英文:
Crop image opencv python
问题
请帮助我裁剪这张图片。
我需要将下面的图像分成两张图像进行比较，但我希望以这样的方式切割它们，使得两张裁剪后的图像具有相同的大小，并移除两张图像之间的多余区域。
我已经使用以下代码进行裁剪，但似乎效果不太好。
import cv2import numpy as np# 读取原始图像image = cv2.imread('2.jpg')# 获取图像的高度和宽度height, width = image.shape[:2]# 计算两个图像之间的切割位置middle = width // 2# 裁剪并获取两个部分的图像image1 = image[:, :middle]image2 = image[:, middle:]# 在图像1中查找轮廓gray1 = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY)_, thresh1 = cv2.threshold(gray1, 1, 255, cv2.THRESH_BINARY)contours1, _ = cv2.findContours(thresh1, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)x1, _, w1, _ = cv2.boundingRect(contours1[0])# 在图像2中查找轮廓gray2 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)_, thresh2 = cv2.threshold(gray2, 1, 255, cv2.THRESH_BINARY)contours2, _ = cv2.findContours(thresh2, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)x2, _, w2, _ = cv2.boundingRect(contours2[0])# 剪切掉两个图像之间的黑色区域image1 = image1[:, x1:(x1+w1)]image2 = image2[:, x2:(x2+w2)]# 显示裁剪后的两张图像cv2.imshow('Image 1', image1)cv2.imshow('Image 2', image2)cv2.waitKey(0)cv2.destroyAllWindows()
请帮助我编写能够按照要求裁剪图像的代码。
英文:
Please help me crop this image.
I need to split the image below into two images to compare them, but I want to cut them in such a way that the two cropped images have the same size and remove the excess area between the two images.
I have used the following code to crop, but it seems to be not very effective.
import cv2import numpy as np# Đọc bức ảnh gốcimage = cv2.imread('2.jpg')# Lấy chiều cao và chiều rộng của ảnhheight, width = image.shape[:2]# Tính vị trí cắt giữa hai ảnhmiddle = width // 2# Cắt và lấy hai phần bức ảnhimage1 = image[:, :middle]image2 = image[:, middle:]# Tìm contours trong ảnh 1gray1 = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY)_, thresh1 = cv2.threshold(gray1, 1, 255, cv2.THRESH_BINARY)contours1, _ = cv2.findContours(thresh1, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)x1, _, w1, _ = cv2.boundingRect(contours1[0])# Tìm contours trong ảnh 2gray2 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)_, thresh2 = cv2.threshold(gray2, 1, 255, cv2.THRESH_BINARY)contours2, _ = cv2.findContours(thresh2, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)x2, _, w2, _ = cv2.boundingRect(contours2[0])# Cắt bỏ vùng đen giữa hai ảnhimage1 = image1[:, x1:(x1+w1)]image2 = image2[:, x2:(x2+w2)]# Hiển thị hai ảnh đã cắtcv2.imshow('Image 1', image1)cv2.imshow('Image 2', image2)cv2.waitKey(0)cv2.destroyAllWindows()
Please help me with the code to crop the image as requested.
答案1
得分: 1
以下是在Python/OpenCV中分离两个部分的一种方法：
- 读取输入
- 在黑色上进行阈值处理
- 应用形态学开运算以去除细线
- 获取最大的外部轮廓
- 获取边界框
- 使用输入和边界框的尺寸来裁剪两个部分
- 保存结果
输入：
import cv2import numpy as np# 读取图像img = cv2.imread('tom_and_jerry.jpg')hh, ww = img.shape[:2]# 在黑色上进行阈值处理lower = (0,0,0)upper = (10,10,10)thresh = cv2.inRange(img, lower, upper)# 应用形态学开运算以去除细线kernel = np.ones((3,3), np.uint8)morph = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)# 获取最大轮廓contours = cv2.findContours(morph, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)contours = contours[0] if len(contours) == 2 else contours[1]big_contour = max(contours, key=cv2.contourArea)# 获取边界框x,y,w,h = cv2.boundingRect(big_contour)# 将图像裁剪成两个部分crop1 = img[0:hh, 0:x]crop2 = img[0:hh, x+w:ww]# 保存结果cv2.imwrite('tom_and_jerry_thresh.jpg', thresh)cv2.imwrite('tom_and_jerry_morph.jpg', morph)cv2.imwrite('tom_and_jerry_crop1.jpg', crop1)cv2.imwrite('tom_and_jerry_crop2.jpg', crop2)# 显示结果cv2.imshow('thresh', thresh)cv2.imshow('morph', morph)cv2.imshow('crop1', crop1)cv2.imshow('crop2', crop2)cv2.waitKey(0)
阈值处理后的图像：
经过形态学处理后的图像：
裁剪1：
裁剪2：
英文:
Here is one way to separate the two parts in Python/OpenCV
- Read the input
- Threshold on black
- Apply morphology open to remove thin lines
- Get the largest external contour
- Get the bounding box
- Use the size of the input and bounding box to crop the two parts
- Save the results
Input:
import cv2import numpy as np# read the imageimg = cv2.imread('tom_and_jerry.jpg')hh, ww = img.shape[:2]# threshold on blacklower = (0,0,0)upper = (10,10,10)thresh = cv2.inRange(img, lower, upper)# apply morphology open to remove thin lineskernel = np.ones((3,3), np.uint8)morph = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)# get largest contourcontours = cv2.findContours(morph, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)contours = contours[0] if len(contours) == 2 else contours[1]big_contour = max(contours, key=cv2.contourArea)# get bounding boxx,y,w,h = cv2.boundingRect(big_contour)# crop the image into two partscrop1 = img[0:hh, 0:x]crop2 = img[0:hh, x+w:ww]# save resultscv2.imwrite('tom_and_jerry_thresh.jpg', thresh)cv2.imwrite('tom_and_jerry_morph.jpg', morph)cv2.imwrite('tom_and_jerry_crop1.jpg', crop1)cv2.imwrite('tom_and_jerry_crop2.jpg', crop2)# show resultscv2.imshow('thresh', thresh)cv2.imshow('morph', morph)cv2.imshow('crop1', crop1)cv2.imshow('crop2', crop2)cv2.waitKey(0)
Threshold Image:
Morphology Cleaned Image:
Crop 1:
Crop 2:
答案2
得分: 1
这是使用Python/OpenCV的另一种方法。
- 读取输入
- 基于黑色进行阈值处理
- 应用形态学开操作以去除细小区域
- 获取沿每列的非零像素计数
- 找到第一个（最小）位置，其中计数大于某个阈值（99%）
- 找到最后一个（最大）位置，其中计数大于某个阈值（99%）
- 使用这些值来裁剪图像为两部分
- 保存结果
输入：
import cv2import numpy as np# 读取图像img = cv2.imread('tom_and_jerry.jpg')hh, ww = img.shape[:2]# 基于黑色进行阈值处理lower = (0, 0, 0)upper = (10, 10, 10)thresh = cv2.inRange(img, lower, upper)# 应用形态学开操作以去除细线kernel = np.ones((3, 3), np.uint8)morph = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)# 计算每列中非零像素的数量counts = np.count_nonzero(morph, axis=0)# 获取计数大于等于hh的99%的第一个和最后一个位置T = 0.99min = np.amin(np.where(counts >= T * hh))max = np.amax(np.where(counts >= T * hh))print(min, max)# 裁剪开始的x位置和宽度wx = min - 1w = max - min + 2# 将图像裁剪成两部分crop1 = img[0:hh, 0:x]crop2 = img[0:hh, x + w:ww]# 保存结果cv2.imwrite('tom_and_jerry_thresh.jpg', thresh)cv2.imwrite('tom_and_jerry_morph.jpg', morph)cv2.imwrite('tom_and_jerry_crop1.jpg', crop1)cv2.imwrite('tom_and_jerry_crop2.jpg', crop2)# 显示结果cv2.imshow('thresh', thresh)cv2.imshow('morph', morph)cv2.imshow('crop1', crop1)cv2.imshow('crop2', crop2)cv2.waitKey(0)
裁剪1：
裁剪2：
英文:
Here is an alternate approach using Python/OpenCV.
- Read the input
- Threshold on black
- Apply morphology open to remove thin regions
- Get the counts of non-zero pixels along each column
- Find the first (min) location where the counts are greater than some threshold (99%)
- Find the last (max) location where the counts are greater than some threshold (99%)
- Use those values to crop the image into the two parts
- Save the results
Input:
import cv2import numpy as np# read the imageimg = cv2.imread('tom_and_jerry.jpg')hh, ww = img.shape[:2]# threshold on blacklower = (0,0,0)upper = (10,10,10)thresh = cv2.inRange(img, lower, upper)# apply morphology open to remove thin lineskernel = np.ones((3,3), np.uint8)morph = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)# count the number of non-zero pixels in each columncounts = np.count_nonzero(morph, axis=0)# get the first and last position where counts >= 99% of hhT=0.99min = np.amin(np.where(counts>=T*hh))max = np.amax(np.where(counts>=T*hh))print(min,max)# crop start x and width wx = min-1w = max-min+2# crop the image into two partscrop1 = img[0:hh, 0:x]crop2 = img[0:hh, x+w:ww]# save resultscv2.imwrite('tom_and_jerry_thresh.jpg', thresh)cv2.imwrite('tom_and_jerry_morph.jpg', morph)cv2.imwrite('tom_and_jerry_crop1.jpg', crop1)cv2.imwrite('tom_and_jerry_crop2.jpg', crop2)# show resultscv2.imshow('thresh', thresh)cv2.imshow('morph', morph)cv2.imshow('crop1', crop1)cv2.imshow('crop2', crop2)cv2.waitKey(0)
Crop 1:
Crop 2:
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