1. 圖像的幾何變換

imgobj = cv2.imread('pho.png') #讀取圖像

cv2.namedWindow("image") #創(chuàng)建窗口并顯示的是圖像類型

cv2.imshow("image",imgobj)

cv2.waitKey(0)??????? #等待事件觸發(fā)，參數(shù)0表示永久等待

cv2.destroyAllWindows()?? #釋放窗口

# resize

res = cv2.resize(imgobj,None,fx=0.5, fy=0.5, interpolation = cv2.INTER_CUBIC)

cv2.imshow("image",res)

#warpAffine?? 仿射

rows,cols = res.shape[0:2]

M = np.float32([[1,0,100],[0,1,50]])

dst = cv2.warpAffine(res,M,(cols,rows))

cv2.imshow('img',dst)

#getRotationMatrix2D? 旋轉(zhuǎn)

M = cv2.getRotationMatrix2D((cols/2,rows/2),90,1)#長寬減半 逆時針90度 縮放1倍

dst2 = cv2.warpAffine(res,M,(cols,rows))

cv2.imshow('img',dst2)

#flip 翻轉(zhuǎn)

dst3=cv2.flip(res,1)#0延x軸翻轉(zhuǎn)? 任意正數(shù)延y軸翻轉(zhuǎn)? 任意負數(shù)延x/y軸同時翻轉(zhuǎn)

cv2.imshow('img',dst3)

#二值化

gray = cv2.cvtColor(res, cv2.COLOR_BGR2GRAY)

ret, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)

cv2.imshow('img',binary)

cv2.waitKey(0)

cv2.destroyAllWindows()

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1. 霍夫變換? 圓檢測

def circle_detect(img):

??? gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

??? trans = cv2.GaussianBlur(gray, (9, 9), 0)

??? ret_1, thresh_1 = cv2.threshold(trans, 40, 255, cv2.THRESH_BINARY)

?? circles_2 = cv2.HoughCircles(thresh_1, cv2.HOUGH_GRADIENT, 1, 10, param1=1000, ?????? param2=20)

??? local_2 = circles_2[0][0]

??? x_2 = int(local_2[0])

??? y_2 = int(local_2[1])

??? r_2 = int(local_2[2])

??? img = cv2.circle(img, (x_2, y_2), r_2, (0, 255, 0), 2)

??? n = 10

??? img = cv2.rectangle(img, (x_2 - r_2 - n, y_2 - r_2 - n), (x_2 + r_2 + n, y_2 + r_2 + n), (255, ?? ???????? 255, 0), 3)

??? return img

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if __name__ == '__main__':

??? img = cv2.imread('rec_cir.jpg')

??? # print(img.shape)

??? circle_detect(img)

??? # cv2.namedWindow('addImage')

??? cv2.imshow('img_add', img)

??? cv2.waitKey(0)

cv2.destroyAllWindows()

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1. 輪廓檢測?? 形狀檢測

需要進一步預處理? 獲取更好的特征

import cv2 as cv

import numpy as np

class ShapeAnalysis:

??? def __init__(self):

??????? self.shapes = {'triangle': 0, 'rectangle': 0, 'polygons': 0, 'circles': 0}

??? def analysis(self, frame):

??????? h, w, ch = frame.shape

??????? result = np.zeros((h, w, ch), dtype=np.uint8)

??????? # 二值化圖像

??????? print("start to detect lines...\n")

??????? gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)

??????? trans = cv.GaussianBlur(gray, (9, 9), 0)

??????? ret_1, thresh_1 = cv.threshold(trans, 40, 255, cv.THRESH_BINARY)

??????? # ret, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY_INV | cv.THRESH_OTSU)

??????? cv.imshow("input image", frame)

??????? contours, hierarchy = cv. findContours (thresh_1, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)

??????? for cnt in range(len(contours)):

??????????? # 提取與繪制輪廓

??????????? cv. drawContours (result, contours, cnt, (0, 255, 0), 2)

??????????? # 輪廓逼近

??????????? epsilon = 0.01 * cv.arcLength(contours[cnt], True)

??????????? approx = cv. approxPolyDP (contours[cnt], epsilon, True)

?

??????????? # 分析幾何形狀

??????????? corners = len(approx)

??????????? shape_type = ""

??????????? if corners == 3:

??????????????? count = self.shapes['triangle']

??????????????? count = count+1

??????????????? self.shapes['triangle'] = count

??????????????? shape_type = "三角形"

??????????? if corners == 4:

??????????????? count = self.shapes['rectangle']

??????????????? count = count + 1

??????????????? self.shapes['rectangle'] = count

?????? ?????????shape_type = "矩形"

??????????? if corners >= 10:

??????????????? count = self.shapes['circles']

??????????????? count = count + 1

??????????????? self.shapes['circles'] = count

??????????????? shape_type = "圓形"

??????????? if 4 < corners < 10:

??????????????? count = self.shapes['polygons']

??????????????? count = count + 1

??????????????? self.shapes['polygons'] = count

??????????????? shape_type = "多邊形"

??????????? # 求解中心位置

??????????? mm = cv.moments(contours[cnt])

??????????? cx = int(mm['m10'] / mm['m00'])

??????????? cy = int(mm['m01'] / mm['m00'])

??????????? cv.circle(result, (cx, cy), 3, (0, 0, 255), -1)

??????????? # 顏色分析

??????????? color = frame[cy][cx]

??????????? color_str = "(" + str(color[0]) + ", " + str(color[1]) + ", " + str(color[2]) + ")"

??????????? # 計算面積與周長

??????????? p = cv.arcLength(contours[cnt], True)

??????????? area = cv.contourArea(contours[cnt])

??????????? print("周長: %.3f, 面積: %.3f 顏色: %s 形狀: %s "% (p, area, color_str, shape_type))

??????? cv.imshow("Analysis Result", self.draw_text_info(result))

??????? cv.imwrite("test-result.png", self.draw_text_info(result))

??????? return self.shapes

?

??? def draw_text_info(self, image):

??????? c1 = self.shapes['triangle']

??????? c2 = self.shapes['rectangle']

??????? c3 = self.shapes['polygons']

??????? c4 = self.shapes['circles']

??????? cv.putText(image, "triangle: "+str(c1), (10, 20), cv.FONT_HERSHEY_PLAIN, 1.2, (255, 0, 0), 1)

??????? cv.putText(image, "rectangle: " + str(c2), (10, 40), cv.FONT_HERSHEY_PLAIN, 1.2, (255, 0, 0), 1)

??????? cv.putText(image, "polygons: " + str(c3), (10, 60), cv.FONT_HERSHEY_PLAIN, 1.2, (255, 0, 0), 1)

??????? cv.putText(image, "circles: " + str(c4), (10, 80), cv.FONT_HERSHEY_PLAIN, 1.2, (255, 0, 0), 1)

??????? return image

?

if __name__ == "__main__":

??? src = cv.imread("rec_cir.jpg")

??? ld = ShapeAnalysis()

??? ld.analysis(src)

??? cv.waitKey(0)

cv.destroyAllWindows()

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1. 圖像分割

#去噪求梯度

??? gray = cv2.cvtColor(img_cv2, cv2.COLOR_BGR2GRAY)

??? trans = cv2.GaussianBlur(gray, (9, 9), 0)

??? gradX = cv2.Sobel(gray, ddepth=cv2.CV_32F, dx=1, dy=0)

??? gradY = cv2.Sobel(gray, ddepth=cv2.CV_32F, dx=0, dy=1)

??? gradient = cv2.subtract(gradX, gradY)

??? gradient = cv2.convertScaleAbs(gradient)

?

??? #去噪 二值化

??? blurred = cv2.GaussianBlur(gradient, (9, 9), 0)

??? (_, thresh) = cv2.threshold(blurred, 90, 255, cv2.THRESH_BINARY)

??? #形態(tài) 腐蝕膨脹

??? kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (25, 25))

??? closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)

??? closed = cv2.erode(closed, None, iterations=4)

??? closed = cv2.dilate(closed, None, iterations=4)

?

?

??? ret_1, binary = cv2.threshold(thresh, 90, 255, cv2.THRESH_BINARY)

??? # ret, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)

??? contours, hierarchy= cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

?

??? c = sorted(contours, key=cv2.contourArea, reverse=True)[0]

??? # compute the rotated bounding box of the largest contour

??? rect = cv2.minAreaRect(c)

??? box = np.int0(cv2.boxPoints(rect))

??? # draw a bounding box arounded the detected barcode and display the image

??? # draw_img = cv2.drawContours(img_cv2, [box], -1, (0, 0, 255), 3)

??? # cv2.imshow("draw_img", draw_img)

?????????? cv2.drawContours(img_cv2, [box], -1, (0, 0, 255), 3)

??? for cnt in range(len(contours)):

??????? # 提取與繪制輪廓

??????? cv2.drawContours(img_cv2, contours, cnt, (0, 255, 0), 3)

??????

但目標提取效果很好，當圖片出現(xiàn)多目標分割且背景復雜或有噪聲情況下，無法準確分割出目標圖像

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1. createBackgroundSubtractorMOG2背景去除

import numpy as np

import cv2

cap = cv2.VideoCapture(0)

fgbg = cv2.createBackgroundSubtractorMOG2(detectShadows = False)

# fgbg = cv2.createBackgroundSubtractorKNN()

while(cap.isOpened()):

??? ret, frame = cap.read()

??? fgmask = fgbg.apply(frame)

??? cv2.imshow('frame',fgmask)

??? k = cv2.waitKey(30) & 0xff

??? if k == 27:

??????? break

cap.release()

cv2.destroyAllWindows()