opencv mask

opencv.py

import cv2
import numpy

image = cv2.imread("test.png")

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)  # convert image to gray

# cv2.imshow("image",gray)
blur_image = cv2.GaussianBlur(gray, (5, 5), 0)  # 平滑图像处理，高斯滤波

edged = cv2.Canny(blur_image, 180, 900)  # Canny方法对图像进行边缘检测

# cv2.imshow("Image", edged)
# cv2.waitKey(0)

# applying close function

kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (40, 40))
# opened = cv2.morphologyEx(edged, cv2.MORPH_OPEN, kernel)
close = cv2.morphologyEx(edged, cv2.MORPH_CLOSE, kernel)

dilated = cv2.dilate(close, None, iterations=2)

cv2.imshow("Closed", dilated)
cv2.waitKey(0)

_, contours, hierarchy = cv2.findContours(close.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

# # work files
# cv2.drawContours(image, contours, -1, (0, 0, 255), 3)
# #  #绘制结果
# cv2.imshow("result", image)

# cv2.drawContours(image, contours, -1, (0, 0, 255), 3)
# 绘制结果
# cv2.imshow("result", image)

# mask = numpy.full(image.shape[:2], 255, dtype=numpy.uint8)  # white space
# mask = numpy.zeros_like(image)

# cv2.imshow("mask",mask)
# cv2.drawContours(mask, contours, 0, 255, 1)
# print(type(mask))
# print(mask.shape)
# print(mask.ndim)
# print(mask.dtype)
#
# cv2.imshow("ss", mask)

result_image = cv2.bitwise_and(image,image,mask=dilated)
# # print(mask)
cv2.imshow("hh",result_image)

# cv2.drawContours(mask, contours, 0, 255, -1)
# #
# cv2.imshow("5",result_image)
# #
# out = numpy.zeros_like(image)
# out[mask == 255] = image[mask == 255]
# cv2.imshow("output", out)
# cv2.waitKey(0)
# mask = np.ones((image.height,image.width),dtype=np.uint8)

# m#ask[:,:]= 0

# croped = np.zeros()

#  // you could also reuse img1 here
#     Mat mask = Mat::zeros(img1.rows, img1.cols, CV_8UC1);
#
#     // CV_FILLED fills the connected components found
#     drawContours(mask, contours, -1, Scalar(255), CV_FILLED);
#
#     /*
#      Before drawing all contours you could also decide
#      to only draw the contour of the largest connected component
#      found. Here's some commented out code how to do that:
#     */
#
# //    vector<double> areas(contours.size());
# //    for(int i = 0; i < contours.size(); i++)
# //        areas[i] = contourArea(Mat(contours[i]));
# //    double max;
# //    Point maxPosition;
# //    minMaxLoc(Mat(areas),0,&max,0,&maxPosition);
# //    drawContours(mask, contours, maxPosition.y, Scalar(1), CV_FILLED);
#
#     // let's create a new image now
#     Mat crop(img0.rows, img0.cols, CV_8UC3);
#
#     // set background to green
#     crop.setTo(Scalar(0,255,0));
#
#     // and copy the magic apple
#     img0.copyTo(crop, mask);


# idx = 0
#
# mask = np.zeros_like(image) # Create mask where white is what we want, black otherwise
# cv2.drawContours(mask, contours, idx, 255, -1) # Draw filled contour in mask
# out = np.zeros_like(image) # Extract out the object and place into output image
# out[mask == 255] = image[mask == 255]
#
# # Show the output image
# cv2.imshow('Output', out)
# cv2.waitKey(0)
# for c in contours:
# 	x,y,w,h = cv2.boundingRect(c)
# 	if w>50 and h>50:
# 		idx+=1
# 		new_img=image[y:y+h,x:x+w]
# 		cv2.imwrite(str(idx) + '.png', new_img)
# cv2.imshow("im",image)




# cv2.imshow("Show Boxes", image)

# for c in cnts:
#     peri = cv2.arcLength(c, True)
#     approx = cv2.approxPolyDP(c, 0.02 * peri, True)
#     cv2.drawContours(image, [approx], -1, (0, 255, 0), 2)
# cv2.imshow("Output", image)
cv2.waitKey(0)