使用 Opencv 检测梯形和菱形时的容差问题

Question

我应该编写一个代码来识别不同的形状，我们得到了一个文件夹，其中给出了不同形状的图像（圆形/三角形/梯形/菱形/正方形/四边形/平行四边形/五边形/六边形）。在识别它们时，它应该以以下格式返回输出：{ 'Shape': ['color', Area, cX, cY] }

我的代码总是混淆梯形和菱形。我对其进行了调试，发现这是因为 sides 和 angle 的公差为 +/-1。我该怎么办？。

以下是我尝试过的 StackOverflow 的一些链接：OpenCV: How to detect rhombus on an image? Detecting trapezium, rhombus, square, quadrilateral, parallelogram by Opencv in Python OpenCV shape detection Tutorial for iPhone OpenCV on shape recognising [closed]

还有更多，但它们存在社区问题，所以我不会在它们上浪费您的时间。长话短说，我没有发现任何有用的东西。

import cv2
import numpy as np

img = cv2.imread('Sample3.png',-1)


class ShapeColorRecognition():
    shape = 'unidentified'
    color = 'undetected'

def __init__(self,img):
    global shape,color,cX, cY,area
    self.shapeList = []
    
    #Getting Contours
    self.img = img
    gray = cv2.cvtColor(self.img,cv2.COLOR_BGR2GRAY)
    blurred = cv2.blur(gray,(5,5))
    ret,thresh = cv2.threshold(blurred,230,255,cv2.THRESH_BINARY)
    contours,_ = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

    for cnt in contours[1:]:
        #Approximating the contours
        approxCont = cv2.approxPolyDP(cnt,0.1*cv2.arcLength(cnt,True),True)
        #Calculating the Centroid coordinates of the particular object by moments
        M = cv2.moments(cnt)
        cX = int(M['m10']/M['m00'])
        cY = int(M['m01']/M['m00'])
        area = cv2.contourArea(cnt)
        #Converting image to the HSV format
        hsv = cv2.cvtColor(self.img,cv2.COLOR_BGR2HSV)
        #Calling the functions for Shapes and Colors
        self.shape(approxCont)
        self.color(hsv,cX, cY)
        #Final List Containing the shape and the color of the object
        self.shapeList.append(tuple([shape,color, area, cX,cY]))

def shape(self,approx):
    """Getting the name of shape of approximated contour"""
    global shape
    p1 = approx[0][0]
    p2 = approx[1][0]
    p3 = approx[-1][0]
    p4 = approx[2][0]
    if len(approx) == 3:
        shape = 'Triangle'
    elif len(approx) == 4:
        (degrees) = self.get_corner_angle(p1, p2, p3)
        (degrees_opp) = self.get_corner_angle_opp(p4, p2, p3)
        dist1 = self.distance(p1, p2, p3, p4)

        # print(degrees)
        if ((89 <= int(degrees) <= 91) and (89 <= int(degrees_opp) <= 91)) and (a == b):
            shape = "Square"
        elif (a == True or b == True)  and (int(degrees) != 90 and int(degrees_opp) != 90):
            shape = "Trapezoid"
            print(int(degrees_opp))
            print(int(degrees))
            print(l1)
            print(l2)
            print(l3)
            print(l4)
        elif (int(degrees) == int(degrees_opp)) and(a == b):
            shape = "Rhombus"
        elif (a == True or b == True) and (int(degrees) == int(degrees_opp)):
            shape  = "Parallelogram"
        elif (int(degrees) != int(degrees_opp)) and (a == False and b == False):
            shape = "Quadilateral"
            print(int(degrees))
            print(l1)
            print(l2)
            print(l3)
            print(l4)

    elif len(approx) == 5:
        shape = 'Pentagon'
    elif len(approx) == 6:
        shape = 'Hexagon'
    else:
        shape = 'Circle'

def unit_vector(self, v):
    return v / np.linalg.norm(v)

def distance(self, p1, p2, p3, p4):
    global l1, l2, l3, l4, a ,b 
    l1 = int(((p4[0] - p3[0])**2 + (p4[1] - p3[1])**2)**0.5)
    l2 = int(((p2[0] - p1[0])**2 + (p2[1] - p1[1])**2)**0.5)
    l3 = int(((p3[0] - p1[0])**2 + (p3[1] - p1[1])**2)**0.5)
    l4 = int(((p4[0] - p2[0])**2 + (p4[1] - p2[1])**2)**0.5)
    a = l1 == l2
    b = l3 == l4
    return l1, l2, l3, l4, a, b

def get_corner_angle(self, p1, p2, p3):
    v1 = np.array([p1[0] - p2[0], p1[1] - p2[1]])
    v2 = np.array([p1[0] - p3[0], p1[1] - p3[1]])
    v1_unit = self.unit_vector(v1)
    v2_unit = self.unit_vector(v2)
    radians = np.arccos(np.clip(np.dot(v1_unit, v2_unit), -1, 1))
    return np.degrees(radians)

def get_corner_angle_opp(self, p4, p2, p3):
    v3 = np.array([p4[0] - p2[0], p4[1] - p2[1]])
    v4 = np.array([p4[0] - p3[0], p4[1] - p3[1]])
    v3_unit = self.unit_vector(v3)
    v4_unit = self.unit_vector(v4)
    radians = np.arccos(np.clip(np.dot(v3_unit, v4_unit), -1, 1))
    return np.degrees(radians)

def color(self,hsv_img,cX,cY):
    """Gives the name of the color of the shape"""
    global color

    #Getting Hue,Saturation,value of the centroid of the shape from HSV image
    h,s,v = hsv_img[cY,cX]

    #Getting final name of the color according their ranges in the HSV color space
    h,s,v = hsv_img[cY, cX]
    if h in range(0,11) or h in range(170,180):
        color = 'Red'
    elif h in range(51,76):
        color = 'Green'
    elif h in range(106,131):
        color = 'Blue'
    return color

#Creating the Object of class
shapeColorObj = ShapeColorRecognition(img)

#Final output

for ans in shapeColorObj.shapeList:
    value = []
    key = (ans[0])
    value.append(ans[1])
    value.append(ans[2])
    value.append(ans[3])
    value.append(ans[4])


# shape.update({key: value})
print(value)
print(key)
# print(shape)

您还可以看到我的形状名称存储在一个变量中，每当我尝试用作键时，它都会出错

AttributeError: 'str' object has no attribute 'update'

或

TypeError: 'str' object does not support item assignment

这是我的输出：

 (base) C:\Users\Windows 10\OneDrive\Desktop\Python\Eynatra assignment>image.py
['Green', 36612.5, 191, 361]
Trapezoid
['Red', 22709.0, 831, 392]
Triangle
['Blue', 50968.0, 524, 361]
Square

非常感谢提前。

This is one of many images (an example)

Answer 1

正如我所提到的，我得到了 +/-1 的容差，所以我使用该容差来创建一个范围并设置 if-elif-else 条件。我想就是这样。如果您认为这还不够，请更正它

附言我的声望点已经少了，所以请不要投票。我总是乐于接受建设性的建议。

import cv2
import numpy as np

img = cv2.imread('Sample4.png',-1)

#Class for recognition of the Shapes and Colors the shapes in the image given
class ShapeColorRecognition():
    shape = 'unidentified'
    color = 'undetected'
    
    def __init__(self,img):
        global shape,color,cX, cY,area
        self.shapeList = []
        
        #Getting Contours
        self.img = img
        gray = cv2.cvtColor(self.img,cv2.COLOR_BGR2GRAY)
        blurred = cv2.blur(gray,(5,5))
        ret,thresh = cv2.threshold(blurred,230,255,cv2.THRESH_BINARY)
        contours,_ = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
 
        for cnt in contours[1:]:
            #Approximating the contours
            approxCont = cv2.approxPolyDP(cnt,0.01*cv2.arcLength(cnt,True),True)
            #Calculating the Centroid coordinates of the particular object by moments
            M = cv2.moments(cnt)
            cX = int(M['m10']/M['m00'])
            cY = int(M['m01']/M['m00'])
            area = cv2.contourArea(cnt)
            #Converting image to the HSV format
            hsv = cv2.cvtColor(self.img,cv2.COLOR_BGR2HSV)
            #Calling the functions for Shapes and Colors
            self.shape(approxCont)
            self.color(hsv,cX, cY)
            #Final List Containing the shape and the color of the object
            self.shapeList.append(tuple([shape,color, area, cX,cY]))

    def shape(self,approx):
        """Getting the name of shape of approximated contour"""
        global shape
        p1 = approx[0][0]
        p2 = approx[1][0]
        p3 = approx[-1][0]
        p4 = approx[2][0]
        if len(approx) == 3:
            shape = 'Triangle'
        elif len(approx) == 4:
            (degrees) = self.get_corner_angle(p1, p2, p3)
            (degrees_opp) = self.get_corner_angle_opp(p4, p2, p3)
            dist1 = self.distance(p1, p2, p3, p4)

            # print(degrees)
            if ((89 <= int(degrees) <= 91) and (89 <= int(degrees_opp) <= 91)) and (a == b):
                shape = "Square"
            elif (a == True or b == True)  and (int(degrees) or int(degrees_opp) !=90) and (int(degrees_opp)-int(degrees) not in (-1,0,1)):
                shape = "Trapezoid"
            elif (int(degrees)-int(degrees_opp) in (-1,0,1)) and(a-b in (-1,0,1)):
                shape = "Rhombus"
            elif (a == True or b == True) and (int(degrees) == int(degrees_opp)):
                shape  = "Parallelogram"
            elif (int(degrees) != int(degrees_opp)) and (a == False and b == False):
                shape = "Quadilateral"
        elif len(approx) == 5:
            shape = 'Pentagon'
        elif len(approx) == 6:
            shape = 'Hexagon'
        else:
            shape = 'Circle'

    def unit_vector(self, v):
        return v / np.linalg.norm(v)

    def distance(self, p1, p2, p3, p4):
        global l1, l2, l3, l4, a ,b 
        l1 = int(((p4[0] - p3[0])**2 + (p4[1] - p3[1])**2)**0.5)
        l2 = int(((p2[0] - p1[0])**2 + (p2[1] - p1[1])**2)**0.5)
        l3 = int(((p3[0] - p1[0])**2 + (p3[1] - p1[1])**2)**0.5)
        l4 = int(((p4[0] - p2[0])**2 + (p4[1] - p2[1])**2)**0.5)
        a = l1 == l2
        b = l3 == l4
        return l1, l2, l3, l4, a, b

    def get_corner_angle(self, p1, p2, p3):
        v1 = np.array([p1[0] - p2[0], p1[1] - p2[1]])
        v2 = np.array([p1[0] - p3[0], p1[1] - p3[1]])
        v1_unit = self.unit_vector(v1)
        v2_unit = self.unit_vector(v2)
        radians = np.arccos(np.clip(np.dot(v1_unit, v2_unit), -1, 1))
        return np.degrees(radians)
    
    def get_corner_angle_opp(self, p4, p2, p3):
        v3 = np.array([p4[0] - p2[0], p4[1] - p2[1]])
        v4 = np.array([p4[0] - p3[0], p4[1] - p3[1]])
        v3_unit = self.unit_vector(v3)
        v4_unit = self.unit_vector(v4)
        radians = np.arccos(np.clip(np.dot(v3_unit, v4_unit), -1, 1))
        return np.degrees(radians)

    def color(self,hsv_img,cX,cY):
        """Gives the name of the color of the shape"""
        global color

        #Getting Hue,Saturation,value of the centroid of the shape from HSV image
        h,s,v = hsv_img[cY,cX]

        #Getting final name of the color according their ranges in the HSV color space
        h,s,v = hsv_img[cY, cX]
        if h in range(0,11) or h in range(170,180):
            color = 'Red'
        elif h in range(51,76):
            color = 'Green'
        elif h in range(106,131):
            color = 'Blue'
        return color

#Creating the Object of class
shapeColorObj = ShapeColorRecognition(img)

#Final output
output=[]

for ans in shapeColorObj.shapeList:
    value = []
    key = (ans[0])
    value.append(ans[1])
    value.append(ans[2])
    value.append(ans[3])
    value.append(ans[4])
    
    output.append((key,value))

print(dict(sorted(output, key=lambda t: t[1][1], reverse=True)))