initital commit

.gitignore

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+/flagged
+/best_model.keras

app.py

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+import gradio as gr
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+
+model_path = "sentinel_classifier_model.keras"
+model = tf.keras.models.load_model(model_path)
+
+
+labels = ['AnnualCrop', 'Forest', 'HerbaceousVegetation', 'Highway', 'Industrial', 'Pasture', 'PermanentCrop', 'Residential', 'River', 'SeaLake']
+    
+def predict_image(image):
+
+    image = Image.fromarray(image.astype('uint8')) 
+    image = image.resize((128, 128)) 
+    image = np.array(image) / 255.0  
+
+  
+    if image.ndim == 2:
+        image = np.stack((image,)*3, axis=-1)
+
+    prediction = model.predict(image[None, ...])
+    confidences = {labels[i]: float(prediction[0][i]) for i in range(len(labels))}
+    return confidences
+
+
+input_image = gr.Image()
+output_text = gr.Textbox(label="Predicted Value")
+
+
+iface = gr.Interface(
+    fn=predict_image,
+    inputs=input_image, 
+    outputs=gr.Label(),
+    title="Sentinel Classifier",
+    examples=["images/forest.jpg", "images/highway.jpg", "images/industrial.jpg", "images/residential.jpg", "images/river.jpg"],
+    description="Upload a satellite image and the classifier will predict what it is."
+)
+
+
+
+iface.launch()

main.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7877\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"http://127.0.0.1:7877/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Min and max values: 0.16862745098039217 1.0\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 836ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "import gradio as gr\n",
+    "import tensorflow as tf\n",
+    "from PIL import Image\n",
+    "import numpy as np\n",
+    "\n",
+    "# Load the model\n",
+    "model_path = \"best_model.keras\"\n",
+    "model = tf.keras.models.load_model(model_path)\n",
+    "\n",
+    "# Define labels\n",
+    "labels = ['AnnualCrop', 'Forest', 'HerbaceousVegetation', 'Highway', 'Industrial', 'Pasture', 'PermanentCrop', 'Residential', 'River', 'SeaLake']\n",
+    "\n",
+    "def predict_image(image):\n",
+    "    image = Image.fromarray(image.astype('uint8'), 'RGB')\n",
+    "    image = image.resize((64, 64))\n",
+    "    image = np.array(image)\n",
+    "\n",
+    "    print(\"Min and max values:\", image.min(), image.max())  # Sollte zwischen 0 und 1 sein\n",
+    "    prediction = model.predict(np.expand_dims(image, axis=0))\n",
+    "    confidences = {labels[i]: float(prediction[0][i]) for i in range(len(labels))}\n",
+    "    return confidences\n",
+    "\n",
+    "\n",
+    "# Gradio interface\n",
+    "iface = gr.Interface(\n",
+    "    fn=predict_image,\n",
+    "    inputs=gr.Image(shape=(128, 128)),\n",
+    "    outputs=gr.Label(num_top_classes=10),\n",
+    "    title=\"Sentinel Image Classifier\",\n",
+    "    description=\"Upload a satellite image and the classifier will predict the type of land cover or feature.\",\n",
+    "    examples=[\"images/forest.jpg\", \"images/highway.jpg\", \"images/industrial.jpg\", \"images/residential.jpg\", \"images/river.jpg\"]\n",
+    ")\n",
+    "\n",
+    "# Launch the interface\n",
+    "iface.launch(share=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 647ms/step\n",
+      "[[2.98899226e-06 3.38417292e-02 1.58750382e-08 1.03646407e-08\n",
+      "  3.04554437e-10 3.97204403e-08 7.68960629e-09 1.02308356e-10\n",
+      "  1.51210475e-06 9.66153681e-01]]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Min and max values: 0.16862745098039217 1.0\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+      "Min and max values: 0.1411764705882353 1.0\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+      "Min and max values: 0.16862745098039217 1.0\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "from PIL import Image\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "# Load the trained model\n",
+    "model_path = 'sentinel_classificatiion_model.keras'  # Adjust the path as necessary\n",
+    "model = tf.keras.models.load_model(model_path)\n",
+    "\n",
+    "# Load and process an example image\n",
+    "image_path = 'images/forest.jpg'  # Replace with an example image from your dataset\n",
+    "image = Image.open(image_path)\n",
+    "\n",
+    "\n",
+    "# Predict using the model\n",
+    "prediction = model.predict(np.expand_dims(image, axis=0))  # Add batch dimension\n",
+    "print(prediction)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

requirements.txt

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+tensorflow
+gradio==3.50
+Pillow