fmw42
2020-12-01 07:07:07
这是Python Wand中的一个简单示例。它将列出均方根差异，并显示一个以红色突出显示差异的图像。
输入1：
输入2：
from wand.image import Image
from wand.display import display
with Image(filename='img1.jpg') as bimg:
with Image(filename='img2.jpg') as fimg:
bimg.fuzz = 0.25*bimg.quantum_range
bimg.artifacts['compare:highlight-color'] = 'red'
bimg.artifacts['compare:lowlight-color'] = 'transparent'
diff_img, diff_val = bimg.compare(fimg, 'root_mean_square')
print(diff_val)
with diff_img:
diff_img.save(filename='img1_img2_diff.jpg')
display(diff_img)
RMSE差异：
0.0238380675979382
差异图片：
现在，用相同的图像1重复。
from wand.image import Image
from wand.display import display
with Image(filename='img1.jpg') as bimg:
with Image(filename='img1.jpg') as fimg:
bimg.fuzz = 0.25*bimg.quantum_range
bimg.artifacts['compare:highlight-color'] = 'red'
bimg.artifacts['compare:lowlight-color'] = 'transparent'
diff_img, diff_val = bimg.compare(fimg, 'root_mean_square')
print(diff_val)
with diff_img:
diff_img.save(filename='img1_img2_diff.jpg')
display(diff_img)
RMSE差异：
0.0
热门帖子
热门github
1
2
4
The Python Risk Identification Tool for generative AI (PyRIT) is an open access automation framework to empower security professionals and machine learning engineers to proactively find risks in their generative AI systems.
(翻译：用于生成式 AI 的 Python 风险识别工具 (PyRIT) 是一个开放式访问自动化框架，使安全专业人员和机器学习工程师能够主动发现其生成式 AI 系统中的风险。)
5
7
8
9
Mamba is a new state space model architecture showing promising performance on information-dense data such as language modeling, where previous subquadratic models fall short of Transformers. It is based on the line of progress on structured state space models, with an efficient hardware-aware design and implementation in the spirit of FlashAttention.
(翻译：Mamba 是一种新的状态空间模型架构，在信息密集型数据（例如语言建模）上显示出良好的性能，而之前的二次模型在 Transformers 方面存在不足。它基于结构化状态空间模型的进展，并本着FlashAttention的精神进行高效的硬件感知设计和实现。)
10
11
13
谢谢，似乎doc已过时，例如@emcconville说，那么0.02 RMSE差异意味着图像差异约2％？