scikit-image
MiDaS
scikit-image | MiDaS | |
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10 | 27 | |
5,901 | 4,137 | |
1.1% | 2.6% | |
9.6 | 2.4 | |
6 days ago | 3 months ago | |
Python | Python | |
GNU General Public License v3.0 or later | MIT License |
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scikit-image
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How to Estimate Depth from a Single Image
We will use the Hugging Face transformers and diffusers libraries for inference, FiftyOne for data management and visualization, and scikit-image for evaluation metrics.
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.
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Converting Scikit-Learn Library Algorithms to C
scikit hog library: https://github.com/scikit-image/scikit-image/blob/main/skimage/feature/_hog.py#L302 , https://github.com/scikit-image/scikit-image/blob/main/skimage/feature/_hoghistogram.pyx
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Is it possible to add a noise to an image in python?
This is a good cv deep learning book with python examples https://www.manning.com/books/deep-learning-for-vision-systems. If you're pretty comfortable with the concepts of traditional image processing this is a good companion to cv2 (so you don't have to reinvent the wheel) https://scikit-image.org/
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A CLI that does simple image processing and also generates cool patterns
Also, don't know if you're familiar with Python, but if you need ideas for to implement for future directions : https://scikit-image.org/
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Color Matrices for scan correction
There's probably something in scikit-image to do what you want, or close enough to build on.
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Python: The Best Image Processing Libraries
Scikit-image The Scikit-image library is a collection of image processing algorithms that are designed to be easy to use and understand. It includes algorithms for common tasks like edge detection, feature extraction, and image restoration. If you are just starting out in image processing, then this is a good library to check out!
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Image Processing is Easier than you Thought! (Getting started with Python Pillow)
Python is a general-purpose programming language that provides many image processing libraries for adding image processing capabilities to digital images. Some of the most common image processing libraries in Python are OpenCV, Python Imaging Library (PIL), Scikit-image etc.
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Scikit-image for Image Processing
Then you would need to find what this plugin does for imshow. First thing you can see is that "interpolation" is not "bicubic" as you used, but "nearest"… but there are other settings here that are responsible for the difference of displays. (it's better that you look at the source code in your environment, as it might be slightly different)
- Patented algorithm removed from scikit-image shortly before merge accept
MiDaS
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How to Estimate Depth from a Single Image
The checkpoint below uses MiDaS, which returns the inverse depth map, so we have to invert it back to get a comparable depth map.
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Distance estimation from monocular vision using deep learning
Hi, I have made use of the KITTI dataset for this, and yes it depends on objects of know sizes. Here I have defined the following classes: Car, Van, Truck, Pedestrian, Person_sitting, Cyclist, Tram, Misc, or DontCare and the predictions are pretty accurate for those classes. Even if it's not the same class, it still recognizes the object since I have made use of the coco names dataset here and that is used along with YOLO for object detection. And there are several already implemented projects that make use of deep learning models trained on 2D datasets to predict 3D distance. This was one of my inspirations for this project: https://blogs.nvidia.com/blog/2019/06/19/drive-labs-distance-to-object-detection/ Furthermore, there are well-documented and researched papers like DistYOLO or MiDaS that makes use of deep learning for depth estimation
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OMPR V0.6.10 update
-Added AI image depth generator Create your own depth map image at a click of a button. Using the awesome MIDAS3.1 https://github.com/isl-org/MiDaS as the backend and the model "dpt_beit_large_512" for the highest quality depth map. Video and GIF depth map generators coming out next together with the Depth movie player feature.
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AI that converts a regular 2d image to stereoscopic
It uses MiDaS. That extension may be the most accessible way to use it at home. IDK.
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Idea: training on magiceye images
Here's the project homepage https://github.com/isl-org/MiDaS
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MiDaS v3_1 and DiscoDiffusion
The problem came up after MiDaS updated to version V3_1 on Dec 24th. Although the fix works fine, with the new version there are many changes, which for me produces slightly different results. I would like to able to produce results like before. I still clone the MiDaS repo, but then set it back to the last commit before the changes in december, which is 66882994a432727317267145dc3c2e47ec78c38a.
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File not found error
try: from midas.dpt_depth import DPTDepthModel except: if not os.path.exists('MiDaS'): gitclone("https://github.com/isl-org/MiDaS.git") gitclone("https://github.com/bytedance/Next-ViT.git", f'{PROJECT_DIR}/externals/Next_ViT') if not os.path.exists('MiDaS/midas_utils.py'): shutil.move('MiDaS/utils.py', 'MiDaS/midas_utils.py') if not os.path.exists(f'{model_path}/dpt_large-midas-2f21e586.pt'): wget("https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt", model_path) sys.path.append(f'{PROJECT_DIR}/MiDaS')
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A quick demo to show how structurally coherent depth2img is compared to img2img using Automatic1111.
Cool. The repo for MiDaS is here. https://github.com/isl-org/MiDaS You can see that they partially trained the model on 3D movies Here's a list of the movies that were used to train it. I wonder if they'll be training a MiDaS v 4.0 as things have moved on quite a bit since it was released in Apr 2021?
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Boosting Monocular Depth repo
We present a stand-alone implementation of our Merging Operator. This new repo allows using any pair of monocular depth estimations in our double estimation. This includes using separate networks for base and high-res estimations, using networks not supported by this repo (such as Midas-v3), or using manually edited depth maps for artistic use. This will also be useful for scientists developing CNN-based MDE as a way to quickly apply double estimation to their own network. For more details please take a look here.
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DepthViewer is now live on Steam :)
I'll make the feature to export only the depthmap .png file. If you need the depthmap .png right now you can use the MiDaS python script.
What are some alternatives?
pillow - Python Imaging Library (Fork)
stable-diffusion-webui-depthmap-script - High Resolution Depth Maps for Stable Diffusion WebUI
OpenCV - Open Source Computer Vision Library
DenseDepth - High Quality Monocular Depth Estimation via Transfer Learning
nude.py - Nudity detection with Python
stablediffusion - High-Resolution Image Synthesis with Latent Diffusion Models
python-qrcode - Python QR Code image generator
deeplearning4j-examples - Deeplearning4j Examples (DL4J, DL4J Spark, DataVec) [Moved to: https://github.com/deeplearning4j/deeplearning4j-examples]
thumbor - thumbor is an open-source photo thumbnail service by globo.com
DiverseDepth - The code and data of DiverseDepth
wand - The ctypes-based simple ImageMagick binding for Python
Insta-DM - Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistency (AAAI 2021)