A Python implementation of the Seam Carving algorithm for content-aware image resizing, preserving key regions by dynamically removing low-energy seams using OpenCV. - ss-369/Seam-Carving-Algorithm
This example will run onPython 2.7andOpenCV 2.4.X/OpenCV 3.0+. For this introduction to basic image processing, I’m going to assume that you have basic knowledge of how to create and execute Python scripts. I’m also going to assume that you have OpenCV installed. If you...
A series of convenience functions to make basic image processing operations such as translation, rotation, resizing and skeletonization with OpenCV and C++. - minooei/imutils
content-aware resizing of images 对于缩小图像 image reduction:seam selection ensures that while preserving the image structure, we remove more of the low energy pixels and fewer of the high energy ones 多缩减低能量像素,少缩减高能量像素 对于放大图像 image enlarging: the order of seam insertion ens...
2. Python Solution (OpenCV) If you prefer OpenCV, use the following script: python import cv2 import os input_folder = "images/" output_folder = "resized_images/" os.makedirs(output_folder, exist_ok=True) for filename in os.listdir(input_folder): if filename.endswith((".jpg", "...
参考Seam Carving for Content-Aware Image Resizing论文实现代码。 对应blog:http://blog.csdn.net/u011630458/article/details/54171081 上传者:u011630458时间:2017-01-07 OpenCV 3.x with Python By Example, 2nd Edition-Packt Publishing(2018).pdf ...
Image compression can have a surprisingly large effect on FID. Images are perceptually indistinguishable from each other but have a large FID score. The FID scores under the images are calculated between all FFHQ images saved using the corresponding JPEG format and the PNG format. ...
generateColorMap()- utilised OpenCV inbuilt functions to superimpose heatmaps on the given image. File:seam_carve.py getEnergy()- generated energy map using sobel operators and convolve function. getMaps()- implemented the function to get seams using Dynamic Programming. Also, stored results of ...
However, if you intend on using Matplotlib, the plt.imshow function assumes the image is in RGB order. A simple call to cv2.cvtColor will resolve this problem, or you can use the opencv2matplotlib convenience function.Example:# INCORRECT: show the image without converting color spaces plt....
In the Python bindings of OpenCV, images are represented as NumPy arrays in BGR order. This works fine when using thecv2.imshowfunction. However, if you intend on using Matplotlib, theplt.imshowfunction assumes the image is in RGB order. A simple call tocv2.cvtColorwill resolve this problem...