((5,5),np.uint8)
+dilate = cv2.dilate(canny,kernel,iterations = 1) 
+cv2.imshow('dilate', dilate)
+
+'''
+# 轮廓检测
+ret, thresh = cv2.threshold(edgeImage, 127, 255, 0)
+contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
+
+def cnt_area(cnt):
+  area = cv2.contourArea(cnt)
+  return area
+contours.sort(key = cnt_area, reverse=True)
+print(len(contours))
+for i in range(0, 1):
+    cnt = contours[i]
+    img = image.copy()
+    cv2.drawContours(img, [cnt], 0, (0,255,0), 3)
+    cv2.imshow('drawContours_' + str(i), img)
+cv2.drawContours(image, contours, -1, (0,255,0), 1)
+cv2.imshow('drawContours', image)
+'''
+
+laplacian = cv2.Laplacian(edgeImage,cv2.CV_8U)
+# laplacian = cv2.Laplacian(edgeImage,cv2.CV_64F)
+cv2.imshow('laplacian', laplacian)
+
+# 二值化
+h, w = laplacian.shape[:2]
+m = np.reshape(laplacian, [1, w*h])
+mean = m.sum()/(w*h)
+# binary = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,25,10)
+ret, laplacian_binary = cv2.threshold(laplacian, mean, 255, cv2.THRESH_BINARY)
+cv2.imshow('laplacian_binary', laplacian_binary)
+
+sobelx = edgeImage
+for i in range(0, 6): # 3,6,8有奇效(3: HoughLinesP检测竖线, 6: HoughLines检测竖线, 8:检测横线)
+    sobelx = cv2.Sobel(sobelx,cv2.CV_8U,1,0,ksize=5)
+    # sobelx = cv2.Sobel(edgeImage,cv2.CV_64F,1,0,ksize=5)
+cv2.imshow('sobelx', sobelx)
+
+sobely = edgeImage
+for i in range(0, 6): # 3,6,8有奇效(3: HoughLinesP检测横线, 6: HoughLines检测横线, 8:检测竖线)
+    sobely = cv2.Sobel(sobely,cv2.CV_8U,0,1,ksize=5)
+    # sobely = cv2.Sobel(edgeImage,cv2.CV_64F,0,1,ksize=5)
+cv2.imshow('sobely', sobely)
+
+binary_1 = cv2.bitwise_and(dilate, laplacian_binary)
+cv2.imshow('binary_1', binary_1)
+
+sobelx_binary = cv2.bitwise_and(sobelx, binary_1)
+cv2.imshow('sobelx_binary', sobelx_binary)
+
+sobely_binary = cv2.bitwise_and(sobely, binary_1)
+cv2.imshow('sobely_binary', sobely_binary)
+
+# lineXImage = sobelx_binary
+# lineYImage = sobely_binary
+lineXImage = binary_1
+lineYImage = binary_1
+
+'''
+# 侵蚀
+kernel = np.ones((5,5),np.uint8)
+edgeImage = cv2.erode(edgeImage,kernel,iterations = 1)
+cv2.imshow('erode', edgeImage)
+'''
+
+'''
+# 扩张
+kernel = np.ones((5,5),np.uint8)
+edgeImage = cv2.dilate(edgeImage,kernel,iterations = 1) 
+cv2.imshow('dilate', edgeImage)
+'''
+
+'''
+# 开运算
+kernel = np.ones((5,5),np.uint8)
+edgeImage = cv2.morphologyEx(edgeImage, cv2.MORPH_OPEN, kernel)
+cv2.imshow('morphologyEx', edgeImage)
+'''
+
+'''
+# 闭运算
+kernel = np.ones((5,5),np.uint8)
+morphologyEx = cv2.morphologyEx(edgeImage, cv2.MORPH_CLOSE, kernel)
+cv2.imshow('morphologyEx', morphologyEx)
+'''
+
+'''
+# 形态学梯度
+kernel = np.ones((5,5),np.uint8)
+edgeImage = cv2.morphologyEx(edgeImage, cv2.MORPH_GRADIENT, kernel)
+cv2.imshow('morphologyEx', edgeImage)
+'''
+
+'''
+# 顶帽
+kernel = np.ones((5,5),np.uint8)
+edgeImage = cv2.morphologyEx(edgeImage, cv2.MORPH_TOPHAT, kernel)
+cv2.imshow('morphologyEx', edgeImage)
+'''
+
+'''
+# 黑帽
+kernel = np.ones((5,5),np.uint8)
+edgeImage = cv2.morphologyEx(edgeImage, cv2.MORPH_BLACKHAT, kernel)
+cv2.imshow('morphologyEx', edgeImage)
+'''
+
+'''
+# 锐化
+cv2.imshow('edgeImage + edgeImage', edgeImage + edgeImage)
+'''
+
+def isVerticalLine(line):
+    x1,y1,x2,y2 = line[0]
+
+    if (x2-x1) == 0 :
+        x_angle = 90
+    else:
+        xielv = (y2-y1)/(x2-x1)
+        x_angle = xielv * (180 / math.pi)
+    x_angle = x_angle % 180
+
+    if x_angle > 67.5 and x_angle < 112.5 :
+        return True
+    else:
+        return False
+
+def isHorizontalLine(line):
+    x1,y1,x2,y2 = line[0]
+
+    if (x2-x1) == 0 :
+        x_angle = 90
+    else:
+        xielv = (y2-y1)/(x2-x1)
+        x_angle = xielv * (180 / math.pi)
+    x_angle = x_angle % 180
+
+    if x_angle < 22.5 or x_angle > 157.5 :
+        return True
+    else:
+        return False
+
+def findHoughEdge(edgeImage, isLinesP=True):
+    if isLinesP:
+        lines = findLinesP(edgeImage)
+    else:
+        lines = findLines(edgeImage)
+
+    if lines is None:
+        return [[0, 1, 5, 21], [8, 1, 5, 3], [4, 12, 0, 8], [41, 11, 20, 15]]
+
+    lines_vertical = filter(lambda line: True if abs(line[0][1] - line[0][3]) > edgeImage.shape[0] * 0.5 else False, lines)
+    lines_horizontal  = filter(lambda line: True if abs(line[0][0] - line[0][2]) > edgeImage.shape[1] * 0.5 else False, lines)
+
+    # lines_vertical = filter(isVerticalLine, lines_vertical)
+    # lines_horizontal = filter(isHorizontalLine, lines_horizontal)
+
+    lines_vertical = sorted(lines_vertical, key=lambda line: line[0][0])
+    lines_horizontal = sorted(lines_horizontal , key=lambda line: line[0][1])
+
+    if len(lines_vertical) < 2 or len(lines_horizontal) < 2:
+        return [[0, 1, 5, 21], [8, 1, 5, 3], [4, 12, 0, 8], [41, 11, 20, 15]]
+    return [lines_vertical[0][0], lines_horizontal [0][0], lines_vertical[-1][0], lines_horizontal [-1][0]]
+
+def writeHoughEdge(edgeImage, title):
+    lines = findHoughEdge(edgeImage, False)
+    houghEdgeImage = image.copy()
+    for line in lines:
+        x1,y1,x2,y2 = line
+        cv2.line(houghEdgeImage,(x1,y1),(x2,y2),(0,0,255),2)
+
+    cv2.imshow('houghEdgeImage' + title, houghEdgeImage)
+
+def writeHoughEdgeP(edgeImage, title):
+    lines = findHoughEdge(edgeImage, True)
+    writeHoughEdgeP = image.copy()
+    for line in lines:
+        x1,y1,x2,y2 = line
+        cv2.line(writeHoughEdgeP,(x1,y1),(x2,y2),(255,0,0),2)
+
+    cv2.imshow('writeHoughEdgeP' + title, writeHoughEdgeP)
+
+def findLines(edgeImage):
+    lines = cv2.HoughLines(edgeImage,1,np.pi/180,200)
+    ok_lines = []
+    for line in lines:
+        rho,theta = line[0]
+
+        if (theta < (np.pi/4. )) or (theta > (3.*np.pi/4.0)): #垂直直线
+            pt1 = (int(rho/np.cos(theta)),0) #该直线与第一行的交点
+            #该直线与最后一行的焦点
+            pt2 = (int((rho-edgeImage.shape[0]*np.sin(theta))/np.cos(theta)),edgeImage.shape[0])
+        else: #水平直线
+            pt1 = (0,int(rho/np.sin(theta))) # 该直线与第一列的交点
+            #该直线与最后一列的交点
+            pt2 = (edgeImage.shape[1], int((rho-edgeImage.shape[1]*np.cos(theta))/np.sin(theta)))
+
+        x1, y1 = pt1
+        x2, y2 = pt2
+
+        if (x2-x1) == 0 :
+            x_angle = 90
+        else:
+            xielv = (y2-y1)/(x2-x1)
+            x_angle = xielv * (180 / math.pi)
+        x_angle = x_angle % 180
+
+        if (x_angle < 22.5 or x_angle > 157.5) or (x_angle > 67.5 and x_angle < 112.5) :
+            ok_lines.append([[x1,y1,x2,y2]])
+    return ok_lines
+
+def findLinesP(edgeImage):
+    thresh_min = min(edgeImage.shape)
+    lines = cv2.HoughLinesP(edgeImage, 1.2, np.pi / 180, 160, minLineLength=int(edgeImage.shape[0] * 0.7),maxLineGap=int(thresh_min * 0.5))
+    ok_lines = []
+    for line in lines:
+        x1,y1,x2,y2 = line[0]
+
+        if (x2-x1) == 0 :
+            x_angle = 90
+        else:
+            xielv = (y2-y1)/(x2-x1)
+            x_angle = xielv * (180 / math.pi)
+        x_angle = x_angle % 180
+
+        if (x_angle < 22.5 or x_angle > 157.5) or (x_angle > 67.5 and x_angle < 112.5) :
+            ok_lines.append([[x1,y1,x2,y2]])
+    return ok_lines
+
+def writeLines(edgeImage, title):
+    lines = findLines(edgeImage)
+    houghEdgeImage = image.copy()
+    for line in lines:
+        x1,y1,x2,y2 = line[0]
+        cv2.line(houghEdgeImage,(x1,y1),(x2,y2),(0,0,255),2)
+    cv2.imshow('findLines' + title, houghEdgeImage)
+
+def writeLinesP(edgeImage, title):
+    lines = findLinesP(edgeImage)
+    houghEdgeImage = image.copy()
+    for line in lines:
+        x1,y1,x2,y2 = line[0]
+        cv2.line(houghEdgeImage,(x1,y1),(x2,y2),(255,0,0),2)
+    cv2.imshow('findLinesP' + title, houghEdgeImage)
+
+def __clac_intersection(line_a, line_b):
+    x1_a, y1_a, x2_a, y2_a = line_a
+    x1_b, y1_b, x2_b, y2_b = line_b
+    A_a = y2_a - y1_a
+    B_a = x1_a - x2_a
+    C_a = x2_a * y1_a - x1_a * y2_a
+    A_b = y2_b - y1_b
+    B_b = x1_b - x2_b
+    C_b = x2_b * y1_b - x1_b * y2_b
+    m = A_a * B_b - A_b * B_a
+    output_x = (C_b * B_a - C_a * B_b) / m
+    output_y = (C_a * A_b - C_b * A_a) / m
+    return (int(output_x), int(output_y))
+
+writeLines(lineXImage, 'X')
+writeLines(lineYImage, 'Y')
+writeLinesP(lineXImage, 'X')
+writeLinesP(lineYImage, 'Y')
+
+# writeHoughEdge(lineXImage, 'X')
+# writeHoughEdge(lineYImage, 'Y')
+# writeHoughEdgeP(lineXImage, 'X')
+# writeHoughEdgeP(lineYImage, 'Y')
+
+linesX = findHoughEdge(lineXImage, False)
+linesY = findHoughEdge(lineYImage, False)
+
+houghEdgeImage = image.copy()
+cv2.line(houghEdgeImage,(linesX[0][0],linesX[0][1]),(linesX[0][2],linesX[0][3]),(255,0,0),2)
+cv2.line(houghEdgeImage,(linesX[2][0],linesX[2][1]),(linesX[2][2],linesX[2][3]),(0,255,0),2)
+cv2.line(houghEdgeImage,(linesY[1][0],linesY[1][1]),(linesY[1][2],linesY[1][3]),(0,0,255),2)
+cv2.line(houghEdgeImage,(linesY[3][0],linesY[3][1]),(linesY[3][2],linesY[3][3]),(255,255,0),2)
+cv2.imshow('houghEdge', houghEdgeImage)
+
+p1 = __clac_intersection((linesX[0][0],linesX[0][1], linesX[0][2],linesX[0][3]), (linesY[1][0],linesY[1][1], linesY[1][2],linesY[1][3]))
+p2 = __clac_intersection((linesY[1][0],linesY[1][1], linesY[1][2],linesY[1][3]), (linesX[2][0],linesX[2][1], linesX[2][2],linesX[2][3]))
+p3 = __clac_intersection((linesX[2][0],linesX[2][1], linesX[2][2],linesX[2][3]), (linesY[3][0],linesY[3][1], linesY[3][2],linesY[3][3]))
+p4 = __clac_intersection((linesY[3][0],linesY[3][1], linesY[3][2],linesY[3][3]), (linesX[0][0],linesX[0][1], linesX[0][2],linesX[0][3]))
+
+print((p1, p2, p3, p4))
+
+warpPerspectiveImage = image.copy()
+pts1 = np.float32([p1,p2,p4,p3])
+pts2 = np.float32([[0,0],[800,0],[0,800],[800,800]])
+M = cv2.getPerspectiveTransform(pts1,pts2)
+dst = cv2.warpPerspective(warpPerspectiveImage,M,(800,800))
+cv2.imshow('warpPerspective', dst)
+
+'''
+circles = cv2.HoughCircles(edgeImage,cv2.HOUGH_GRADIENT,1,20,param1=50,param2=30,minRadius=0,maxRadius=0)
+circles = np.uint16(np.around(circles))
+for i in circles[0,:]:
+    # 绘制外圆
+    cv2.circle(image,(i[0],i[1]),i[2],(0,255,0),2)
+    # 绘制圆心
+    cv2.circle(image,(i[0],i[1]),2,(0,0,255),3)
+'''
+
+'''
+pts1 = np.float32([[56,65],[368,52],[28,387],[389,390]])
+pts2 = np.float32([[0,0],[300,0],[0,300],[300,300]])
+M = cv2.getPerspectiveTransform(pts1, pts2)
+image = cv2.warpPerspective(image, M, (600, 600))
+cv2.imshow('show', image)
+'''
+
+cv2.waitKey(0)
+cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/图像识别CV/pythoncv_learn/detection_chessboard_v3.py b/图像识别CV/pythoncv_learn/detection_chessboard_v3.py
new file mode 100644
index 0000000..ad6ac8f
--- /dev/null
+++ b/图像识别CV/pythoncv_learn/detection_chessboard_v3.py
@@ -0,0 +1,193 @@
+import cv2
+import numpy as np
+import math
+from matplotlib import pyplot as plt
+
+img = cv2.imread('10069.jpg')
+
+img_shape = img.shape
+length = img_shape[0]
+height = img_shape[1]
+
+img = cv2.resize(img, (800, int(800 * length / height)))
+
+hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
+
+lower_skin = np.array([14,43,46])
+upper_skin = np.array([30,255,255])
+
+mask = cv2.inRange(hsv,lower_skin,upper_skin)
+img = cv2.bitwise_and(img,img,mask=mask)
+
+img_shape = img.shape
+img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+dst = img_gray.copy()
+
+# 获取图像的长宽
+length = img_shape[0]
+height = img_shape[1]
+midlength = length / 2
+midheight = height / 2
+quarterL = length / 3
+quarterT = height / 3
+sigmaX = 2*(quarterL**2)
+sigmaY = 2*(quarterT**2)
+
+# 设置kernel
+kernel = np.ones((5, 5), np.int16)
+kernel[2][2] = -24
+
+# 设置卷积核
+kernel2 = np.ones((5, 5), np.int16)
+kernel3 = np.ones((3, 3), np.int16)
+dst = cv2.morphologyEx(img_gray, cv2.MORPH_OPEN, kernel2)
+
+# 平滑
+# 高斯
+dst = cv2.blur(dst, (5, 5))
+# dst = cv2.GaussianBlur(dst, (5, 5), 0)
+
+# 锐化
+blur = cv2.Laplacian(dst, cv2.CV_16S, ksize=3)
+dst = cv2.convertScaleAbs(blur)
+
+# 线性滤波,低通滤波
+dst = cv2.filter2D(img_gray, -1, kernel)
+
+# 平滑（双边滤波）
+# 双边滤波
+dst = cv2.bilateralFilter(dst, 9, 100, 100)
+
+# 在二值化前进行膨胀，以增强线段
+# 膨胀导致线段筛选困难
+# dst = cv2.dilate(dst, kernel3)
+
+# 二值化
+ret, dst = cv2.threshold(dst, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
+
+# Hough变换前的高斯赋值
+# 从图像中央到边缘，逐渐降低权重
+for i in range(length):
+    for j in range(height):
+        dst[i][j] = dst[i][j] * math.exp((i-midlength)**2/sigmaX +
+                                         (j-midheight)**2/sigmaY)
+
+# Hough变换
+lines = cv2.HoughLines(dst, 1, np.pi/180, 250)
+
+houghLinesImage = img.copy()
+print(len(lines))
+for line in lines:
+    rho,theta = line[0]
+    a = np.cos(theta)
+    b = np.sin(theta)
+    x0 = a*rho
+    y0 = b*rho
+    x1 = int(x0 + 1000*(-b))
+    y1 = int(y0 + 1000*(a))
+    x2 = int(x0 - 1000*(-b))
+    y2 = int(y0 - 1000*(a))
+    if (x2-x1) == 0 :
+        xielv = (y2-y1)/0.00000001
+    else:
+        xielv = (y2-y1)/(x2-x1)
+    # if (xielv > -0.08 and xielv < 0.08)  or xielv > 8.0 or xielv < -8.0 :
+        # print(xielv)
+    cv2.line(houghLinesImage,(x1,y1),(x2,y2),(0,0,255),2)
+cv2.imshow('houghLinesImage', houghLinesImage)
+
+
+
+# 利用方差进行平行线的判断
+# 近邻生长法
+# Hough变换
+# 首先找到水平的范围，上边界为rho最大，theta最小，下边界为rho最小，theta最大（<90)
+ThetaMIN2 = 180
+ThetaMIN = 180
+ThetaMAX = -1
+ThetaMAX2 = -1
+RhoMAX = -1
+RhoMAX2 = -1
+RhoMIN = 1000
+RhoMIN2 = 1000
+up = 0
+bot = 0
+up2 = 0
+bot2 = 0
+
+for i in range(int(lines[0].size/2)):
+    # 遍历全部点集
+    r, t = lines[0][i]
+    print(lines[0][i])
+    t = t / np.pi*180
+    if t < 90:
+        # theta < 90，属于水平范围
+        if t > ThetaMAX:
+            ThetaMAX = t
+            up = i
+        if r > RhoMAX:
+            RhoMAX = r
+            bot = i
+    else:
+        if t > ThetaMAX2:
+            ThetaMAX2 = t
+            up2 = i
+        if t < ThetaMIN2:
+            ThetaMIN2 = t
+            bot2 = i
+
+result = [up, bot, up2, bot2]
+print(result)
+# Hough变换
+xtheta = []
+yrho = []
+for rho, theta in lines[0]:
+    a = np.cos(theta)
+    b = np.sin(theta)
+    x0 = a*rho
+    y0 = b*rho
+    x1 = int(x0 + 1000*(-b))
+    y1 = int(y0 + 1000*(a))
+    x2 = int(x0 - 1000*(-b))
+    y2 = int(y0 - 1000*(a))
+    # if theta < 0.5:
+    xtheta.append(theta / np.pi * 180)
+    yrho.append(rho)
+    cv2.line(img, (x1, y1), (x2, y2), (255, 255, 0), 2)
+
+print(result)
+for i in result:
+    rho = lines[0][i][0]
+    theta = lines[0][i][1]
+    a = np.cos(theta)
+    b = np.sin(theta)
+    x0 = a*rho
+    y0 = b*rho
+    x1 = int(x0 + 1000*(-b))
+    y1 = int(y0 + 1000*(a))
+    x2 = int(x0 - 1000*(-b))
+    y2 = int(y0 - 1000*(a))
+    cv2.line(img, (x1, y1), (x2, y2), (255, 0, 0), 10)
+
+# 计算最有可能的两条best
+
+# 显示
+# plt.subplot(1, 2, 1), plt.imshow(dst, 'gray')
+
+plt.subplot(1, 3, 1), plt.imshow(dst, 'gray')
+plt.title('test')
+plt.xticks([]), plt.yticks([])
+
+plt.subplot(1, 3, 2), plt.imshow(img_gray, 'gray')
+plt.title('ori')
+plt.xticks([]), plt.yticks([])
+
+# plt.subplot(1, 4, 3), plt.scatter(xtheta, yrho, c='b', marker='o')
+# plt.title('Hough')
+# plt.xlabel('X'), plt.ylabel('Y')
+
+plt.subplot(1, 3, 3), plt.imshow(img)
+plt.title('Lines')
+plt.xticks([]), plt.yticks([])
+
+plt.show()
diff --git a/图像识别CV/pythoncv_learn/detection_chessboard_v4.py b/图像识别CV/pythoncv_learn/detection_chessboard_v4.py
new file mode 100644
index 0000000..53db7cb
--- /dev/null
+++ b/图像识别CV/pythoncv_learn/detection_chessboard_v4.py
@@ -0,0 +1,214 @@
+import cv2
+import numpy as np
+import math
+from matplotlib import pyplot as plt
+
+img = cv2.imread('10076.jpg')
+
+img_shape = img.shape
+length = img_shape[0]
+height = img_shape[1]
+
+if length < height:
+    img = cv2.resize(img, (800, int(800 * length / height)))
+else:
+    img = cv2.resize(img, (int(800 * height / length), 800))
+
+# 边缘保护滤波
+img = cv2.edgePreservingFilter(img)
+cv2.imshow('edgePreservingFilter', img)
+
+hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
+
+lower_skin = np.array([11,43,46])
+upper_skin = np.array([34,255,255])
+
+mask = cv2.inRange(hsv,lower_skin,upper_skin)
+img = cv2.bitwise_and(img,img,mask=mask)
+
+img_shape = img.shape
+img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+dst = img_gray.copy()
+
+# 获取图像的长宽
+length = img_shape[0]
+height = img_shape[1]
+midlength = length / 2
+midheight = height / 2
+quarterL = length / 3
+quarterT = height / 3
+sigmaX = 2*(quarterL**2)
+sigmaY = 2*(quarterT**2)
+
+# 设置kernel
+kernel = np.ones((5, 5), np.int16)
+kernel[2][2] = -24
+
+# 设置卷积核
+kernel2 = np.ones((5, 5), np.int16)
+kernel3 = np.ones((3, 3), np.int16)
+dst = cv2.morphologyEx(img_gray, cv2.MORPH_OPEN, kernel2)
+
+# 平滑
+# 高斯
+dst = cv2.blur(dst, (5, 5))
+# dst = cv2.GaussianBlur(dst, (5, 5), 0)
+
+# 锐化
+blur = cv2.Laplacian(dst, cv2.CV_16S, ksize=3)
+dst = cv2.convertScaleAbs(blur)
+
+# 线性滤波,低通滤波
+dst = cv2.filter2D(img_gray, -1, kernel)
+
+# 平滑（双边滤波）
+# 双边滤波
+dst = cv2.bilateralFilter(dst, 9, 100, 100)
+
+# 在二值化前进行膨胀，以增强线段
+# 膨胀导致线段筛选困难
+# dst = cv2.dilate(dst, kernel3)
+
+# 二值化
+ret, dst = cv2.threshold(dst, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
+
+# Hough变换前的高斯赋值
+# 从图像中央到边缘，逐渐降低权重
+'''
+for i in range(length):
+    for j in range(height):
+        dst[i][j] = dst[i][j] * math.exp((i-midlength)**2/sigmaX +
+                                         (j-midheight)**2/sigmaY)
+'''
+
+'''
+# Hough变换
+lines = cv2.HoughLines(dst, 1, np.pi/180, 250)
+
+houghLinesImage = img.copy()
+print(len(lines))
+for line in lines:
+    rho,theta = line[0]
+    a = np.cos(theta)
+    b = np.sin(theta)
+    x0 = a*rho
+    y0 = b*rho
+    x1 = int(x0 + 1000*(-b))
+    y1 = int(y0 + 1000*(a))
+    x2 = int(x0 - 1000*(-b))
+    y2 = int(y0 - 1000*(a))
+    if (x2-x1) == 0 :
+        xielv = (y2-y1)/0.00000001
+    else:
+        xielv = (y2-y1)/(x2-x1)
+    # if (xielv > -0.08 and xielv < 0.08)  or xielv > 8.0 or xielv < -8.0 :
+        # print(xielv)
+    cv2.line(houghLinesImage,(x1,y1),(x2,y2),(0,0,255),2)
+cv2.imshow('houghLinesImage', houghLinesImage)
+'''
+
+# Hough变换
+thresh_min = min(dst.shape)
+lines = cv2.HoughLinesP(dst, 1, np.pi / 180, 200, minLineLength=int(dst.shape[0] * 0.47), maxLineGap=int(thresh_min * 0.5))
+
+houghLinesImage = img.copy()
+for line in lines:
+    x1,y1,x2,y2 = line[0]
+    cv2.line(houghLinesImage,(x1,y1),(x2,y2),(0,0,255),2)
+cv2.imshow('houghLinesImage', houghLinesImage)
+
+# 利用方差进行平行线的判断
+# 近邻生长法
+# Hough变换
+# 首先找到水平的范围，上边界为rho最大，theta最小，下边界为rho最小，theta最大（<90)
+ThetaMIN2 = 180
+ThetaMIN = 180
+ThetaMAX = -1
+ThetaMAX2 = -1
+RhoMAX = -1
+RhoMAX2 = -1
+RhoMIN = 1000
+RhoMIN2 = 1000
+up = 0
+bot = 0
+up2 = 0
+bot2 = 0
+
+'''
+for i in range(int(lines[0].size/2)):
+    # 遍历全部点集
+    r, t = lines[0][i]
+    print(lines[0][i])
+    t = t / np.pi*180
+    if t < 90:
+        # theta < 90，属于水平范围
+        if t > ThetaMAX:
+            ThetaMAX = t
+            up = i
+        if r > RhoMAX:
+            RhoMAX = r
+            bot = i
+    else:
+        if t > ThetaMAX2:
+            ThetaMAX2 = t
+            up2 = i
+        if t < ThetaMIN2:
+            ThetaMIN2 = t
+            bot2 = i
+
+result = [up, bot, up2, bot2]
+print(result)
+# Hough变换
+xtheta = []
+yrho = []
+for rho, theta in lines[0]:
+    a = np.cos(theta)
+    b = np.sin(theta)
+    x0 = a*rho
+    y0 = b*rho
+    x1 = int(x0 + 1000*(-b))
+    y1 = int(y0 + 1000*(a))
+    x2 = int(x0 - 1000*(-b))
+    y2 = int(y0 - 1000*(a))
+    # if theta < 0.5:
+    xtheta.append(theta / np.pi * 180)
+    yrho.append(rho)
+    cv2.line(img, (x1, y1), (x2, y2), (255, 255, 0), 2)
+
+print(result)
+for i in result:
+    rho = lines[0][i][0]
+    theta = lines[0][i][1]
+    a = np.cos(theta)
+    b = np.sin(theta)
+    x0 = a*rho
+    y0 = b*rho
+    x1 = int(x0 + 1000*(-b))
+    y1 = int(y0 + 1000*(a))
+    x2 = int(x0 - 1000*(-b))
+    y2 = int(y0 - 1000*(a))
+    cv2.line(img, (x1, y1), (x2, y2), (255, 0, 0), 10)
+'''
+
+# 计算最有可能的两条best
+
+# 显示
+# plt.subplot(1, 2, 1), plt.imshow(dst, 'gray')
+
+plt.subplot(1, 3, 1), plt.imshow(dst, 'gray')
+plt.title('test')
+plt.xticks([]), plt.yticks([])
+
+plt.subplot(1, 3, 2), plt.imshow(img_gray, 'gray')
+plt.title('ori')
+plt.xticks([]), plt.yticks([])
+
+# plt.subplot(1, 4, 3), plt.scatter(xtheta, yrho, c='b', marker='o')
+# plt.title('Hough')
+# plt.xlabel('X'), plt.ylabel('Y')
+
+plt.subplot(1, 3, 3), plt.imshow(img)
+plt.title('Lines')
+plt.xticks([]), plt.yticks([])
+
+plt.show()
diff --git a/图像识别CV/pythoncv_learn/image_cutting_v5.py b/图像识别CV/pythoncv_learn/image_cutting_v5.py
index 4f59a1b..8ec957e 100644
--- a/图像识别CV/pythoncv_learn/image_cutting_v5.py
+++ b/图像识别CV/pythoncv_learn/image_cutting_v5.py
@@ -149,7 +149,7 @@ def findLine(edgeImage, title):
     cv2.imshow('HoughLinesP' + title, houghLinesPImage)
 
 if __name__ == '__main__':
-    image = cv2.imread('10007.jpg', cv2.IMREAD_COLOR)
+    image = cv2.imread('10076.jpg', cv2.IMREAD_COLOR)
     # image = cv2.imread('10008.jpg', cv2.IMREAD_GRAYSCALE)
 
     #如果本身是彩图 似乎也没什么影响！
@@ -164,6 +164,7 @@ if __name__ == '__main__':
     threshold = 1
     labels = k_means(image_s, k, threshold)
 
+    '''
     labels = (labels * (255 / k)).astype(np.uint8)
 
     labels = cv2.resize(labels, (800, 800))
@@ -180,13 +181,38 @@ if __name__ == '__main__':
     cv2.imshow('image_binary', image_binary)
 
     findLine(image_binary, "line")
+    '''
+
+    labels_5 = (labels / 5).astype(np.uint8)
+    labels_4_5 = (labels / 4).astype(np.uint8)
+    labels_4 = labels_4_5 - labels_5
+    labels_3_4_5 = (labels / 3).astype(np.uint8)
+    labels_3 = labels_3_4_5 - labels_4_5
+    labels_2_3_4_5 = (labels / 2).astype(np.uint8)
+    labels_2 = labels_2_3_4_5 - labels_3_4_5 - labels_4_5
+    labels_1 = labels - labels_2 * 2 - labels_3 *3 - labels_4 * 4 - labels_5 * 5
 
-    plt.subplot(1, 2, 1)
+    plt.subplot(1, 7, 1)
     plt.title("Soucre Image")
     plt.imshow(image,cmap="gray")
-    plt.subplot(1, 2, 2)
+    plt.subplot(1, 7, 2)
     plt.title("Segamenting Image with k-means\n" + "k=" + str(k) + "  threshold=" + str(threshold))
     plt.imshow(labels)
+    plt.subplot(1, 7, 3)
+    plt.title("labels_1")
+    plt.imshow(labels_1)
+    plt.subplot(1, 7, 4)
+    plt.title("labels_2")
+    plt.imshow(labels_2)
+    plt.subplot(1, 7, 5)
+    plt.title("labels_3")
+    plt.imshow(labels_3)
+    plt.subplot(1, 7, 6)
+    plt.title("labels_4")
+    plt.imshow(labels_4)
+    plt.subplot(1, 7, 7)
+    plt.title("labels_5")
+    plt.imshow(labels_5)
     plt.show()
 
     cv2.waitKey(0)
diff --git a/图像识别CV/pythoncv_learn/image_cutting_v6.py b/图像识别CV/pythoncv_learn/image_cutting_v6.py
new file mode 100644
index 0000000..640d97e
--- /dev/null
+++ b/图像识别CV/pythoncv_learn/image_cutting_v6.py
@@ -0,0 +1,58 @@
+import cv2
+import numpy as np
+
+def main():
+    img = cv2.imread('10076.jpg')
+
+    img_shape = img.shape
+    length = img_shape[0]
+    height = img_shape[1]
+
+    if length < height:
+        img = cv2.resize(img, (800, int(800 * length / height)))
+    else:
+        img = cv2.resize(img, (int(800 * height / length), 800))
+
+    # img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    skinMask = HSVBin(img)
+    contours = getContours(skinMask)
+    cv2.drawContours(img,contours,-1,(0,255,0),2)
+    cv2.imshow('capture',img)
+    k = cv2.waitKey(0)
+
+def getContours(img):
+    kernel = np.ones((5,5),np.uint8)
+    closed = cv2.morphologyEx(img,cv2.MORPH_OPEN,kernel)
+    closed = cv2.morphologyEx(closed,cv2.MORPH_CLOSE,kernel)
+    contours,h = cv2.findContours(closed,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
+    vaildContours = []
+    for cont in contours:
+        if cv2.contourArea(cont)>9000:
+            #x,y,w,h = cv2.boundingRect(cont)
+            #if h/w >0.75:
+            #filter face failed
+            vaildContours.append(cv2.convexHull(cont))
+            #rect = cv2.minAreaRect(cont)
+            #box = cv2.cv.BoxPoint(rect)
+            #vaildContours.append(np.int0(box))
+    return  vaildContours
+
+def HSVBin(img):
+    hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
+    # cv2.imshow('hsv',hsv)
+
+    '''
+    lower_skin = np.array([11,43,46])
+    upper_skin = np.array([34,255,255])
+    '''
+    lower_skin = np.array([14,43,46])
+    upper_skin = np.array([30,255,255])
+
+    mask = cv2.inRange(hsv,lower_skin,upper_skin)
+    # cv2.imshow('mask',mask)
+    res = cv2.bitwise_and(img,img,mask=mask)
+    cv2.imshow('res',res)
+    return mask
+
+if __name__ =='__main__':
+    main()
diff --git a/图像识别CV/pythoncv_learn/pic1.png b/图像识别CV/pythoncv_learn/pic1.png
new file mode 100644
index 0000000..2463d49
Binary files /dev/null and b/图像识别CV/pythoncv_learn/pic1.png differ
diff --git a/图像识别CV/pythoncv_learn/pic2.jpg b/图像识别CV/pythoncv_learn/pic2.jpg
new file mode 100644
index 0000000..ed16f9a
Binary files /dev/null and b/图像识别CV/pythoncv_learn/pic2.jpg differ
diff --git a/图像识别CV/pythoncv_learn/pic3.jpg b/图像识别CV/pythoncv_learn/pic3.jpg
new file mode 100644
index 0000000..344eb4f
Binary files /dev/null and b/图像识别CV/pythoncv_learn/pic3.jpg differ
diff --git a/图像识别CV/pythoncv_learn/pic4.jpg b/图像识别CV/pythoncv_learn/pic4.jpg
new file mode 100644
index 0000000..a3f81df
Binary files /dev/null and b/图像识别CV/pythoncv_learn/pic4.jpg differ
diff --git a/图像识别CV/pythoncv_learn/pic5.png b/图像识别CV/pythoncv_learn/pic5.png
new file mode 100644
index 0000000..ec5b2c6
Binary files /dev/null and b/图像识别CV/pythoncv_learn/pic5.png differ
diff --git a/图像识别CV/pythoncv_learn/srcImage.jpg b/图像识别CV/pythoncv_learn/srcImage.jpg
new file mode 100644
index 0000000..dd6177d
Binary files /dev/null and b/图像识别CV/pythoncv_learn/srcImage.jpg differ
diff --git a/图像识别CV/pythoncv_learn/test.jpg b/图像识别CV/pythoncv_learn/test.jpg
new file mode 100644
index 0000000..8ebcff5
Binary files /dev/null and b/图像识别CV/pythoncv_learn/test.jpg differ