From 7e89697b8f88955c293d426cf1ebd273c00059d2 Mon Sep 17 00:00:00 2001 From: Julio Cesar Contreras Huerta Date: Mon, 2 Dec 2024 22:49:50 +0100 Subject: [PATCH] up images --- README.md | 92 +++++++++++++++++++++++++--------- assets/images/bibi.png | Bin 0 -> 433743 bytes assets/images/cnns_plot.png | Bin 0 -> 913029 bytes assets/images/mambas_plot.png | Bin 0 -> 903530 bytes assets/images/mean.png | Bin 0 -> 880158 bytes assets/images/swins_plot.png | Bin 0 -> 910466 bytes 6 files changed, 69 insertions(+), 23 deletions(-) create mode 100644 assets/images/bibi.png create mode 100644 assets/images/cnns_plot.png create mode 100644 assets/images/mambas_plot.png create mode 100644 assets/images/mean.png create mode 100644 assets/images/swins_plot.png diff --git a/README.md b/README.md index b007b22..3662bf5 100644 --- a/README.md +++ b/README.md @@ -36,6 +36,29 @@ --- + +## **Table of Contents** + +- [**Overview** 📊](#overview-) +- [**Installation** ⚙️](#installation-) +- [**How to use** 🛠️](#how-to-use-) + - [**Load libraries**](#load-libraries) + - [**Download Sentinel-2 L2A cube**](#download-sentinel-2-l2a-cube) + - [**Prepare the data (CPU and GPU usage)**](#prepare-the-data-cpu-and-gpu-usage) + - [**Default model setup**](#default-model-setup) + - [**Configuring the Spatial Resolution Enhancement Model**](#configuring-the-spatial-resolution-enhancement-model) + - [**Available Models:**](#available-models) + - [**1. CNN Models**](#1-cnn-models) + - [**2. SWIN Models**](#2-swin-models) + - [**3. MAMBA Models**](#3-mamba-models) + - [**4. Diffusion Model**](#4-diffusion-model) + - [**5. Simple Models (Bilinear and Bicubic)**](#5-simple-models-bilinear-and-bicubic) + - [**Predict only RGBNIR bands** 🌍](#predict-only-rgbnir-bands-) + - [**Estimate the uncertainty of the model** 📊](#estimate-the-uncertainty-of-the-model-) + - [**Estimate the Local Attention Map of the model** 📊](#estimate-the-local-attention-map-of-the-model-) + - [**Use the opensr-test and supers2 to analyze the hallucination pixels** 📊](#use-the-opensr-test-and-supers2-to-analyze-the-hallucination-pixels-) + + ## **Overview** 📊 **supers2** is a Python package designed to enhance the spatial resolution of Sentinel-2 satellite images to 2.5 meters using a set of neural network models. @@ -73,13 +96,13 @@ import supers2 ```python # Create a Sentinel-2 L2A data cube for a specific location and date range da = cubo.create( - lat=4.31, - lon=-76.2, - collection="sentinel-2-l2a", - bands=["B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B11", "B12"], - start_date="2021-06-01", - end_date="2021-10-10", - edge_size=128, + lat=39.49152740347753, + lon=-0.4308725142800361, + collection="sentinel-2-l2a", + bands=["B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B11", "B12"], + start_date="2023-01-01", + end_date="2023-12-31", + edge_size=64, resolution=10 ) ``` @@ -107,7 +130,8 @@ original_s2_numpy = (da[11].compute().to_numpy() / 10_000).astype("float32") # Create the tensor and move it to the appropriate device (CPU or GPU) X = torch.from_numpy(original_s2_numpy).float().to(device) ``` -#### **Default model setup** +### **Default model setup** + The default model is pre-trained for 2.5m resolution but supports 5m and 10m resolutions via the `resolution` parameter. It uses lightweight CNN architectures for super-resolution and fusion (`sr_model_snippet`, `fusionx2_model_snippet`, `fusionx4_model_snippet`). Models run on CPU or GPU, configurable via `device`. For more details on the architectures, refer to the [section](#available-models). ```python @@ -133,13 +157,13 @@ plt.show()

-### **Configuring the Spatial Resolution Enhancement Model** +### **Configuring Model** In **supers2**, you can choose from several types of models to enhance the spatial resolution of Sentinel-2 images. Below are the configurations for each model type and their respective [size options](https://github.com/IPL-UV/supers2/releases/tag/v0.1.0). Each model is configured using `supers2.setmodel`, where the `sr_model_snippet` argument defines the super-resolution model, and `fusionx2_model_snippet` and `fusionx4_model_snippet` correspond to additional fusion models. -### **Available Models:** +## **Available Models:** -#### **1. CNN Models** +### **1. CNN Models** CNN-based models are available in the following sizes: `lightweight`, `small`, `medium`, `expanded`, and `large`. ```python @@ -163,7 +187,13 @@ Model size options (replace `small` with the desired size): - `expanded` - `large` -#### **2. SWIN Models** + +

+ +

+ + +### **2. SWIN Models** SWIN models are optimized for varying levels of detail and offer size options: `lightweight`, `small`, `medium`, and `expanded`. ```python @@ -184,7 +214,13 @@ Available sizes: - `medium` - `expanded` -#### **3. MAMBA Models** + +

+ +

+ + +### **3. MAMBA Models** MAMBA models also come in various sizes, similar to SWIN and CNN: `lightweight`, `small`, `medium`, and `expanded`. ```python @@ -205,8 +241,13 @@ Available sizes: - `medium` - `expanded` +

+ +

+ + -#### **4. Diffusion Model** +### **4. Diffusion Model** The opensrdiffusion model is only available in the `large` size. This model is suited for deep resolution enhancement without additional configurations. ```python @@ -220,30 +261,35 @@ models = supers2.setmodel( ) ``` -#### **5. Simple Models (Bilinear and Bicubic)** +### **5. Simple Models (Bilinear and Bicubic)** For fast interpolation, bilinear and bicubic interpolation models can be used. These models do not require complex configurations and are useful for quick evaluations of enhanced resolution. + ```python from supers2.models.simple import BilinearSR, BicubicSR # Bilinear Interpolation Model bilinear_model = BilinearSR(device=device, scale_factor=4).to(device) -super_bilinear = bilinear_model(X[None]) +super_bilinear = bilinear_model(X[None]).squeeze(0) # Bicubic Interpolation Model bicubic_model = BicubicSR(device=device, scale_factor=4).to(device) -super_bicubic = bicubic_model(X[None]) +super_bicubic = bicubic_model(X[None]).squeeze(0) ``` -### **Apply spatial resolution enhancement** +

+ +

+ +## **Apply spatial resolution enhancement** -### **Predict only RGBNIR bands** 🌍 +## **Predict only RGBNIR bands** 🌍 ```python superX = supers2.predict_rgbnir(X[[2, 1, 0, 6]]) ``` -### **Estimate the uncertainty of the model** 📊 +## **Estimate the uncertainty of the model** 📊 ```python from supers2.trained_models import SRmodels @@ -267,11 +313,11 @@ ax[1].imshow(std_map[0:3].cpu().numpy().transpose(1, 2, 0)*100) ax[1].set_title("Standard Deviation") plt.show() ``` -

- +

+ ### Estimate the Local Attention Map of the model 📊 @@ -296,4 +342,4 @@ plt.show() ``` -### Use the opensr-test and supers2 to analyze the hallucination pixels 📊 +## Use the opensr-test and supers2 to analyze the hallucination pixels 📊 diff --git a/assets/images/bibi.png b/assets/images/bibi.png new file mode 100644 index 0000000000000000000000000000000000000000..650d3d773f3ae806118b1e144ad6f3746fa6b134 GIT binary patch literal 433743 zcmd42WmHvd_ddE2DUmRc1_9|5kPelS1|CR1@d+|Qc zd(QZc|8JZR=gaxvP&aGeYpr?DdClv(=8dnMj2OlPf(I}d3`6{#@Ou~xIS>ZB_Z$@& zd?I)H{1*JhZ6)&2>Vv7Cm93VAE=*d>%FM{r%E&us;}#S}Sj z1dc9Ddi6%cVd9>s9+<-!(IGTg-yisu-DmlZIvkHh(1UpH@bvNhr|2)<$;W-pKz{2c zM~N-+#{T@xI~Ht-hZ~c#dx=MI9hvy*Gna$QOdZp*cZShlvl08C;Y ztG`33Lf-#}e+Z6*6ztvmUw;g~S#^`j8~DF30ItZC|39t#?@f2vH~*J=Lsz){|NO^V zmETQW;nUB_;p*dNR|lQbl^Rz|xSH$p)A$n=#)KvgBk|vT%=R4T;g?JUbZ}A*=%5i;AgPpf^z-Rx7@+=W1?=}mepx}Y8?8G&E?ySM)_Rj~|a(Hd2 z(a67>RFuv_ji?@Nwwawd#R;vxD;I~`c-|hhcD@|kDAPL*E=(qZBLArE%@Xxu)kGvV 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