如何将缩小图像的轮廓应用于原始图像？

Question

我有一个完美的代码可以用 OpenCV 找到轮廓。但是，我的代码处理缩小的图像以提高计算速度。如何将缩小图像的轮廓应用于原始图像？

这是我的 Python 代码：

# Image Read and Resizing
source_image = cv.imread(image_path)
copied_image = source_image.copy()
copied_image = imutils.resize(copied_image, height=500)

# Apply GaussianBlur + OTSU-Thresholding
grayscale_image = cv.cvtColor(copied_image, cv.COLOR_BGR2GRAY)
grayscale_image = cv.GaussianBlur(grayscale_image, (5, 5), 0)
ret, grayscale_image = cv.threshold(grayscale_image, 200, 255, cv.THRESH_BINARY + cv.THRESH_OTSU)

# Find Contours
contours, hierarchy = cv.findContours(grayscale_image, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
contour_sizes = [(cv.contourArea(contour), contour) for contour in contours]
biggest_contour = max(contour_sizes, key=lambda x: x[0])[1]

# Crop Image
x, y, w, h = cv.boundingRect(biggest_contour)
cropped_image = copied_image[y:y + h, x:x + w]

copied_image 小于source_image。我只使用了最大的轮廓。现在，我想用source_image 应用找到的轮廓。但是，在我的代码中，获取的轮廓是基于copied_image。

Answer 1

如果您可以接受 1 或 2 个像素的（不）精度，那么一个非常简单的解决方案就是将边界矩形的 x, y, w, h 值乘以相应的缩放因子：

import cv2
import numpy as np

# Set up some test image
image = np.zeros((400, 400), np.uint8)
image = cv2.circle(image, (160, 160), 80, 255, cv2.FILLED)

# Find contour, and determine original bounding rectangle
cnt_orig = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[0]
x, y, w, h = cv2.boundingRect(cnt_orig[0])
print('Original bounding rectangle: ', x, y, w, h)

# Downsize image
image_small = cv2.resize(image.copy(), (124, 287))

# Determine scaling factors
scale_x = image.shape[1] / image_small.shape[1]
scale_y = image.shape[0] / image_small.shape[0]

# Find contour, and determine reconstructed bounding rectangle w.r.t. the scaling factors
cnt_small = cv2.findContours(image_small, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[0]
x, y, w, h = cv2.boundingRect(cnt_small[0]) * np.array([scale_x, scale_y, scale_x, scale_y])
print('Reconstructed bounding rectangle: ', x, y, w, h)

输出：

Original bounding rectangle:  80 80 161 161
Reconstructed bounding rectangle:  80.64... 79.44... 161.29... 161.67...

注意：使用的测试图像非常简单。在更复杂的图像中找到更复杂的轮廓时，（不）准确度可能会提高。

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System information
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Platform:    Windows-10-10.0.16299-SP0
Python:      3.8.5
NumPy:       1.19.4
OpenCV:      4.4.0
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