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"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [
{
"file_id": "1vk3FhMHmGl7YxU2A3ALN5sDedH9J1dye",
"timestamp": 1767587949286
}
]
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "JfujIX-kPS6-",
"executionInfo": {
"status": "error",
"timestamp": 1767596401791,
"user_tz": -330,
"elapsed": 121823,
"user": {
"displayName": "Paisa Singh",
"userId": "14121704566639170955"
}
},
"outputId": "20a7e146-90b4-4118-8fa0-9d3b105ee763"
},
"outputs": [
{
"output_type": "error",
"ename": "ValueError",
"evalue": "mount failed",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/tmp/ipython-input-2920689287.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Mount Google Drive to access your data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdrive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdrive\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"✅ Google Drive mounted!\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.12/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36mmount\u001b[0;34m(mountpoint, force_remount, timeout_ms, readonly)\u001b[0m\n\u001b[1;32m 95\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmountpoint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce_remount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m120000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreadonly\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 96\u001b[0m \u001b[0;34m\"\"\"Mount your Google Drive at the specified mountpoint path.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 97\u001b[0;31m return _mount(\n\u001b[0m\u001b[1;32m 98\u001b[0m \u001b[0mmountpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 99\u001b[0m \u001b[0mforce_remount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mforce_remount\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.12/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36m_mount\u001b[0;34m(mountpoint, force_remount, timeout_ms, ephemeral, readonly)\u001b[0m\n\u001b[1;32m 270\u001b[0m \u001b[0;34m'https://research.google.com/colaboratory/faq.html#drive-timeout'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 271\u001b[0m )\n\u001b[0;32m--> 272\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'mount failed'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mextra_reason\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 273\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mcase\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 274\u001b[0m \u001b[0;31m# Terminate the DriveFS binary before killing bash.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: mount failed"
]
}
],
"source": [
"\n",
"# Mount Google Drive to access your data\n",
"from google.colab import drive\n",
"drive.mount('/content/drive')\n",
"print(\" Google Drive mounted!\")\n",
"\n",
"# Install required packages (with compatible versions for Python 3.12)\n",
"!pip install -q --upgrade google-auth-oauthlib\n",
"!pip install -q earthengine-api==0.1.395\n",
"!pip install -q geemap rasterio geopandas torch torchvision matplotlib scikit-learn tqdm\n",
"\n",
"print(\" All packages installed!\")\n"
]
},
{
"cell_type": "code",
"source": [
"\n",
"\n",
"import ee\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"print(\"Authenticating Earth Engine...\")\n",
"print(\" You will see a link - click it, select your Google account, and copy the code.\")\n",
"\n",
"try:\n",
" ee.Authenticate()\n",
" print(\" Authentication successful!\")\n",
"except Exception as e:\n",
" print(f\" Error: {e}\")\n",
" print(\"If you see a URL, click it and complete the authentication.\")\n",
"\n",
"# Initialize with your project ID\n",
"PROJECT_ID = '[REDACTED_FOR_SECURITY]'\n",
"\n",
"try:\n",
" ee.Initialize(project=PROJECT_ID)\n",
" print(f\" Earth Engine initialized with project: {PROJECT_ID}\")\n",
"except Exception as e:\n",
" print(f\" Failed to initialize: {e}\")\n",
" print(\"Make sure you have created the project in Google Cloud Console.\")\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "md2Rxq8bUw-r",
"executionInfo": {
"status": "ok",
"timestamp": 1767586254145,
"user_tz": -330,
"elapsed": 1683,
"user": {
"displayName": "Enigma Cypher",
"userId": "06934947732805796536"
}
},
"outputId": "cced80be-8441-421c-9cad-16ccd0e2d2a2"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Authenticating Earth Engine...\n",
"🔐 You will see a link - click it, select your Google account, and copy the code.\n",
"✅ Authentication successful!\n",
"✅ Earth Engine initialized with project: satmae-2026\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"\n",
"\n",
"import rasterio\n",
"import numpy as np\n",
"import os\n",
"import ee\n",
"import geemap\n",
"from google.colab import drive\n",
"\n",
"# 1. Mount Drive\n",
"drive.mount('/content/drive', force_remount=True)\n",
"\n",
"# 2. Load Mask from Drive\n",
"mask_folder = 'Punjab Wheat Mask_Binary'\n",
"mask_filename = 'Punjab Mask 2024.tif'\n",
"\n",
"print(f\" Searching for '{mask_filename}'...\")\n",
"\n",
"found_path = None\n",
"for root, dirs, files in os.walk('/content/drive/MyDrive/'):\n",
" if mask_filename in files:\n",
" found_path = os.path.join(root, mask_filename)\n",
" print(f\" Found mask at: {found_path}\")\n",
" break\n",
"\n",
"if found_path is None:\n",
" raise FileNotFoundError(\" Could not find 'Punjab Mask 2024.tif'. Make sure you added a shortcut to My Drive!\")\n",
"\n",
"# Read the mask to get metadata (CRS, Transform, Shape)\n",
"with rasterio.open(found_path) as src:\n",
" mask_data = src.read(1) # Read first band\n",
" mask_crs = src.crs\n",
" mask_bounds = src.bounds\n",
" mask_transform = src.transform\n",
" height, width = mask_data.shape\n",
"\n",
" # Normalize mask (0 and 1)\n",
" mask_data = np.where(mask_data > 0, 1, 0)\n",
"\n",
"print(f\" Loaded Mask: {mask_data.shape}\")\n",
"\n",
"# 3. Download Matching Satellite Data from Earth Engine\n",
"print(\"\\n Preparing to download satellite imagery for this mask area...\")\n",
"\n",
"# Initialize EE (Assuming you did Step 0 & 1 successfully)\n",
"try:\n",
" ee.Initialize(project='[REDACTED_FOR_SECURITY]')\n",
"except:\n",
" ee.Authenticate()\n",
" ee.Initialize(project='[REDACTED_FOR_SECURITY]')\n",
"\n",
"# Create ROI from Mask Bounds\n",
"roi_geometry = ee.Geometry.Rectangle([mask_bounds.left, mask_bounds.bottom, mask_bounds.right, mask_bounds.top],\n",
" proj=str(mask_crs),\n",
" geodesic=False)\n",
"\n",
"# --- SATELLITE PROCESSING (Simplified for speed) ---\n",
"START_DATE = '2023-10-15' # Matches \"2024\" wheat season\n",
"END_DATE = '2024-04-15'\n",
"\n",
"print(f\"Fetching Sentinel-2 data ({START_DATE} to {END_DATE})...\")\n",
"\n",
"s2 = (ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED')\n",
" .filterDate(START_DATE, END_DATE)\n",
" .filterBounds(roi_geometry)\n",
" .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20))\n",
" .median() # Simple median composite for \"Toy Model\" speed\n",
" .select(['B2', 'B3', 'B4', 'B8']) # B, G, R, NIR\n",
" .clip(roi_geometry))\n",
"\n",
"# Download Image matching the Mask\n",
"print(\"Downloading image (this matches your mask pixels)...\")\n",
"image_data = geemap.ee_to_numpy(s2, region=roi_geometry, scale=10)\n",
"\n",
"# Check for size mismatch (common issue)\n",
"# Resize image to match mask exactly if slightly off\n",
"if image_data.shape[:2] != mask_data.shape:\n",
" print(f\" Resizing image {image_data.shape[:2]} to match mask {mask_data.shape}...\")\n",
" import cv2\n",
" image_data = cv2.resize(image_data, (width, height), interpolation=cv2.INTER_LINEAR)\n",
"\n",
"print(f\" Loaded Image: {image_data.shape}\")\n",
"\n",
"# Save\n",
"np.save('/content/image_data.npy', image_data)\n",
"np.save('/content/mask_data.npy', mask_data)\n",
"print(\" Data saved! Proceed to Step 2 (Tiling).\")\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 254
},
"id": "fTzgQ71CU-SU",
"executionInfo": {
"status": "error",
"timestamp": 1767586751930,
"user_tz": -330,
"elapsed": 11091,
"user": {
"displayName": "Enigma Cypher",
"userId": "06934947732805796536"
}
},
"outputId": "e2e1edef-fdf4-4645-9c56-9af5d9448bdb"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/drive\n",
"🔍 Searching for 'Punjab Mask 2024.tif'...\n"
]
},
{
"output_type": "error",
"ename": "FileNotFoundError",
"evalue": "❌ Could not find 'Punjab Mask 2024.tif'. Make sure you added a shortcut to My Drive!",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/tmp/ipython-input-2150260376.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfound_path\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mFileNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"❌ Could not find 'Punjab Mask 2024.tif'. Make sure you added a shortcut to My Drive!\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0;31m# Read the mask to get metadata (CRS, Transform, Shape)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: ❌ Could not find 'Punjab Mask 2024.tif'. Make sure you added a shortcut to My Drive!"
]
}
]
},
{
"cell_type": "code",
"source": [
"import os\n",
"from google.colab import drive\n",
"\n",
"# 1. Mount Drive\n",
"print(\"Mounting Drive...\")\n",
"drive.mount('/content/drive', force_remount=True)\n",
"\n",
"# 2. Define target\n",
"target_file = 'Punjab Mask 2024.tif'\n",
"target_folder = 'Punjab Wheat Mask_Binary'\n",
"\n",
"print(f\"\\n SCANNING GOOGLE DRIVE FOR: {target_file}\")\n",
"print(\"This might take 1-2 minutes if you have many files...\")\n",
"print(\"-\" * 50)\n",
"\n",
"found = False\n",
"\n",
"# Walk through the entire drive\n",
"for root, dirs, files in os.walk('/content/drive/MyDrive'):\n",
" # Check if folder matches\n",
" if os.path.basename(root) == target_folder:\n",
" print(f\" Found Folder: {root}\")\n",
"\n",
" # Check if file matches\n",
" if target_file in files:\n",
" full_path = os.path.join(root, target_file)\n",
" print(f\"\\n FOUND IT! \")\n",
" print(f\"Path: {full_path}\")\n",
" print(\"-\" * 50)\n",
" found = True\n",
" break\n",
"\n",
" # Optional: Print every 500 folders scanned to show it's working\n",
" # if len(root) % 500 == 0: print(\".\", end=\"\")\n",
"\n",
"if not found:\n",
" print(\"\\n FILE NOT FOUND.\")\n",
" print(\"Possibilities:\")\n",
" print(\"1. The shortcut hasn't synced yet (Wait 5 min and try again).\")\n",
" print(\"2. It's in 'Shared with me' but NOT added as a shortcut to 'My Drive'.\")\n",
" print(\"3. The filename is slightly different (e.g., 'Punjab_Mask_2024.tif' vs 'Punjab Mask 2024.tif').\")\n",
"\n",
" print(\"\\nListing top-level folders in My Drive for debugging:\")\n",
" print(os.listdir('/content/drive/MyDrive'))\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 288
},
"id": "QhnumBukYUve",
"executionInfo": {
"status": "ok",
"timestamp": 1767586906185,
"user_tz": -330,
"elapsed": 5350,
"user": {
"displayName": "Enigma Cypher",
"userId": "06934947732805796536"
}
},
"outputId": "81b27b55-bf5f-4f50-c893-437e2cd29911"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <style>\n",
" .geemap-dark {\n",
" --jp-widgets-color: white;\n",
" --jp-widgets-label-color: white;\n",
" --jp-ui-font-color1: white;\n",
" --jp-layout-color2: #454545;\n",
" background-color: #383838;\n",
" }\n",
"\n",
" .geemap-dark .jupyter-button {\n",
" --jp-layout-color3: #383838;\n",
" }\n",
"\n",
" .geemap-colab {\n",
" background-color: var(--colab-primary-surface-color, white);\n",
" }\n",
"\n",
" .geemap-colab .jupyter-button {\n",
" --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
" }\n",
" </style>\n",
" "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounting Drive...\n",
"Mounted at /content/drive\n",
"\n",
"🔍 SCANNING GOOGLE DRIVE FOR: Punjab Mask 2024.tif\n",
"This might take 1-2 minutes if you have many files...\n",
"--------------------------------------------------\n",
"\n",
"❌ FILE NOT FOUND.\n",
"Possibilities:\n",
"1. The shortcut hasn't synced yet (Wait 5 min and try again).\n",
"2. It's in 'Shared with me' but NOT added as a shortcut to 'My Drive'.\n",
"3. The filename is slightly different (e.g., 'Punjab_Mask_2024.tif' vs 'Punjab Mask 2024.tif').\n",
"\n",
"Listing top-level folders in My Drive for debugging:\n",
"['Colab Notebooks', 'Classroom', 'ethical hacking', 'ethical map', 'DOSI', 'files_extra', 'gradio_apps_ready.zip', 'Punjab Wheat Mask_Binary']\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import os\n",
"from google.colab import drive\n",
"\n",
"# Mount Drive\n",
"drive.mount('/content/drive', force_remount=True)\n",
"\n",
"# Target folder\n",
"target_folder = '/content/drive/MyDrive/Punjab Wheat Mask_Binary'\n",
"\n",
"print(f\" Scanning folder: {target_folder}\")\n",
"print(\"=\" * 60)\n",
"\n",
"if os.path.exists(target_folder):\n",
" print(f\" Folder EXISTS!\\n\")\n",
"\n",
" # List everything in the folder\n",
" print(\"Contents:\")\n",
" for item in os.listdir(target_folder):\n",
" full_path = os.path.join(target_folder, item)\n",
" if os.path.isfile(full_path):\n",
" size_mb = os.path.getsize(full_path) / (1024 * 1024)\n",
" print(f\" {item} ({size_mb:.2f} MB)\")\n",
" else:\n",
" print(f\" {item}/ (subfolder)\")\n",
"\n",
" # Deep scan for all .tif files\n",
" print(\"\\n Searching for all .tif files recursively:\")\n",
" for root, dirs, files in os.walk(target_folder):\n",
" for file in files:\n",
" if file.lower().endswith('.tif') or file.lower().endswith('.tiff'):\n",
" full_path = os.path.join(root, file)\n",
" rel_path = os.path.relpath(full_path, target_folder)\n",
" print(f\" Found: {rel_path}\")\n",
"else:\n",
" print(f\" Folder NOT FOUND\")\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 324
},
"id": "QCtO0A5YZLNR",
"executionInfo": {
"status": "ok",
"timestamp": 1767586976517,
"user_tz": -330,
"elapsed": 5470,
"user": {
"displayName": "Enigma Cypher",
"userId": "06934947732805796536"
}
},
"outputId": "6c7d672f-d113-4ac3-ea63-265052fa4b7c"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <style>\n",
" .geemap-dark {\n",
" --jp-widgets-color: white;\n",
" --jp-widgets-label-color: white;\n",
" --jp-ui-font-color1: white;\n",
" --jp-layout-color2: #454545;\n",
" background-color: #383838;\n",
" }\n",
"\n",
" .geemap-dark .jupyter-button {\n",
" --jp-layout-color3: #383838;\n",
" }\n",
"\n",
" .geemap-colab {\n",
" background-color: var(--colab-primary-surface-color, white);\n",
" }\n",
"\n",
" .geemap-colab .jupyter-button {\n",
" --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
" }\n",
" </style>\n",
" "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/drive\n",
"📂 Scanning folder: /content/drive/MyDrive/Punjab Wheat Mask_Binary\n",
"============================================================\n",
"✅ Folder EXISTS!\n",
"\n",
"Contents:\n",
" 📄 Punjab Mask 2023.tif (38.87 MB)\n",
" 📄 Punjab Mask 2022.tif (40.88 MB)\n",
" 📄 Punjab Mask 2021.tif (43.26 MB)\n",
" 📄 Punjab Mask 2024.tif (27.84 MB)\n",
" 📄 Readme.gdoc (0.00 MB)\n",
"\n",
"🔍 Searching for all .tif files recursively:\n",
" ✅ Found: Punjab Mask 2023.tif\n",
" ✅ Found: Punjab Mask 2022.tif\n",
" ✅ Found: Punjab Mask 2021.tif\n",
" ✅ Found: Punjab Mask 2024.tif\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"\n",
"import rasterio\n",
"import numpy as np\n",
"import ee\n",
"import geemap\n",
"from google.colab import drive\n",
"\n",
"# 1. Mount Drive\n",
"drive.mount('/content/drive', force_remount=True)\n",
"\n",
"# 2. Load the Mask (Ground Truth)\n",
"mask_path = '/content/drive/MyDrive/Punjab Wheat Mask_Binary/Punjab Mask 2024.tif'\n",
"\n",
"print(f\"Loading mask from: {mask_path}\")\n",
"\n",
"with rasterio.open(mask_path) as src:\n",
" mask_data = src.read(1) # Read the first band\n",
" mask_bounds = src.bounds\n",
" mask_crs = src.crs\n",
" height, width = mask_data.shape\n",
"\n",
" # Normalize mask to 0 and 1 (Binary)\n",
" mask_data = np.where(mask_data > 0, 1, 0)\n",
"\n",
"print(f\" Mask Loaded! Shape: {height}x{width}\")\n",
"print(f\" Bounds: {mask_bounds}\")\n",
"\n",
"# 3. Download Matching Satellite Data from Earth Engine\n",
"print(\"\\n Authenticating Earth Engine...\")\n",
"\n",
"try:\n",
" ee.Initialize(project='[REDACTED_FOR_SECURITY]')\n",
"except:\n",
" ee.Authenticate()\n",
" ee.Initialize(project='[REDACTED_FOR_SECURITY]')\n",
"\n",
"# Define the region of interest (ROI) based on your mask's location\n",
"roi = ee.Geometry.Rectangle(\n",
" [mask_bounds.left, mask_bounds.bottom, mask_bounds.right, mask_bounds.top],\n",
" proj=str(mask_crs),\n",
" geodesic=False\n",
")\n",
"\n",
"# Define Date Range for \"Punjab Mask 2024\" (Wheat Season: Oct 2023 - Apr 2024)\n",
"START_DATE = '2023-10-15'\n",
"END_DATE = '2024-04-15'\n",
"\n",
"print(f\"Downloading Sentinel-2 data ({START_DATE} to {END_DATE})...\")\n",
"print(\"This might take 1-2 minutes...\")\n",
"\n",
"# Create a clean composite image (Median of the whole season)\n",
"# We use bands B2(Blue), B3(Green), B4(Red), B8(NIR)\n",
"s2_img = (ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED')\n",
" .filterBounds(roi)\n",
" .filterDate(START_DATE, END_DATE)\n",
" .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20))\n",
" .median()\n",
" .select(['B2', 'B3', 'B4', 'B8'])\n",
" .clip(roi))\n",
"\n",
"# Download and reshape to match mask EXACTLY\n",
"# We use 'shape' argument to force it to match your mask dimensions\n",
"try:\n",
" image_data = geemap.ee_to_numpy(s2_img, region=roi, shape=(height, width))\n",
"\n",
" # Check if we got data\n",
" if image_data is None:\n",
" raise ValueError(\"Download failed (returned None).\")\n",
"\n",
" print(f\" Satellite Image Downloaded! Shape: {image_data.shape}\")\n",
"\n",
" # 4. Save for the next step\n",
" np.save('/content/image_data.npy', image_data)\n",
" np.save('/content/mask_data.npy', mask_data)\n",
" print(\"\\n SUCCESS! Data is ready.\")\n",
" print(\" Now go to 'STEP 2: PREPARE TRAINING DATA' in the guide.\")\n",
"\n",
"except Exception as e:\n",
" print(f\"\\n Error downloading from Earth Engine: {e}\")\n",
" print(\"Try reducing the size of your mask if it's too large, or check your internet.\")\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 216
},
"id": "IVgqs55OZapO",
"executionInfo": {
"status": "ok",
"timestamp": 1767587062210,
"user_tz": -330,
"elapsed": 27866,
"user": {
"displayName": "Enigma Cypher",
"userId": "06934947732805796536"
}
},
"outputId": "f375a3b1-d1ff-41e4-f40a-70970b2c88de"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <style>\n",
" .geemap-dark {\n",
" --jp-widgets-color: white;\n",
" --jp-widgets-label-color: white;\n",
" --jp-ui-font-color1: white;\n",
" --jp-layout-color2: #454545;\n",
" background-color: #383838;\n",
" }\n",
"\n",
" .geemap-dark .jupyter-button {\n",
" --jp-layout-color3: #383838;\n",
" }\n",
"\n",
" .geemap-colab {\n",
" background-color: var(--colab-primary-surface-color, white);\n",
" }\n",
"\n",
" .geemap-colab .jupyter-button {\n",
" --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
" }\n",
" </style>\n",
" "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/drive\n",
"Loading mask from: /content/drive/MyDrive/Punjab Wheat Mask_Binary/Punjab Mask 2024.tif\n",
"✅ Mask Loaded! Shape: 33067x34086\n",
" Bounds: BoundingBox(left=73.87960492267133, bottom=29.541726938969347, right=76.94160240012114, top=32.51218608896737)\n",
"\n",
"⏳ Authenticating Earth Engine...\n",
"Downloading Sentinel-2 data (2023-10-15 to 2024-04-15)...\n",
"This might take 1-2 minutes...\n",
"\n",
"❌ Error downloading from Earth Engine: Invalid JSON payload received. Unknown name \"shape\": Cannot find field.\n",
"Try reducing the size of your mask if it's too large, or check your internet.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"\n",
"\n",
"import rasterio\n",
"from rasterio.windows import from_bounds\n",
"import numpy as np\n",
"import ee\n",
"import geemap\n",
"from google.colab import drive\n",
"\n",
"# 1. Mount Drive\n",
"drive.mount('/content/drive', force_remount=True)\n",
"\n",
"# 2. Setup Paths & Authenticate\n",
"mask_path = '/content/drive/MyDrive/Punjab Wheat Mask_Binary/Punjab Mask 2024.tif'\n",
"\n",
"try:\n",
" ee.Initialize(project='[REDACTED_FOR_SECURITY]')\n",
"except:\n",
" ee.Authenticate()\n",
" ee.Initialize(project='[REDACTED_FOR_SECURITY]')\n",
"\n",
"# 3. Define a Manageable Sample Area (Center of the Map)\n",
"# We will grab a 20km x 20km box in the center to test the pipeline\n",
"with rasterio.open(mask_path) as src:\n",
" full_bounds = src.bounds\n",
" crs = src.crs\n",
"\n",
" # Calculate center\n",
" center_x = (full_bounds.left + full_bounds.right) / 2\n",
" center_y = (full_bounds.bottom + full_bounds.top) / 2\n",
"\n",
" # Define a 0.2 degree box (approx 20km x 20km) around the center\n",
" # This creates a ~2000x2000 pixel image (manageable for Colab)\n",
" half_size = 0.1\n",
" sample_bounds = (\n",
" center_x - half_size, # left\n",
" center_y - half_size, # bottom\n",
" center_x + half_size, # right\n",
" center_y + half_size # top\n",
" )\n",
"\n",
" print(f\" Selecting sample area around: {center_y:.4f}, {center_x:.4f}\")\n",
"\n",
" # Read ONLY the mask pixels for this sample window\n",
" window = from_bounds(*sample_bounds, transform=src.transform)\n",
" mask_data = src.read(1, window=window)\n",
"\n",
" # Normalize Mask\n",
" mask_data = np.where(mask_data > 0, 1, 0)\n",
"\n",
"print(f\" Mask Sample Loaded! Shape: {mask_data.shape}\")\n",
"\n",
"# 4. Download Matching Satellite Data from Earth Engine\n",
"# Create EE Geometry for the sample bounds\n",
"roi = ee.Geometry.Rectangle(\n",
" [sample_bounds[0], sample_bounds[1], sample_bounds[2], sample_bounds[3]],\n",
" proj=str(crs),\n",
" geodesic=False\n",
")\n",
"\n",
"START_DATE = '2023-10-15'\n",
"END_DATE = '2024-04-15'\n",
"\n",
"print(f\" Downloading Sentinel-2 sample ({START_DATE} to {END_DATE})...\")\n",
"\n",
"s2_img = (ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED')\n",
" .filterBounds(roi)\n",
" .filterDate(START_DATE, END_DATE)\n",
" .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20))\n",
" .median()\n",
" .select(['B2', 'B3', 'B4', 'B8']) # Blue, Green, Red, NIR\n",
" .clip(roi))\n",
"\n",
"# Download with specific scale (10m) to match Sentinel-2 resolution\n",
"image_data = geemap.ee_to_numpy(s2_img, region=roi, scale=10)\n",
"\n",
"# 5. Handle Slight Shape Mismatch (Common with coordinate projections)\n",
"# Resize satellite image to match mask exactly\n",
"if image_data.shape[:2] != mask_data.shape:\n",
" import cv2\n",
" print(f\" Adjusting size: Image {image_data.shape[:2]} vs Mask {mask_data.shape}\")\n",
" # Resize image to match mask (using linear interpolation)\n",
" image_data = cv2.resize(image_data, (mask_data.shape[1], mask_data.shape[0]), interpolation=cv2.INTER_LINEAR)\n",
"\n",
"print(f\" Satellite Data Downloaded! Shape: {image_data.shape}\")\n",
"\n",
"# 6. Save\n",
"np.save('/content/image_data.npy', image_data)\n",
"np.save('/content/mask_data.npy', mask_data)\n",
"print(\"\\n SUCCESS! Sample data is ready.\")\n",
"print(\" Proceed to STEP 2 (Tiling) in the guide.\")\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 621
},
"id": "RRVORM4XZvRr",
"executionInfo": {
"status": "error",
"timestamp": 1767587125473,
"user_tz": -330,
"elapsed": 6987,
"user": {
"displayName": "Enigma Cypher",
"userId": "06934947732805796536"
}
},
"outputId": "d4e64662-f354-4e31-d1b0-cf2f96870142"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <style>\n",
" .geemap-dark {\n",
" --jp-widgets-color: white;\n",
" --jp-widgets-label-color: white;\n",
" --jp-ui-font-color1: white;\n",
" --jp-layout-color2: #454545;\n",
" background-color: #383838;\n",
" }\n",
"\n",
" .geemap-dark .jupyter-button {\n",
" --jp-layout-color3: #383838;\n",
" }\n",
"\n",
" .geemap-colab {\n",
" background-color: var(--colab-primary-surface-color, white);\n",
" }\n",
"\n",
" .geemap-colab .jupyter-button {\n",
" --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
" }\n",
" </style>\n",
" "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/drive\n",
"📍 Selecting sample area around: 31.0270, 75.4106\n",
"✅ Mask Sample Loaded! Shape: (2226, 2226)\n",
"⏳ Downloading Sentinel-2 sample (2023-10-15 to 2024-04-15)...\n"
]
},
{
"output_type": "error",
"ename": "Exception",
"evalue": "Total request size (178623216 bytes) must be less than or equal to 50331648 bytes.",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mHttpError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/local/lib/python3.12/dist-packages/ee/data.py\u001b[0m in \u001b[0;36m_execute_cloud_call\u001b[0;34m(call, num_retries)\u001b[0m\n\u001b[1;32m 348\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 349\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mcall\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_retries\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_retries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 350\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mgoogleapiclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHttpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.12/dist-packages/googleapiclient/_helpers.py\u001b[0m in \u001b[0;36mpositional_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 129\u001b[0m \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 130\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 131\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.12/dist-packages/googleapiclient/http.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, http, num_retries)\u001b[0m\n\u001b[1;32m 937\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m300\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 938\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mHttpError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muri\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muri\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 939\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpostproc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mHttpError\u001b[0m: <HttpError 400 when requesting https://earthengine.googleapis.com/v1/projects/satmae-2026/image:computePixels? returned \"Total request size (178623216 bytes) must be less than or equal to 50331648 bytes.\". Details: \"Total request size (178623216 bytes) must be less than or equal to 50331648 bytes.\">",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mEEException\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/local/lib/python3.12/dist-packages/geemap/common.py\u001b[0m in \u001b[0;36mee_to_numpy\u001b[0;34m(ee_object, region, scale, bands, **kwargs)\u001b[0m\n\u001b[1;32m 3133\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3134\u001b[0;31m \u001b[0mstruct_array\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mee\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomputePixels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3135\u001b[0m \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstruct_array\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mband\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mband\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mstruct_array\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.12/dist-packages/ee/data.py\u001b[0m in \u001b[0;36mcomputePixels\u001b[0;34m(params)\u001b[0m\n\u001b[1;32m 889\u001b[0m \u001b[0m_maybe_populate_workload_tag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 890\u001b[0;31m data = _execute_cloud_call(\n\u001b[0m\u001b[1;32m 891\u001b[0m \u001b[0m_get_cloud_projects_raw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.12/dist-packages/ee/data.py\u001b[0m in \u001b[0;36m_execute_cloud_call\u001b[0;34m(call, num_retries)\u001b[0m\n\u001b[1;32m 350\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mgoogleapiclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHttpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 351\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0m_translate_cloud_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# pylint: disable=raise-missing-from\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 352\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mEEException\u001b[0m: Total request size (178623216 bytes) must be less than or equal to 50331648 bytes.",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mException\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/tmp/ipython-input-474270229.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 76\u001b[0m \u001b[0;31m# Download with specific scale (10m) to match Sentinel-2 resolution\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 77\u001b[0;31m \u001b[0mimage_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgeemap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mee_to_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms2_img\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mregion\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mroi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 78\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[0;31m# 5. Handle Slight Shape Mismatch (Common with coordinate projections)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.12/dist-packages/geemap/common.py\u001b[0m in \u001b[0;36mee_to_numpy\u001b[0;34m(ee_object, region, scale, bands, **kwargs)\u001b[0m\n\u001b[1;32m 3136\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3137\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3138\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3139\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mException\u001b[0m: Total request size (178623216 bytes) must be less than or equal to 50331648 bytes."
]
}
]
},
{
"cell_type": "code",
"source": [
"\n",
"\n",
"import rasterio\n",
"from rasterio.windows import from_bounds\n",
"import numpy as np\n",
"import ee\n",
"import geemap\n",
"import cv2\n",
"from google.colab import drive\n",
"\n",
"# 1. Mount Drive\n",
"drive.mount('/content/drive', force_remount=True)\n",
"\n",
"# 2. Setup\n",
"mask_path = '/content/drive/MyDrive/Punjab Wheat Mask_Binary/Punjab Mask 2024.tif'\n",
"\n",
"try:\n",
" ee.Initialize(project='[REDACTED_FOR_SECURITY]')\n",
"except:\n",
" ee.Authenticate()\n",
" ee.Initialize(project='[REDACTED_FOR_SECURITY]')\n",
"\n",
"# 3. Define a SMALL Sample Area (5km x 5km) to avoid limits\n",
"with rasterio.open(mask_path) as src:\n",
" full_bounds = src.bounds\n",
" crs = src.crs\n",
"\n",
" center_x = (full_bounds.left + full_bounds.right) / 2\n",
" center_y = (full_bounds.bottom + full_bounds.top) / 2\n",
"\n",
" # 0.04 degrees is approx 4-5 km\n",
" half_size = 0.04\n",
" sample_bounds = (\n",
" center_x - half_size,\n",
" center_y - half_size,\n",
" center_x + half_size,\n",
" center_y + half_size\n",
" )\n",
"\n",
" print(f\" Selecting small sample area (5km x 5km)...\")\n",
"\n",
" window = from_bounds(*sample_bounds, transform=src.transform)\n",
" mask_data = src.read(1, window=window)\n",
" mask_data = np.where(mask_data > 0, 1, 0)\n",
"\n",
"print(f\" Mask Sample Loaded! Shape: {mask_data.shape}\")\n",
"\n",
"# 4. Download Matching Satellite Data\n",
"roi = ee.Geometry.Rectangle(\n",
" [sample_bounds[0], sample_bounds[1], sample_bounds[2], sample_bounds[3]],\n",
" proj=str(crs),\n",
" geodesic=False\n",
")\n",
"\n",
"START_DATE = '2023-11-01'\n",
"END_DATE = '2024-03-30'\n",
"\n",
"print(f\" Downloading Sentinel-2 data (small patch)...\")\n",
"\n",
"s2_img = (ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED')\n",
" .filterBounds(roi)\n",
" .filterDate(START_DATE, END_DATE)\n",
" .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20))\n",
" .median()\n",
" .select(['B2', 'B3', 'B4', 'B8']) # B, G, R, NIR\n",
" .clip(roi))\n",
"\n",
"# Download with 10m scale\n",
"# Using default_value=0 to handle empty pixels\n",
"image_data = geemap.ee_to_numpy(s2_img, region=roi, scale=10, default_value=0)\n",
"\n",
"# 5. Fix Shape Mismatch\n",
"# Force image to match mask size exactly\n",
"if image_data.shape[:2] != mask_data.shape:\n",
" print(f\" Resize: {image_data.shape[:2]} -> {mask_data.shape}\")\n",
" image_data = cv2.resize(image_data, (mask_data.shape[1], mask_data.shape[0]), interpolation=cv2.INTER_LINEAR)\n",
"\n",
"print(f\" Satellite Data Downloaded! Shape: {image_data.shape}\")\n",
"\n",
"# 6. Save\n",
"np.save('/content/image_data.npy', image_data)\n",
"np.save('/content/mask_data.npy', mask_data)\n",
"print(\"\\n SUCCESS! Small sample dataset created.\")\n",
"print(\" Proceed to STEP 2 (Tiling).\")\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 621
},
"id": "Tb9Y_wBjZ6aw",
"executionInfo": {
"status": "error",
"timestamp": 1767587170756,
"user_tz": -330,
"elapsed": 6967,
"user": {
"displayName": "Enigma Cypher",
"userId": "06934947732805796536"
}
},
"outputId": "ba6850d1-d40d-43c7-ae5d-4fbb945017d5"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <style>\n",
" .geemap-dark {\n",
" --jp-widgets-color: white;\n",
" --jp-widgets-label-color: white;\n",
" --jp-ui-font-color1: white;\n",
" --jp-layout-color2: #454545;\n",
" background-color: #383838;\n",
" }\n",
"\n",
" .geemap-dark .jupyter-button {\n",
" --jp-layout-color3: #383838;\n",
" }\n",
"\n",
" .geemap-colab {\n",
" background-color: var(--colab-primary-surface-color, white);\n",
" }\n",
"\n",
" .geemap-colab .jupyter-button {\n",
" --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
" }\n",
" </style>\n",
" "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/drive\n",
"📍 Selecting small sample area (5km x 5km)...\n",
"✅ Mask Sample Loaded! Shape: (891, 891)\n",
"⏳ Downloading Sentinel-2 data (small patch)...\n"
]
},
{
"output_type": "error",
"ename": "Exception",
"evalue": "Invalid JSON payload received. Unknown name \"default_value\": Cannot find field.",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mHttpError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/local/lib/python3.12/dist-packages/ee/data.py\u001b[0m in \u001b[0;36m_execute_cloud_call\u001b[0;34m(call, num_retries)\u001b[0m\n\u001b[1;32m 348\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 349\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mcall\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_retries\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_retries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 350\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mgoogleapiclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHttpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.12/dist-packages/googleapiclient/_helpers.py\u001b[0m in \u001b[0;36mpositional_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 129\u001b[0m \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 130\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 131\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.12/dist-packages/googleapiclient/http.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, http, num_retries)\u001b[0m\n\u001b[1;32m 937\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m300\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 938\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mHttpError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muri\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muri\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 939\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpostproc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mHttpError\u001b[0m: <HttpError 400 when requesting https://earthengine.googleapis.com/v1/projects/satmae-2026/image:computePixels? returned \"Invalid JSON payload received. Unknown name \"default_value\": Cannot find field.\". Details: \"[{'@type': 'type.googleapis.com/google.rpc.BadRequest', 'fieldViolations': [{'description': 'Invalid JSON payload received. Unknown name \"default_value\": Cannot find field.'}]}]\">",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mEEException\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/local/lib/python3.12/dist-packages/geemap/common.py\u001b[0m in \u001b[0;36mee_to_numpy\u001b[0;34m(ee_object, region, scale, bands, **kwargs)\u001b[0m\n\u001b[1;32m 3133\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3134\u001b[0;31m \u001b[0mstruct_array\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mee\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomputePixels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3135\u001b[0m \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstruct_array\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mband\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mband\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mstruct_array\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.12/dist-packages/ee/data.py\u001b[0m in \u001b[0;36mcomputePixels\u001b[0;34m(params)\u001b[0m\n\u001b[1;32m 889\u001b[0m \u001b[0m_maybe_populate_workload_tag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 890\u001b[0;31m data = _execute_cloud_call(\n\u001b[0m\u001b[1;32m 891\u001b[0m \u001b[0m_get_cloud_projects_raw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.12/dist-packages/ee/data.py\u001b[0m in \u001b[0;36m_execute_cloud_call\u001b[0;34m(call, num_retries)\u001b[0m\n\u001b[1;32m 350\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mgoogleapiclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHttpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 351\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0m_translate_cloud_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# pylint: disable=raise-missing-from\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 352\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mEEException\u001b[0m: Invalid JSON payload received. Unknown name \"default_value\": Cannot find field.",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mException\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/tmp/ipython-input-4091036830.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[0;31m# Download with 10m scale\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[0;31m# Using default_value=0 to handle empty pixels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m \u001b[0mimage_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgeemap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mee_to_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms2_img\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mregion\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mroi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdefault_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0;31m# 5. Fix Shape Mismatch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.12/dist-packages/geemap/common.py\u001b[0m in \u001b[0;36mee_to_numpy\u001b[0;34m(ee_object, region, scale, bands, **kwargs)\u001b[0m\n\u001b[1;32m 3136\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3137\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3138\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3139\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mException\u001b[0m: Invalid JSON payload received. Unknown name \"default_value\": Cannot find field."
]
}
]
},
{
"cell_type": "code",
"source": [
"\n",
"\n",
"import rasterio\n",
"from rasterio.windows import from_bounds\n",
"import numpy as np\n",
"import ee\n",
"import geemap\n",
"import cv2\n",
"from google.colab import drive\n",
"\n",
"# 1. Mount Drive\n",
"drive.mount('/content/drive', force_remount=True)\n",
"\n",
"# 2. Setup\n",
"mask_path = '/content/drive/MyDrive/Punjab Wheat Mask_Binary/Punjab Mask 2024.tif'\n",
"\n",
"try:\n",
" ee.Initialize(project='[REDACTED_FOR_SECURITY]')\n",
"except:\n",
" ee.Authenticate()\n",
" ee.Initialize(project='[REDACTED_FOR_SECURITY]')\n",
"\n",
"# 3. Define a SMALL Sample Area (5km x 5km) to avoid limits\n",
"with rasterio.open(mask_path) as src:\n",
" full_bounds = src.bounds\n",
" crs = src.crs\n",
"\n",
" center_x = (full_bounds.left + full_bounds.right) / 2\n",
" center_y = (full_bounds.bottom + full_bounds.top) / 2\n",
"\n",
" # 0.04 degrees is approx 4-5 km\n",
" half_size = 0.04\n",
" sample_bounds = (\n",
" center_x - half_size,\n",
" center_y - half_size,\n",
" center_x + half_size,\n",
" center_y + half_size\n",
" )\n",
"\n",
" print(f\" Selecting small sample area (5km x 5km)...\")\n",
"\n",
" window = from_bounds(*sample_bounds, transform=src.transform)\n",
" mask_data = src.read(1, window=window)\n",
" mask_data = np.where(mask_data > 0, 1, 0)\n",
"\n",
"print(f\" Mask Sample Loaded! Shape: {mask_data.shape}\")\n",
"\n",
"# 4. Download Matching Satellite Data\n",
"roi = ee.Geometry.Rectangle(\n",
" [sample_bounds[0], sample_bounds[1], sample_bounds[2], sample_bounds[3]],\n",
" proj=str(crs),\n",
" geodesic=False\n",
")\n",
"\n",
"START_DATE = '2023-11-01'\n",
"END_DATE = '2024-03-30'\n",
"\n",
"print(f\" Downloading Sentinel-2 data (small patch)...\")\n",
"\n",
"s2_img = (ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED')\n",
" .filterBounds(roi)\n",
" .filterDate(START_DATE, END_DATE)\n",
" .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20))\n",
" .median()\n",
" .select(['B2', 'B3', 'B4', 'B8']) # B, G, R, NIR\n",
" .clip(roi))\n",
"\n",
"# Download with 10m scale (NO default_value parameter - it's not supported)\n",
"image_data = geemap.ee_to_numpy(s2_img, region=roi, scale=10)\n",
"\n",
"# 5. Fix Shape Mismatch\n",
"# Force image to match mask size exactly\n",
"if image_data.shape[:2] != mask_data.shape:\n",
" print(f\" Resize: {image_data.shape[:2]} -> {mask_data.shape}\")\n",
" image_data = cv2.resize(image_data, (mask_data.shape[1], mask_data.shape[0]), interpolation=cv2.INTER_LINEAR)\n",
"\n",
"print(f\" Satellite Data Downloaded! Shape: {image_data.shape}\")\n",
"\n",
"# 6. Normalize (scale to 0-1)\n",
"image_data = np.clip(image_data / 3000, 0, 1) # Sentinel-2 values ~0-3000\n",
"\n",
"# 7. Save\n",
"np.save('/content/image_data.npy', image_data)\n",
"np.save('/content/mask_data.npy', mask_data)\n",
"print(\"\\n SUCCESS! Small sample dataset created.\")\n",
"print(\" Proceed to STEP 2 (Tiling).\")\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 180
},
"id": "0rtm-qM8aHGt",
"executionInfo": {
"status": "ok",
"timestamp": 1767587230122,
"user_tz": -330,
"elapsed": 14504,
"user": {
"displayName": "Enigma Cypher",
"userId": "06934947732805796536"
}
},
"outputId": "16239f22-9baa-43a3-8e6a-cfb60105b6a0"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <style>\n",
" .geemap-dark {\n",
" --jp-widgets-color: white;\n",
" --jp-widgets-label-color: white;\n",
" --jp-ui-font-color1: white;\n",
" --jp-layout-color2: #454545;\n",
" background-color: #383838;\n",
" }\n",
"\n",
" .geemap-dark .jupyter-button {\n",
" --jp-layout-color3: #383838;\n",
" }\n",
"\n",
" .geemap-colab {\n",
" background-color: var(--colab-primary-surface-color, white);\n",
" }\n",
"\n",
" .geemap-colab .jupyter-button {\n",
" --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
" }\n",
" </style>\n",
" "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/drive\n",
"📍 Selecting small sample area (5km x 5km)...\n",
"✅ Mask Sample Loaded! Shape: (891, 891)\n",
"⏳ Downloading Sentinel-2 data (small patch)...\n",
"⚠️ Resize: (891, 892) -> (891, 891)\n",
"✅ Satellite Data Downloaded! Shape: (891, 891, 4)\n",
"\n",
"🎉 SUCCESS! Small sample dataset created.\n",
"👉 Proceed to STEP 2 (Tiling).\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"import torch\n",
"from torch.utils.data import DataLoader, TensorDataset, random_split\n",
"import matplotlib.pyplot as plt\n",
"\n",
"print(\"Loading data...\")\n",
"\n",
"image_data = np.load('/content/image_data.npy')\n",
"mask_data = np.load('/content/mask_data.npy')\n",
"\n",
"print(f\"Image shape: {image_data.shape}\")\n",
"print(f\"Mask shape: {mask_data.shape}\")\n",
"\n",
"image_data = np.clip(image_data, 0, 1)\n",
"image_data = np.nan_to_num(image_data, nan=0)\n",
"\n",
"mask_data = np.clip(mask_data, 0, 1)\n",
"mask_data = np.nan_to_num(mask_data, nan=0)\n",
"\n",
"print(\"Data normalized\")\n",
"\n",
"def create_patches(image, mask, patch_size=64, stride=64):\n",
" height, width = image.shape[:2]\n",
" patches_img = []\n",
" patches_mask = []\n",
"\n",
" for y in range(0, height - patch_size + 1, stride):\n",
" for x in range(0, width - patch_size + 1, stride):\n",
" img_patch = image[y:y+patch_size, x:x+patch_size, :]\n",
" mask_patch = mask[y:y+patch_size, x:x+patch_size]\n",
"\n",
" if np.mean(img_patch) > 0.01:\n",
" patches_img.append(img_patch)\n",
" patches_mask.append(mask_patch)\n",
"\n",
" X = np.array(patches_img)\n",
" y = np.array(patches_mask)\n",
"\n",
" X = np.transpose(X, (0, 3, 1, 2))\n",
" y = np.expand_dims(y, axis=1)\n",
"\n",
" return torch.tensor(X, dtype=torch.float32), torch.tensor(y, dtype=torch.float32)\n",
"\n",
"print(\"Creating patches...\")\n",
"X_data, y_data = create_patches(image_data, mask_data, patch_size=64, stride=64)\n",
"\n",
"print(f\"Created {X_data.shape[0]} patches\")\n",
"print(f\"X shape: {X_data.shape}\")\n",
"print(f\"y shape: {y_data.shape}\")\n",
"\n",
"print(\"Splitting data...\")\n",
"\n",
"train_size = int(0.7 * len(X_data))\n",
"val_size = int(0.15 * len(X_data))\n",
"test_size = len(X_data) - train_size - val_size\n",
"\n",
"train_dataset, val_dataset, test_dataset = random_split(\n",
" TensorDataset(X_data, y_data),\n",
" [train_size, val_size, test_size]\n",
")\n",
"\n",
"batch_size = 8\n",
"\n",
"train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
"val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)\n",
"test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)\n",
"\n",
"print(f\"Train: {len(train_dataset)} samples\")\n",
"print(f\"Val: {len(val_dataset)} samples\")\n",
"print(f\"Test: {len(test_dataset)} samples\")\n",
"\n",
"fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
"\n",
"X_batch, y_batch = next(iter(train_loader))\n",
"\n",
"rgb = X_batch[0, [2, 1, 0], :, :].numpy()\n",
"rgb = np.transpose(rgb, (1, 2, 0))\n",
"rgb = np.clip(rgb, 0, 1)\n",
"\n",
"mask = y_batch[0, 0, :, :].numpy()\n",
"\n",
"axes[0].imshow(rgb)\n",
"axes[0].set_title('RGB Composite')\n",
"axes[0].axis('off')\n",
"\n",
"axes[1].imshow(mask, cmap='gray')\n",
"axes[1].set_title('Wheat Mask')\n",
"axes[1].axis('off')\n",
"\n",
"axes[2].imshow(rgb)\n",
"axes[2].imshow(mask, cmap='RdYlGn', alpha=0.4)\n",
"axes[2].set_title('Overlay')\n",
"axes[2].axis('off')\n",
"\n",
"plt.tight_layout()\n",
"plt.savefig('/content/sample_patch.png', dpi=100)\n",
"plt.show()\n",
"\n",
"print(\"Sample visualization saved\")\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 657
},
"id": "zLyqQCVjakNv",
"executionInfo": {
"status": "ok",
"timestamp": 1767587363332,
"user_tz": -330,
"elapsed": 5888,
"user": {
"displayName": "Enigma Cypher",
"userId": "06934947732805796536"
}
},
"outputId": "523d7dc3-df82-4b00-b67f-60f514718229"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <style>\n",
" .geemap-dark {\n",
" --jp-widgets-color: white;\n",
" --jp-widgets-label-color: white;\n",
" --jp-ui-font-color1: white;\n",
" --jp-layout-color2: #454545;\n",
" background-color: #383838;\n",
" }\n",
"\n",
" .geemap-dark .jupyter-button {\n",
" --jp-layout-color3: #383838;\n",
" }\n",
"\n",
" .geemap-colab {\n",
" background-color: var(--colab-primary-surface-color, white);\n",
" }\n",
"\n",
" .geemap-colab .jupyter-button {\n",
" --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
" }\n",
" </style>\n",
" "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Loading data...\n",
"Image shape: (891, 891, 4)\n",
"Mask shape: (891, 891)\n",
"Data normalized\n",
"Creating patches...\n",
"Created 169 patches\n",
"X shape: torch.Size([169, 4, 64, 64])\n",
"y shape: torch.Size([169, 1, 64, 64])\n",
"Splitting data...\n",
"Train: 118 samples\n",
"Val: 25 samples\n",
"Test: 26 samples\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1500x500 with 3 Axes>"
],
"image/png": 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},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Sample visualization saved\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import torch\n",
"import torch.nn as nn\n",
"\n",
"device = torch.device('cpu')\n",
"print(f\"Device: {device}\")\n",
"\n",
"class LightweightEncoder(nn.Module):\n",
" def __init__(self, in_channels=4, embed_dim=256, depth=4, num_heads=4, patch_size=16):\n",
" super().__init__()\n",
" self.patch_size = patch_size\n",
" self.embed_dim = embed_dim\n",
"\n",
" self.patch_embed = nn.Conv2d(\n",
" in_channels, embed_dim,\n",
" kernel_size=patch_size, stride=patch_size\n",
" )\n",
"\n",
" num_patches = (64 // patch_size) ** 2\n",
" self.pos_embed = nn.Parameter(torch.randn(1, num_patches + 1, embed_dim) * 0.02)\n",
" self.cls_token = nn.Parameter(torch.randn(1, 1, embed_dim) * 0.02)\n",
"\n",
" encoder_layer = nn.TransformerEncoderLayer(\n",
" d_model=embed_dim,\n",
" nhead=num_heads,\n",
" dim_feedforward=embed_dim * 2,\n",
" batch_first=True,\n",
" dropout=0.1,\n",
" activation='gelu'\n",
" )\n",
" self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=depth)\n",
" self.norm = nn.LayerNorm(embed_dim)\n",
"\n",
" def forward(self, x):\n",
" B = x.shape[0]\n",
"\n",
" x = self.patch_embed(x)\n",
" x = x.flatten(2).transpose(1, 2)\n",
"\n",
" cls_tokens = self.cls_token.expand(B, -1, -1)\n",
" x = torch.cat([cls_tokens, x], dim=1)\n",
"\n",
" x = x + self.pos_embed\n",
"\n",
" x = self.transformer(x)\n",
" x = self.norm(x)\n",
"\n",
" return x\n",
"\n",
"class LightweightDecoder(nn.Module):\n",
" def __init__(self, embed_dim=256, num_classes=2):\n",
" super().__init__()\n",
"\n",
" self.conv1 = nn.Sequential(\n",
" nn.Conv2d(embed_dim, 128, kernel_size=3, padding=1),\n",
" nn.BatchNorm2d(128),\n",
" nn.ReLU(inplace=True),\n",
" )\n",
"\n",
" self.up1 = nn.Sequential(\n",
" nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1),\n",
" nn.BatchNorm2d(64),\n",
" nn.ReLU(inplace=True),\n",
" )\n",
"\n",
" self.up2 = nn.Sequential(\n",
" nn.ConvTranspose2d(64, 32, kernel_size=4, stride=2, padding=1),\n",
" nn.BatchNorm2d(32),\n",
" nn.ReLU(inplace=True),\n",
" )\n",
"\n",
" self.up3 = nn.Sequential(\n",
" nn.ConvTranspose2d(32, 16, kernel_size=4, stride=2, padding=1),\n",
" nn.BatchNorm2d(16),\n",
" nn.ReLU(inplace=True),\n",
" )\n",
"\n",
" self.up4 = nn.Sequential(\n",
" nn.ConvTranspose2d(16, 8, kernel_size=4, stride=2, padding=1),\n",
" nn.BatchNorm2d(8),\n",
" nn.ReLU(inplace=True),\n",
" )\n",
"\n",
" self.final = nn.Conv2d(8, num_classes, kernel_size=1)\n",
"\n",
" def forward(self, x):\n",
" B, N, C = x.shape\n",
"\n",
" x = x[:, 1:, :]\n",
" x = x.transpose(1, 2).reshape(B, C, 4, 4)\n",
"\n",
" x = self.conv1(x)\n",
" x = self.up1(x)\n",
" x = self.up2(x)\n",
" x = self.up3(x)\n",
" x = self.up4(x)\n",
" x = self.final(x)\n",
"\n",
" return x\n",
"\n",
"class CropSegmentationModel(nn.Module):\n",
" def __init__(self, in_channels=4, embed_dim=256, depth=4, num_heads=4, num_classes=2):\n",
" super().__init__()\n",
" self.encoder = LightweightEncoder(in_channels, embed_dim, depth, num_heads)\n",
" self.decoder = LightweightDecoder(embed_dim, num_classes)\n",
"\n",
" def forward(self, x):\n",
" features = self.encoder(x)\n",
" segmentation = self.decoder(features)\n",
" return segmentation\n",
"\n",
"num_channels = X_data.shape[1]\n",
"model = CropSegmentationModel(\n",
" in_channels=num_channels,\n",
" embed_dim=256,\n",
" depth=4,\n",
" num_heads=4,\n",
" num_classes=2\n",
").to(device)\n",
"\n",
"total_params = sum(p.numel() for p in model.parameters())\n",
"print(f\"Model created with {total_params:,} parameters\")\n",
"print(f\"Input channels: {num_channels}\")\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 71
},
"id": "VhJEw8iuashQ",
"executionInfo": {
"status": "ok",
"timestamp": 1767587369567,
"user_tz": -330,
"elapsed": 43,
"user": {
"displayName": "Enigma Cypher",
"userId": "06934947732805796536"
}
},
"outputId": "45d6f88d-c543-4817-f394-7cd4acad4931"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <style>\n",
" .geemap-dark {\n",
" --jp-widgets-color: white;\n",
" --jp-widgets-label-color: white;\n",
" --jp-ui-font-color1: white;\n",
" --jp-layout-color2: #454545;\n",
" background-color: #383838;\n",
" }\n",
"\n",
" .geemap-dark .jupyter-button {\n",
" --jp-layout-color3: #383838;\n",
" }\n",
"\n",
" .geemap-colab {\n",
" background-color: var(--colab-primary-surface-color, white);\n",
" }\n",
"\n",
" .geemap-colab .jupyter-button {\n",
" --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
" }\n",
" </style>\n",
" "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Device: cpu\n",
"Model created with 2,845,690 parameters\n",
"Input channels: 4\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import torch.optim as optim\n",
"from tqdm import tqdm\n",
"\n",
"print(\"Starting training\")\n",
"print(f\"Device: {device}\")\n",
"print(f\"Batch size: {batch_size}\")\n",
"\n",
"criterion = nn.CrossEntropyLoss()\n",
"optimizer = optim.Adam(model.parameters(), lr=1e-3)\n",
"\n",
"num_epochs = 5\n",
"best_val_loss = float('inf')\n",
"patience = 3\n",
"patience_counter = 0\n",
"\n",
"train_losses = []\n",
"val_losses = []\n",
"\n",
"for epoch in range(num_epochs):\n",
" print(f\"\\nEpoch {epoch+1}/{num_epochs}\")\n",
" print(\"-\" * 60)\n",
"\n",
" model.train()\n",
" train_loss = 0\n",
"\n",
" pbar = tqdm(train_loader, desc='Training')\n",
" for X_batch, y_batch in pbar:\n",
" X_batch, y_batch = X_batch.to(device), y_batch.to(device)\n",
"\n",
" outputs = model(X_batch)\n",
" loss = criterion(outputs, y_batch.squeeze(1).long())\n",
"\n",
" optimizer.zero_grad()\n",
" loss.backward()\n",
" torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
" optimizer.step()\n",
"\n",
" train_loss += loss.item()\n",
" pbar.set_postfix({'loss': f'{loss.item():.4f}'})\n",
"\n",
" train_loss /= len(train_loader)\n",
" train_losses.append(train_loss)\n",
" print(f\"Train Loss: {train_loss:.4f}\")\n",
"\n",
" model.eval()\n",
" val_loss = 0\n",
"\n",
" with torch.no_grad():\n",
" pbar = tqdm(val_loader, desc='Validation')\n",
" for X_batch, y_batch in pbar:\n",
" X_batch, y_batch = X_batch.to(device), y_batch.to(device)\n",
"\n",
" outputs = model(X_batch)\n",
" loss = criterion(outputs, y_batch.squeeze(1).long())\n",
"\n",
" val_loss += loss.item()\n",
" pbar.set_postfix({'loss': f'{loss.item():.4f}'})\n",
"\n",
" val_loss /= len(val_loader)\n",
" val_losses.append(val_loss)\n",
" print(f\"Val Loss: {val_loss:.4f}\")\n",
"\n",
" if val_loss < best_val_loss:\n",
" best_val_loss = val_loss\n",
" patience_counter = 0\n",
" torch.save(model.state_dict(), '/content/best_model.pth')\n",
" print(\"Model saved\")\n",
" else:\n",
" patience_counter += 1\n",
"\n",
" print(f\"Patience: {patience_counter}/{patience}\")\n",
"\n",
" if patience_counter >= patience:\n",
" print(f\"Early stopping at epoch {epoch+1}\")\n",
" break\n",
"\n",
"model.load_state_dict(torch.load('/content/best_model.pth'))\n",
"print(\"Training complete\")\n",
"\n",
"plt.figure(figsize=(10, 5))\n",
"plt.plot(train_losses, label='Train Loss', marker='o', linewidth=2)\n",
"plt.plot(val_losses, label='Val Loss', marker='s', linewidth=2)\n",
"plt.xlabel('Epoch')\n",
"plt.ylabel('Loss')\n",
"plt.title('Training Progress')\n",
"plt.legend()\n",
"plt.grid(True, alpha=0.3)\n",
"plt.tight_layout()\n",
"plt.savefig('/content/training_loss.png', dpi=100)\n",
"plt.show()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "kuKau0ZSavf1",
"executionInfo": {
"status": "ok",
"timestamp": 1767587398718,
"user_tz": -330,
"elapsed": 17800,
"user": {
"displayName": "Enigma Cypher",
"userId": "06934947732805796536"
}
},
"outputId": "d4aed577-79d6-492f-fe72-d34dbac4b427"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <style>\n",
" .geemap-dark {\n",
" --jp-widgets-color: white;\n",
" --jp-widgets-label-color: white;\n",
" --jp-ui-font-color1: white;\n",
" --jp-layout-color2: #454545;\n",
" background-color: #383838;\n",
" }\n",
"\n",
" .geemap-dark .jupyter-button {\n",
" --jp-layout-color3: #383838;\n",
" }\n",
"\n",
" .geemap-colab {\n",
" background-color: var(--colab-primary-surface-color, white);\n",
" }\n",
"\n",
" .geemap-colab .jupyter-button {\n",
" --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
" }\n",
" </style>\n",
" "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Starting training\n",
"Device: cpu\n",
"Batch size: 8\n",
"\n",
"Epoch 1/5\n",
"------------------------------------------------------------\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Training: 100%|██████████| 15/15 [00:03<00:00, 4.62it/s, loss=0.6076]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Train Loss: 0.6258\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Validation: 100%|██████████| 4/4 [00:00<00:00, 23.19it/s, loss=0.6913]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Val Loss: 0.6548\n",
"Model saved\n",
"Patience: 0/3\n",
"\n",
"Epoch 2/5\n",
"------------------------------------------------------------\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Training: 100%|██████████| 15/15 [00:02<00:00, 7.37it/s, loss=0.5685]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Train Loss: 0.6074\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Validation: 100%|██████████| 4/4 [00:00<00:00, 34.02it/s, loss=0.6683]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Val Loss: 0.6481\n",
"Model saved\n",
"Patience: 0/3\n",
"\n",
"Epoch 3/5\n",
"------------------------------------------------------------\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Training: 100%|██████████| 15/15 [00:01<00:00, 7.83it/s, loss=0.5603]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Train Loss: 0.6011\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Validation: 100%|██████████| 4/4 [00:00<00:00, 35.49it/s, loss=0.6712]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Val Loss: 0.6408\n",
"Model saved\n",
"Patience: 0/3\n",
"\n",
"Epoch 4/5\n",
"------------------------------------------------------------\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Training: 100%|██████████| 15/15 [00:01<00:00, 7.58it/s, loss=0.5992]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Train Loss: 0.5975\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Validation: 100%|██████████| 4/4 [00:00<00:00, 36.18it/s, loss=0.9962]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Val Loss: 0.8606\n",
"Patience: 1/3\n",
"\n",
"Epoch 5/5\n",
"------------------------------------------------------------\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Training: 100%|██████████| 15/15 [00:01<00:00, 8.02it/s, loss=0.6284]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Train Loss: 0.6099\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Validation: 100%|██████████| 4/4 [00:00<00:00, 36.00it/s, loss=0.6986]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Val Loss: 0.6489\n",
"Patience: 2/3\n",
"Training complete\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1000x500 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"import matplotlib.pyplot as plt\n",
"from sklearn.metrics import confusion_matrix, accuracy_score\n",
"\n",
"print(\"Testing...\")\n",
"\n",
"model.eval()\n",
"all_preds = []\n",
"all_targets = []\n",
"\n",
"with torch.no_grad():\n",
" for X_batch, y_batch in tqdm(test_loader, desc='Testing'):\n",
" X_batch, y_batch = X_batch.to(device), y_batch.to(device)\n",
"\n",
" outputs = model(X_batch)\n",
" predictions = outputs.argmax(1).cpu().numpy()\n",
" targets = y_batch.squeeze(1).cpu().numpy()\n",
"\n",
" all_preds.append(predictions)\n",
" all_targets.append(targets)\n",
"\n",
"all_preds = np.concatenate(all_preds)\n",
"all_targets = np.concatenate(all_targets)\n",
"\n",
"print(\"Test complete\")\n",
"\n",
"accuracy = accuracy_score(all_targets.flatten(), all_preds.flatten())\n",
"cm = confusion_matrix(all_targets.flatten(), all_preds.flatten())\n",
"\n",
"print(\"-\" * 60)\n",
"print(\"TEST RESULTS\")\n",
"print(\"-\" * 60)\n",
"print(f\"Accuracy: {accuracy:.4f}\")\n",
"print(f\"Confusion Matrix:\")\n",
"print(f\" True Negatives: {cm[0, 0]}\")\n",
"print(f\" False Positives: {cm[0, 1]}\")\n",
"print(f\" False Negatives: {cm[1, 0]}\")\n",
"print(f\" True Positives: {cm[1, 1]}\")\n",
"\n",
"fig, axes = plt.subplots(3, 3, figsize=(15, 15))\n",
"\n",
"for idx in range(3):\n",
" X_batch, y_batch = next(iter(test_loader))\n",
" X_sample = X_batch[idx].cpu().numpy()\n",
" y_sample = y_batch[idx, 0].cpu().numpy()\n",
"\n",
" with torch.no_grad():\n",
" X_batch = X_batch.to(device)\n",
" output = model(X_batch)\n",
" pred = output[idx].argmax(0).cpu().numpy()\n",
"\n",
" rgb = X_sample[[2, 1, 0], :, :]\n",
" rgb = np.transpose(rgb, (1, 2, 0))\n",
" rgb = np.clip(rgb, 0, 1)\n",
"\n",
" axes[0, idx].imshow(rgb)\n",
" axes[0, idx].set_title(f'Sample {idx+1}: RGB')\n",
" axes[0, idx].axis('off')\n",
"\n",
" axes[1, idx].imshow(y_sample, cmap='RdYlGn', vmin=0, vmax=1)\n",
" axes[1, idx].set_title('Ground Truth')\n",
" axes[1, idx].axis('off')\n",
"\n",
" axes[2, idx].imshow(pred, cmap='RdYlGn', vmin=0, vmax=1)\n",
" axes[2, idx].set_title('Prediction')\n",
" axes[2, idx].axis('off')\n",
"\n",
"plt.tight_layout()\n",
"plt.savefig('/content/test_results.png', dpi=100)\n",
"plt.show()\n",
"\n",
"fig, ax = plt.subplots(figsize=(8, 7))\n",
"im = ax.imshow(cm, cmap='Blues', aspect='auto')\n",
"\n",
"classes = ['Background', 'Wheat']\n",
"ax.set_xticks(np.arange(len(classes)))\n",
"ax.set_yticks(np.arange(len(classes)))\n",
"ax.set_xticklabels(classes)\n",
"ax.set_yticklabels(classes)\n",
"ax.set_xlabel('Predicted')\n",
"ax.set_ylabel('Actual')\n",
"ax.set_title('Confusion Matrix')\n",
"\n",
"for i in range(2):\n",
" for j in range(2):\n",
" text = ax.text(j, i, cm[i, j], ha=\"center\", va=\"center\",\n",
" color=\"white\" if cm[i, j] > cm.max() / 2 else \"black\",\n",
" fontsize=14, fontweight='bold')\n",
"\n",
"plt.colorbar(im, ax=ax)\n",
"plt.tight_layout()\n",
"plt.savefig('/content/confusion_matrix.png', dpi=100)\n",
"plt.show()\n",
"\n",
"print(\"All visualizations saved\")\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "9OJR4nl9a28X",
"executionInfo": {
"status": "ok",
"timestamp": 1767587416887,
"user_tz": -330,
"elapsed": 5330,
"user": {
"displayName": "Enigma Cypher",
"userId": "06934947732805796536"
}
},
"outputId": "2d4f42b7-bf8b-4f29-d716-f6d3b51760ec"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <style>\n",
" .geemap-dark {\n",
" --jp-widgets-color: white;\n",
" --jp-widgets-label-color: white;\n",
" --jp-ui-font-color1: white;\n",
" --jp-layout-color2: #454545;\n",
" background-color: #383838;\n",
" }\n",
"\n",
" .geemap-dark .jupyter-button {\n",
" --jp-layout-color3: #383838;\n",
" }\n",
"\n",
" .geemap-colab {\n",
" background-color: var(--colab-primary-surface-color, white);\n",
" }\n",
"\n",
" .geemap-colab .jupyter-button {\n",
" --jp-layout-color3: var(--colab-primary-surface-color, white);\n",
" }\n",
" </style>\n",
" "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Testing...\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Testing: 100%|██████████| 4/4 [00:00<00:00, 25.14it/s]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Test complete\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"------------------------------------------------------------\n",
"TEST RESULTS\n",
"------------------------------------------------------------\n",
"Accuracy: 0.6684\n",
"Confusion Matrix:\n",
" True Negatives: 0\n",
" False Positives: 35314\n",
" False Negatives: 0\n",
" True Positives: 71182\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1500x1500 with 9 Axes>"
],
"image/png": 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},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 800x700 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"All visualizations saved\n"
]
}
]
}
]
} |