diff --git a/similarity-python/similarity-python_umap_adjusted.ipynb b/similarity-python/similarity-python_umap_adjusted.ipynb new file mode 100644 index 0000000..a559d43 --- /dev/null +++ b/similarity-python/similarity-python_umap_adjusted.ipynb @@ -0,0 +1,462 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# !pip install transformers torch umap-learn pillow numpy matplotlib pandas pylabeladjust" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import numpy as np\n", + "import os\n", + "import pickle\n", + "import shutil\n", + "import pandas as pd\n", + "from PIL import Image, UnidentifiedImageError\n", + "from tqdm import tqdm\n", + "from transformers import CLIPImageProcessor, CLIPModel\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using device: mps\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 102/102 [00:15<00:00, 6.53it/s]\n" + ] + } + ], + "source": [ + "\n", + "# path to the folder with images generated by vikus-viewer-script (1024)\n", + "image_path = 'vikus-viewer/data/1024/'\n", + "# batch_size for CLIP depends on your GPU memory\n", + "batch_size = 10\n", + "\n", + "def create_embeddings(image_folder, batch_size=10):\n", + " if torch.cuda.is_available():\n", + " device = torch.device(\"cuda\")\n", + " elif torch.backends.mps.is_available():\n", + " device = torch.device(\"mps\") # MPS for Metal Performance Shaders on macOS\n", + " else:\n", + " device = torch.device(\"cpu\")\n", + " print(f\"Using device: {device}\")\n", + "\n", + " model = CLIPModel.from_pretrained('openai/clip-vit-base-patch32').to(device)\n", + " processor = CLIPImageProcessor.from_pretrained('openai/clip-vit-base-patch32')\n", + "\n", + " embeddings = {}\n", + "\n", + " image_files = [f for f in os.listdir(image_folder) if f.endswith('.jpg')]\n", + " \n", + " #image_files = image_files[:20]\n", + "\n", + " for i in tqdm(range(0, len(image_files), batch_size)):\n", + " batch_files = image_files[i:i + batch_size]\n", + " batch_images = []\n", + " valid_indices = []\n", + "\n", + " for j, image_file in enumerate(batch_files):\n", + " this_image_path = os.path.join(image_folder, image_file)\n", + "\n", + " try:\n", + " image = Image.open(this_image_path)\n", + " #image = image.resize((224, 224))\n", + " except UnidentifiedImageError:\n", + " print(f\"Invalid image file: {this_image_path}\")\n", + " invalid_folder = os.path.join(os.path.dirname(image_folder), 'invalid')\n", + " if not os.path.exists(invalid_folder):\n", + " os.mkdir(invalid_folder)\n", + " shutil.move(this_image_path, os.path.join(invalid_folder, image_file))\n", + " continue\n", + "\n", + " batch_images.append(image)\n", + " valid_indices.append(j)\n", + "\n", + " inputs = processor(batch_images, return_tensors=\"pt\", padding=True)\n", + " inputs = inputs.to(device)\n", + "\n", + " with torch.no_grad():\n", + " batch_embeddings = model.get_image_features(**inputs).cpu().numpy()\n", + "\n", + " for j, image_file in enumerate(batch_files):\n", + " if j not in valid_indices:\n", + " continue\n", + "\n", + " image_id = os.path.splitext(image_file)[0]\n", + " embeddings[image_id] = batch_embeddings[valid_indices.index(j)]\n", + "\n", + " return embeddings\n", + "\n", + "embeddings = create_embeddings(image_path, batch_size)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# save embeddings if you want to use them later\n", + "with open('clip-embeddings.pickle', 'wb') as f:\n", + " pickle.dump(embeddings, f)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# load embeddings if you have already created them\n", + "import pickle\n", + "with open('clip-embeddings.pickle', 'rb') as f:\n", + " embeddings = pickle.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/Noich001/anaconda3/lib/python3.11/site-packages/umap/umap_.py:1943: UserWarning: n_jobs value -1 overridden to 1 by setting random_state. Use no seed for parallelism.\n", + " warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n" + ] + } + ], + "source": [ + "# use umap to reduce dimensionality to 2D\n", + "import umap\n", + "import numpy as np\n", + "\n", + "reducer = umap.UMAP(random_state=42, min_dist=0.05, n_neighbors=50)\n", + "embedding = np.array(list(embeddings.values()))\n", + "embedding = embedding.reshape((embedding.shape[0], -1))\n", + "embedding = embedding / np.linalg.norm(embedding, axis=1, keepdims=True)\n", + "embedding = reducer.fit_transform(embedding)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " x y width height\n", + "0 -0.061831 -0.646391 0.050488 0.035155\n", + "1 -1.552722 0.054856 0.034180 0.051929\n", + "2 0.359539 0.276307 0.060171 0.029498\n", + "3 0.430037 0.419490 0.059814 0.029674\n", + "4 1.220929 0.561560 0.049227 0.036055" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import os\n", + "from PIL import Image\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "\n", + "# First we have to get the sizes of the images:\n", + "image_sizes = []\n", + "\n", + "for ix,filename in enumerate(os.listdir(image_path)):\n", + " if filename.lower().endswith(('.png', '.jpg', '.jpeg', '.gif', '.bmp')):\n", + " this_image_path = os.path.join(image_path, filename)\n", + " image = Image.open(this_image_path)\n", + " width, height = image.size\n", + " image_sizes.append((embedding[ix,0],embedding[ix,1],width, height))\n", + "\n", + "rectangles = pd.DataFrame(image_sizes, columns=[\"x\",\"y\",\"width\", \"height\"])\n", + "rectangles_standardized = rectangles.copy()\n", + "\n", + "\n", + "scaler = StandardScaler()\n", + "scale_factor=.05\n", + "\n", + "# First we standardize the x and y coordinates:\n", + "rectangles_standardized[['x','y']] = scaler.fit_transform(rectangles_standardized[['x','y']])\n", + "\n", + "\n", + "# Then we rescale the sizes reative to the extent of a standardized embedding, using our scale factor:\n", + "rectangles_standardized[['width','height']] = rectangles_standardized[['width','height']] / rectangles_standardized[['width','height']].max(axis=0) * scale_factor\n", + "\n", + "# We then bring all size to encompass the same area. This makes smaller and larger images more comparable in the final embedding.\n", + "# We won't want that for all use-cases, but it's nice for looking at collections.\n", + "rectangles_standardized['original_area'] = rectangles_standardized['width'] * rectangles_standardized['height']\n", + "target_area = rectangles_standardized['original_area'].median()\n", + "rectangles_standardized['scaling_factor'] = (target_area / rectangles_standardized['original_area']) ** 0.5\n", + "rectangles_standardized['width'] = rectangles_standardized['width'] * rectangles_standardized['scaling_factor']\n", + "rectangles_standardized['height'] = rectangles_standardized['height'] * rectangles_standardized['scaling_factor']\n", + "\n", + "# We drop the temporary columns used for calculations\n", + "rectangles_standardized.drop(columns=['original_area', 'scaling_factor'], inplace=True)\n", + "\n", + "\n", + "display(rectangles_standardized.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#We can plot the initial layout with pylabeladjust:\n", + "\n", + "from pylabeladjust import adjust_labels,adjust_texts,plot_rectangles\n", + "fig, ax = plt.subplots(figsize=(10,10))\n", + "plot_rectangles(ax,rectangles_standardized,color= '#ab0b00')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d3ddb034c22c4cdca18092c0b84af6aa", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/250 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10,10))\n", + "plot_rectangles(ax,rectangles_adjusted,color= '#ab0b00')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/hz/4s_tpd8s47xfzz7rhhfjdbb00000gn/T/ipykernel_35274/3152037373.py:5: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['id'] = list(embeddings.keys())\n" + ] + } + ], + "source": [ + "# export to csv for visualization with vikus-viewer\n", + "import pandas as pd\n", + "\n", + "df = rectangles_adjusted[['x', 'y']]\n", + "df['id'] = list(embeddings.keys())\n", + "df.to_csv(\"projection.csv\")" + ] + } + ], + "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": "3.11.5" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +}