diff --git a/aggregatepatches.ipynb b/aggregatepatches.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Reconstruction completed successfully!\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os\n",
+ "from PIL import Image\n",
+ "\n",
+ "# Parameters\n",
+ "patch_size = 256\n",
+ "overlap = 128 # 50% overlap\n",
+ "\n",
+ "# Directories for patches\n",
+ "processed_data_dir = r\"/data/nooshin/computationalAg/Nassar 2020/patches/data\"\n",
+ "processed_mask_dir = r\"/data/nooshin/computationalAg/Nassar 2020/patches/mask\"\n",
+ "processed_color_mask_dir = r\"/data/nooshin/computationalAg/Nassar 2020/patches/mask_color\"\n",
+ "\n",
+ "# Output directories for reconstructed images\n",
+ "reconstructed_data_dir = r\"/data/nooshin/computationalAg/Nassar 2020/reconstructed/data\"\n",
+ "reconstructed_mask_dir = r\"/data/nooshin/computationalAg/Nassar 2020/reconstructed/mask\"\n",
+ "reconstructed_color_mask_dir = r\"/data/nooshin/computationalAg/Nassar 2020/reconstructed/mask_color\"\n",
+ "\n",
+ "os.makedirs(reconstructed_data_dir, exist_ok=True)\n",
+ "os.makedirs(reconstructed_mask_dir, exist_ok=True)\n",
+ "os.makedirs(reconstructed_color_mask_dir, exist_ok=True)\n",
+ "\n",
+ "# Sub-regions to process\n",
+ "sub_regions_to_process = {\n",
+ " \"sub0\": (6449, 8642), # Example: original width and height for sub0\n",
+ " \"sub1\": (3342, 2970),\n",
+ " \"sub2\": (3527, 4827),\n",
+ " \"sub3\": (5570, 5755),\n",
+ " \"sub4\": (6126, 5012),\n",
+ " \"sub5\": (3342, 14481),\n",
+ "}\n",
+ "\n",
+ "# Function to aggregate patches\n",
+ "def aggregate_patches(patches, original_width, original_height, patch_size, overlap):\n",
+ " \"\"\"Reconstructs the original image from patches.\"\"\"\n",
+ " # Calculate padded width and height\n",
+ " padded_width = (original_width + patch_size - 1) // patch_size * patch_size\n",
+ " padded_height = (original_height + patch_size - 1) // patch_size * patch_size\n",
+ "\n",
+ " # Create a blank canvas\n",
+ " reconstructed_image = Image.new(\"RGB\", (padded_width, padded_height))\n",
+ "\n",
+ " # Stitch patches onto the canvas\n",
+ " idx = 0\n",
+ " for y in range(0, padded_height - patch_size + 1, overlap):\n",
+ " for x in range(0, padded_width - patch_size + 1, overlap):\n",
+ " reconstructed_image.paste(patches[idx], (x, y))\n",
+ " idx += 1\n",
+ "\n",
+ " # Crop to original dimensions to remove padding\n",
+ " return reconstructed_image.crop((0, 0, original_width, original_height))\n",
+ "\n",
+ "# Reconstruct images for each sub-region\n",
+ "for sub_region, (original_width, original_height) in sub_regions_to_process.items():\n",
+ " # Collect patches\n",
+ " data_patches = []\n",
+ " mask_patches = []\n",
+ " color_mask_patches = []\n",
+ "\n",
+ " patch_files = [f for f in os.listdir(processed_data_dir) if f.startswith(sub_region)]\n",
+ " patch_files.sort() # Ensure patches are loaded in order\n",
+ "\n",
+ " for patch_file in patch_files:\n",
+ " data_patch_path = os.path.join(processed_data_dir, patch_file)\n",
+ " mask_patch_path = os.path.join(processed_mask_dir, patch_file)\n",
+ " color_patch_path = os.path.join(processed_color_mask_dir, patch_file)\n",
+ "\n",
+ " if os.path.exists(data_patch_path):\n",
+ " data_patches.append(Image.open(data_patch_path))\n",
+ " if os.path.exists(mask_patch_path):\n",
+ " mask_patches.append(Image.open(mask_patch_path))\n",
+ " if os.path.exists(color_patch_path):\n",
+ " color_mask_patches.append(Image.open(color_patch_path))\n",
+ "\n",
+ " # Reconstruct images\n",
+ " if data_patches:\n",
+ " reconstructed_data = aggregate_patches(data_patches, original_width, original_height, patch_size, overlap)\n",
+ " reconstructed_data.save(os.path.join(reconstructed_data_dir, f\"{sub_region}.png\"))\n",
+ "\n",
+ " if mask_patches:\n",
+ " reconstructed_mask = aggregate_patches(mask_patches, original_width, original_height, patch_size, overlap)\n",
+ " reconstructed_mask.save(os.path.join(reconstructed_mask_dir, f\"{sub_region}.png\"))\n",
+ "\n",
+ " if color_mask_patches:\n",
+ " reconstructed_color_mask = aggregate_patches(color_mask_patches, original_width, original_height, patch_size, overlap)\n",
+ " reconstructed_color_mask.save(os.path.join(reconstructed_color_mask_dir, f\"{sub_region}.png\"))\n",
+ "\n",
+ "print(\"Reconstruction completed successfully!\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Processing sub0: Found 3468 patches.\n",
+ "Expected patches: 3468\n",
+ "Processing sub1: Found 648 patches.\n",
+ "Expected patches: 648\n",
+ "Processing sub2: Found 1064 patches.\n",
+ "Expected patches: 1064\n",
+ "Processing sub3: Found 1980 patches.\n",
+ "Expected patches: 1980\n",
+ "Processing sub4: Found 1920 patches.\n",
+ "Expected patches: 1920\n",
+ "Processing sub5: Found 3078 patches.\n",
+ "Expected patches: 3078\n",
+ "Reconstruction completed successfully!\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os\n",
+ "from PIL import Image\n",
+ "import numpy as np\n",
+ "\n",
+ "# Define patch directory and output directory\n",
+ "patch_dir = r\"/data/nooshin/computationalAg/Nassar 2020/patches/mask\"\n",
+ "output_dir = r\"/data/nooshin/computationalAg/Nassar 2020/reconstructed\"\n",
+ "os.makedirs(output_dir, exist_ok=True)\n",
+ "\n",
+ "# Parameters\n",
+ "patch_size = 256\n",
+ "overlap = 128\n",
+ "\n",
+ "def reconstruct_image(patch_files, original_height, original_width, patch_size, overlap):\n",
+ " \"\"\"\n",
+ " Reconstruct the original image from patches.\n",
+ " \"\"\"\n",
+ " # Initialize empty array for the reconstructed image\n",
+ " reconstructed_image = np.zeros((original_height, original_width), dtype=np.float64)\n",
+ " weight_map = np.zeros((original_height, original_width), dtype=np.float64)\n",
+ "\n",
+ " # Sort patch files to ensure correct order\n",
+ " patch_files = sorted(patch_files)\n",
+ " patch_index = 0\n",
+ "\n",
+ " # Iterate through the positions\n",
+ " for y_start in range(0, original_height, patch_size - overlap):\n",
+ " for x_start in range(0, original_width, patch_size - overlap):\n",
+ " # Ensure patch_index does not exceed the number of patch files\n",
+ " if patch_index >= len(patch_files):\n",
+ " print(f\"Warning: Patch index {patch_index} exceeds available patches.\")\n",
+ " continue\n",
+ "\n",
+ " # Load the current patch\n",
+ " patch = np.array(Image.open(patch_files[patch_index]))\n",
+ " patch_index += 1\n",
+ "\n",
+ " # Define the placement area\n",
+ " y_end = min(y_start + patch_size, original_height)\n",
+ " x_end = min(x_start + patch_size, original_width)\n",
+ "\n",
+ " # Place the patch in the reconstructed image\n",
+ " reconstructed_image[y_start:y_end, x_start:x_end] += patch[: y_end - y_start, : x_end - x_start]\n",
+ " weight_map[y_start:y_end, x_start:x_end] += 1\n",
+ "\n",
+ " # Normalize by the weight map and cast to uint8\n",
+ " reconstructed_image = (reconstructed_image / np.maximum(weight_map, 1)).astype(np.uint8)\n",
+ "\n",
+ " return Image.fromarray(reconstructed_image)\n",
+ "\n",
+ "\n",
+ "# Sub-regions with dimensions\n",
+ "sub_regions_to_process = {\n",
+ " \"sub0\": (6449, 8642), # Replace with actual dimensions\n",
+ " \"sub1\": (3342, 2970),\n",
+ " \"sub2\": (3527, 4827),\n",
+ " \"sub3\": (5570, 5755),\n",
+ " \"sub4\": (6126, 5012),\n",
+ " \"sub5\": (3342, 14481),\n",
+ "}\n",
+ "\n",
+ "for sub_region, (original_height, original_width) in sub_regions_to_process.items():\n",
+ " patch_files = [\n",
+ " os.path.join(patch_dir, file)\n",
+ " for file in os.listdir(patch_dir)\n",
+ " if file.startswith(sub_region)\n",
+ " ]\n",
+ "\n",
+ " # Debugging: Print number of patches and expected patches\n",
+ " num_patches = len(patch_files)\n",
+ " print(f\"Processing {sub_region}: Found {num_patches} patches.\")\n",
+ "\n",
+ " # Ensure enough patches are present\n",
+ " step_size = patch_size - overlap\n",
+ " expected_patches = ((original_height + step_size - 1) // step_size) * (\n",
+ " (original_width + step_size - 1) // step_size\n",
+ " )\n",
+ " print(f\"Expected patches: {expected_patches}\")\n",
+ "\n",
+ " if num_patches != expected_patches:\n",
+ " print(f\"Warning: Mismatch in patches for {sub_region}. Found: {num_patches}, Expected: {expected_patches}\")\n",
+ "\n",
+ " # Reconstruct the sub-region image\n",
+ " reconstructed_image = reconstruct_image(patch_files, original_height, original_width, patch_size, overlap)\n",
+ "\n",
+ " # Save the reconstructed image\n",
+ " reconstructed_image.save(os.path.join(output_dir, f\"{sub_region}_reconstructed.png\"))\n",
+ "\n",
+ "print(\"Reconstruction completed successfully!\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Warning: Mismatch in patches for sub0. Found: 3468, Expected: 3350\n",
+ "Warning: Mismatch in patches for sub1. Found: 648, Expected: 598\n",
+ "Warning: Mismatch in patches for sub2. Found: 1064, Expected: 999\n",
+ "Warning: Mismatch in patches for sub3. Found: 1980, Expected: 1892\n",
+ "Warning: Mismatch in patches for sub4. Found: 1920, Expected: 1833\n",
+ "Warning: Mismatch in patches for sub5. Found: 3078, Expected: 2938\n",
+ "Reconstruction of data patches completed successfully!\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os\n",
+ "from PIL import Image\n",
+ "import numpy as np\n",
+ "\n",
+ "# Define input and output directories\n",
+ "data_patch_dir = r\"/data/nooshin/computationalAg/Nassar 2020/patches/data\"\n",
+ "output_dir = r\"/data/nooshin/computationalAg/Nassar 2020/reconstructed\"\n",
+ "\n",
+ "# Create output directory if it doesn't exist\n",
+ "os.makedirs(output_dir, exist_ok=True)\n",
+ "\n",
+ "# Patch parameters\n",
+ "patch_size = 256\n",
+ "overlap = 128\n",
+ "step_size = patch_size - overlap\n",
+ "\n",
+ "# Sub-region metadata (include original dimensions)\n",
+ "sub_regions_metadata = {\n",
+ " \"sub0\": {\"width\": 6449, \"height\": 8642},\n",
+ " \"sub1\": {\"width\": 3342, \"height\": 2970},\n",
+ " \"sub2\": {\"width\": 3527, \"height\": 4827},\n",
+ " \"sub3\": {\"width\": 5570, \"height\": 5755},\n",
+ " \"sub4\": {\"width\": 6126, \"height\": 5012},\n",
+ " \"sub5\": {\"width\": 3342, \"height\": 14481},\n",
+ "}\n",
+ "\n",
+ "# Reconstruct an image from its patches\n",
+ "def reconstruct_data_image(patch_files, original_height, original_width, patch_size, overlap):\n",
+ " \"\"\"\n",
+ " Reconstruct an image from its patches with overlap.\n",
+ " \"\"\"\n",
+ " # Initialize an empty array for the reconstructed image\n",
+ " reconstructed_image = np.zeros((original_height, original_width, 3), dtype=np.float32)\n",
+ " weight_map = np.zeros((original_height, original_width), dtype=np.float32)\n",
+ "\n",
+ " patch_index = 0\n",
+ " step_size = patch_size - overlap\n",
+ " for y_start in range(0, original_height, step_size):\n",
+ " for x_start in range(0, original_width, step_size):\n",
+ " y_end = min(y_start + patch_size, original_height)\n",
+ " x_end = min(x_start + patch_size, original_width)\n",
+ "\n",
+ " # Load the current patch\n",
+ " if patch_index >= len(patch_files):\n",
+ " print(\"Warning: Insufficient patches to fully reconstruct.\")\n",
+ " break\n",
+ "\n",
+ " patch = np.array(Image.open(patch_files[patch_index]))\n",
+ " patch_index += 1\n",
+ "\n",
+ " # Place the patch in the reconstructed image\n",
+ " reconstructed_image[y_start:y_end, x_start:x_end, :] += patch[:y_end - y_start, :x_end - x_start, :]\n",
+ " weight_map[y_start:y_end, x_start:x_end] += 1\n",
+ "\n",
+ " # Normalize by the weight map to handle overlapping areas\n",
+ " reconstructed_image /= np.maximum(weight_map[:, :, None], 1)\n",
+ "\n",
+ " return Image.fromarray(reconstructed_image.astype(np.uint8))\n",
+ "\n",
+ "\n",
+ "# Process each sub-region for reconstruction\n",
+ "for sub_region, metadata in sub_regions_metadata.items():\n",
+ " original_width = metadata[\"width\"]\n",
+ " original_height = metadata[\"height\"]\n",
+ "\n",
+ " # Collect all patch files for the sub-region\n",
+ " patch_files = sorted([\n",
+ " os.path.join(data_patch_dir, file)\n",
+ " for file in os.listdir(data_patch_dir)\n",
+ " if file.startswith(sub_region)\n",
+ " ])\n",
+ "\n",
+ " # Verify the number of patches\n",
+ " num_patches_horizontally = (original_width - overlap) // step_size + 1\n",
+ " num_patches_vertically = (original_height - overlap) // step_size + 1\n",
+ " expected_patches = num_patches_horizontally * num_patches_vertically\n",
+ "\n",
+ " if len(patch_files) != expected_patches:\n",
+ " print(f\"Warning: Mismatch in patches for {sub_region}. Found: {len(patch_files)}, Expected: {expected_patches}\")\n",
+ "\n",
+ " # Reconstruct the sub-region image\n",
+ " reconstructed_image = reconstruct_data_image(patch_files, original_height, original_width, patch_size, overlap)\n",
+ "\n",
+ " # Save the reconstructed image\n",
+ " reconstructed_image.save(os.path.join(output_dir, f\"{sub_region}_reconstructed.png\"))\n",
+ "\n",
+ "print(\"Reconstruction of data patches completed successfully!\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Warning: Mismatch in patches for sub0. Found: 3468, Expected: 3350\n",
+ "Warning: Mismatch in patches for sub1. Found: 648, Expected: 598\n",
+ "Warning: Mismatch in patches for sub2. Found: 1064, Expected: 999\n",
+ "Warning: Mismatch in patches for sub3. Found: 1980, Expected: 1892\n",
+ "Warning: Mismatch in patches for sub4. Found: 1920, Expected: 1833\n",
+ "Warning: Mismatch in patches for sub5. Found: 3078, Expected: 2938\n",
+ "Reconstruction of data patches completed successfully!\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os\n",
+ "from PIL import Image\n",
+ "import numpy as np\n",
+ "\n",
+ "# Define input and output directories\n",
+ "data_patch_dir = r\"/data/nooshin/computationalAg/Nassar 2020/patches/data\"\n",
+ "output_dir = r\"/data/nooshin/computationalAg/Nassar 2020/reconstructed/withoutnorm\"\n",
+ "\n",
+ "# Create output directory if it doesn't exist\n",
+ "os.makedirs(output_dir, exist_ok=True)\n",
+ "\n",
+ "# Patch parameters\n",
+ "patch_size = 256\n",
+ "overlap = 128\n",
+ "step_size = patch_size - overlap\n",
+ "\n",
+ "# Sub-region metadata (include original dimensions)\n",
+ "sub_regions_metadata = {\n",
+ " \"sub0\": {\"width\": 6449, \"height\": 8642},\n",
+ " \"sub1\": {\"width\": 3342, \"height\": 2970},\n",
+ " \"sub2\": {\"width\": 3527, \"height\": 4827},\n",
+ " \"sub3\": {\"width\": 5570, \"height\": 5755},\n",
+ " \"sub4\": {\"width\": 6126, \"height\": 5012},\n",
+ " \"sub5\": {\"width\": 3342, \"height\": 14481},\n",
+ "}\n",
+ "\n",
+ "def reconstruct_data_image(patch_files, original_height, original_width, patch_size, overlap):\n",
+ " \"\"\"\n",
+ " Reconstruct an image from its patches without normalization.\n",
+ " \"\"\"\n",
+ " # Initialize an empty array for the reconstructed image\n",
+ " reconstructed_image = np.zeros((original_height, original_width, 3), dtype=np.uint8)\n",
+ "\n",
+ " patch_index = 0\n",
+ " for y_start in range(0, original_height, step_size):\n",
+ " for x_start in range(0, original_width, step_size):\n",
+ " y_end = min(y_start + patch_size, original_height)\n",
+ " x_end = min(x_start + patch_size, original_width)\n",
+ "\n",
+ " # Load the current patch\n",
+ " if patch_index >= len(patch_files):\n",
+ " print(\"Warning: Insufficient patches to fully reconstruct.\")\n",
+ " break\n",
+ "\n",
+ " patch = np.array(Image.open(patch_files[patch_index]))\n",
+ " patch_index += 1\n",
+ "\n",
+ " # Place the patch in the reconstructed image\n",
+ " reconstructed_image[y_start:y_end, x_start:x_end, :] = patch[:y_end - y_start, :x_end - x_start, :]\n",
+ "\n",
+ " return Image.fromarray(reconstructed_image)\n",
+ "\n",
+ "\n",
+ "# Process each sub-region for reconstruction\n",
+ "for sub_region, metadata in sub_regions_metadata.items():\n",
+ " original_width = metadata[\"width\"]\n",
+ " original_height = metadata[\"height\"]\n",
+ "\n",
+ " # Collect all patch files for the sub-region\n",
+ " patch_files = sorted([\n",
+ " os.path.join(data_patch_dir, file)\n",
+ " for file in os.listdir(data_patch_dir)\n",
+ " if file.startswith(sub_region)\n",
+ " ])\n",
+ "\n",
+ " # Verify the number of patches\n",
+ " num_patches_horizontally = (original_width - overlap) // step_size + 1\n",
+ " num_patches_vertically = (original_height - overlap) // step_size + 1\n",
+ " expected_patches = num_patches_horizontally * num_patches_vertically\n",
+ "\n",
+ " if len(patch_files) != expected_patches:\n",
+ " print(f\"Warning: Mismatch in patches for {sub_region}. Found: {len(patch_files)}, Expected: {expected_patches}\")\n",
+ "\n",
+ " # Reconstruct the sub-region image\n",
+ " reconstructed_image = reconstruct_data_image(patch_files, original_height, original_width, patch_size, overlap)\n",
+ "\n",
+ " # Save the reconstructed image\n",
+ " reconstructed_image.save(os.path.join(output_dir, f\"{sub_region}_reconstructed.png\"))\n",
+ "\n",
+ "print(\"Reconstruction of data patches completed successfully!\")\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.8.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}