nb to filter images

_dev/filter_images.ipynb

@@ -0,0 +1,333 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pipeline.clip_wrapper import ClipWrapper\n",
+    "from pipeline.process_videos import DATAFRAME_PATH\n",
+    "import pandas as pd\n",
+    "import faiss\n",
+    "import numpy as np\n",
+    "from PIL import Image\n",
+    "from io import BytesIO\n",
+    "import base64\n",
+    "from tqdm import tqdm\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_parquet(DATAFRAME_PATH)\n",
+    "dim_columns = df.filter(regex=\"^dim_\").columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "71761\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(df))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Filter images with low stds (to remove blank images)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 71761/71761 [05:17<00:00, 226.16it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "img_stds = []\n",
+    "for _, row in tqdm(df.iterrows(), total=len(df)):\n",
+    "    img = Image.open(BytesIO(base64.b64decode(row[\"base64_image\"])))\n",
+    "    img = img.resize((128, img.height * 128 // img.width))\n",
+    "    img = np.array(img)\n",
+    "    img = img.reshape(-1, 3)\n",
+    "    img_stds.append(img.std(axis=0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img_stds_mean = np.array(img_stds).mean(axis=1)\n",
+    "plt.hist(img_stds_mean, bins=200)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "69821\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_ = df.copy()\n",
+    "# filter out images with std < 2\n",
+    "df_[\"img_std\"] = img_stds_mean\n",
+    "df_ = df_[df_[\"img_std\"] > 5]\n",
+    "print(len(df_))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# overwrite the dataframe\n",
+    "df_.to_parquet(DATAFRAME_PATH, index=False)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## WIP below"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embedder = ClipWrapper().texts2vec\n",
+    "metadata = df.drop(columns=dim_columns)\n",
+    "index = faiss.IndexFlatIP(len(dim_columns))\n",
+    "index.add(np.ascontiguousarray(df[dim_columns].to_numpy(np.float32)))\n",
+    "\n",
+    "def search(query: str, k=5):\n",
+    "    v = embedder([query]).detach().numpy()\n",
+    "    (distances,), (indices,) = index.search(v, k)\n",
+    "    return distances, indices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_clusters = 128\n",
+    "num_imgs_to_show = 10\n",
+    "\n",
+    "kmeans = faiss.Kmeans(len(dim_columns), num_clusters, niter=20, verbose=True)\n",
+    "kmeans.train(df[dim_columns].to_numpy(np.float32))\n",
+    "\n",
+    "# print 4 images from each cluster \n",
+    "for i in range(num_clusters):\n",
+    "    # get the closest images to the cluster center\n",
+    "    _, (indices,) = index.search(kmeans.centroids[i].reshape(1, -1), num_imgs_to_show)\n",
+    "\n",
+    "    # get the metadata for the closest images\n",
+    "    closest = metadata.iloc[indices]\n",
+    "\n",
+    "    print(f\"Cluster {i}\")\n",
+    "\n",
+    "    # display the images\n",
+    "    imgs = []\n",
+    "    for _, row in closest.iterrows():\n",
+    "        img = Image.open(BytesIO(base64.b64decode(row[\"base64_image\"])))\n",
+    "        imgs.append(img.resize((128, img.height * 128 // img.width)))\n",
+    "    # combine the images into a single image\n",
+    "    widths, heights = zip(*(i.size for i in imgs))\n",
+    "    total_width = sum(widths)\n",
+    "    max_height = max(heights)\n",
+    "    new_im = Image.new('RGB', (total_width, max_height))\n",
+    "    x_offset = 0\n",
+    "    for im in imgs:\n",
+    "        new_im.paste(im, (x_offset,0))\n",
+    "        x_offset += im.size[0]\n",
+    "    display(new_im)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "similar_clusters = []\n",
+    "# identify clusters where the images are all very similar\n",
+    "for i in range(num_clusters):\n",
+    "    # get the closest images to the cluster center\n",
+    "    _, (indices,) = index.search(kmeans.centroids[i].reshape(1, -1), 1000)\n",
+    "    # get the metadata for the closest images\n",
+    "    closest = df.iloc[indices]\n",
+    "    # get the distances between the images and the cluster center\n",
+    "    distances = np.linalg.norm(closest[dim_columns] - kmeans.centroids[i], axis=1)\n",
+    "    # if all the distances are very small, then the images are all very similar\n",
+    "    if np.all(distances < 0.3):\n",
+    "        similar_clusters.append(i)\n",
+    "        print(f\"Cluster {i} is very similar\")\n",
+    "        closest = metadata.iloc[indices]\n",
+    "        imgs = []\n",
+    "        for _, row in closest.iterrows():\n",
+    "            img = Image.open(BytesIO(base64.b64decode(row[\"base64_image\"])))\n",
+    "            imgs.append(img.resize((128, img.height * 128 // img.width)))\n",
+    "        widths, heights = zip(*(i.size for i in imgs))\n",
+    "        total_width = sum(widths)\n",
+    "        max_height = max(heights)\n",
+    "        new_im = Image.new('RGB', (total_width, max_height))\n",
+    "        x_offset = 0\n",
+    "        for im in imgs:\n",
+    "            new_im.paste(im, (x_offset,0))\n",
+    "            x_offset += im.size[0]\n",
+    "        display(new_im)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in similar_clusters:\n",
+    "    # get the closest images to the cluster center\n",
+    "    _, (indices,) = index.search(kmeans.centroids[i].reshape(1, -1), num_imgs_to_show)\n",
+    "    # get the metadata for the closest images\n",
+    "    closest = df.iloc[indices]\n",
+    "    # get the distances between the images and the cluster center\n",
+    "    distances = np.linalg.norm(closest[dim_columns] - kmeans.centroids[i], axis=1)\n",
+    "    # if all the distances are very small, then the images are all very similar\n",
+    "    print(len(distances))\n",
+    "    # if np.all(distances < 0.2):\n",
+    "    #     similar_clusters.append(i)\n",
+    "    #     print(f\"Cluster {i} is very similar\")\n",
+    "    #     closest = metadata.iloc[indices]\n",
+    "    #     imgs = []\n",
+    "    #     for _, row in closest.iterrows():\n",
+    "    #         img = Image.open(BytesIO(base64.b64decode(row[\"base64_image\"])))\n",
+    "    #         imgs.append(img.resize((128, img.height * 128 // img.width)))\n",
+    "    #     widths, heights = zip(*(i.size for i in imgs))\n",
+    "    #     total_width = sum(widths)\n",
+    "    #     max_height = max(heights)\n",
+    "    #     new_im = Image.new('RGB', (total_width, max_height))\n",
+    "    #     x_offset = 0\n",
+    "    #     for im in imgs:\n",
+    "    #         new_im.paste(im, (x_offset,0))\n",
+    "    #         x_offset += im.size[0]\n",
+    "    #     display(new_im)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sampling a subset of 32768 / 71761 for training\n",
+      "Clustering 32768 points in 512D to 128 clusters, redo 1 times, 20 iterations\n",
+      "  Preprocessing in 0.06 s\n",
+      "  Iteration 19 (0.31 s, search 0.26 s): objective=8657.27 imbalance=1.284 nsplit=0       \n"
+     ]
+    }
+   ],
+   "source": [
+    "# cluster the vectors\n",
+    "kmeans = faiss.Kmeans(len(dim_columns), 128, niter=20, verbose=True)\n",
+    "kmeans.train(df[dim_columns].to_numpy(np.float32))\n",
+    "# D, I = kmeans.index.search(df[dim_columns].to_numpy(np.float32), 1)\n",
+    "\n",
+    "# identify the cluster of the query\n",
+    "# query = \"empty black image with nothing in it\"\n",
+    "# v = embedder([query]).detach().numpy()\n",
+    "# D, I = kmeans.index.search(v, 100)\n",
+    "\n",
+    "# print images from each cluster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# distances, indices = semantic_searcher.search(\"empty black image with nothing in it\", k=2000)\n",
+    "\n",
+    "# # for idx in indices:\n",
+    "# #     img = Image.open(BytesIO(base64.b64decode(df.iloc[idx][\"base64_image\"])))\n",
+    "# #     display(img.resize((256, img.height * 256 // img.width)))\n",
+    "# for i in range(10):\n",
+    "#     idx = indices[-1 - i]\n",
+    "#     img = Image.open(BytesIO(base64.b64decode(df.iloc[idx][\"base64_image\"])))\n",
+    "#     display(img.resize((256, img.height * 256 // img.width)))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "visual-content-search-over-videos",
+   "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": "3.9.16"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

poetry.lock

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 test = ["pytest"]
 typing = ["mypy (>=0.990)"]
 
+[[package]]
+name = "contourpy"
+version = "1.0.7"
+description = "Python library for calculating contours of 2D quadrilateral grids"
+optional = false
+python-versions = ">=3.8"
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+test-no-images = ["pytest"]
+
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+description = "Composable style cycles"
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@@ -802,6 +887,31 @@ files = [
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+
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 name = "iniconfig"
 version = "2.0.0"
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+
 [[package]]
 name = "lxml"
 version = "4.9.2"
@@ -1607,6 +1812,68 @@ files = [
     {file = "MarkupSafe-2.1.2.tar.gz", hash = "sha256:abcabc8c2b26036d62d4c746381a6f7cf60aafcc653198ad678306986b09450d"},
 ]
 
+[[package]]
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+version = "3.7.1"
+description = "Python plotting package"
+optional = false
+python-versions = ">=3.8"
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+    {file = "matplotlib-3.7.1.tar.gz", hash = "sha256:7b73305f25eab4541bd7ee0b96d87e53ae9c9f1823be5659b806cd85786fe882"},
+]
+
+[package.dependencies]
+contourpy = ">=1.0.1"
+cycler = ">=0.10"
+fonttools = ">=4.22.0"
+importlib-resources = {version = ">=3.2.0", markers = "python_version < \"3.10\""}
+kiwisolver = ">=1.0.1"
+numpy = ">=1.20"
+packaging = ">=20.0"
+pillow = ">=6.2.0"
+pyparsing = ">=2.3.1"
+python-dateutil = ">=2.7"
+
 [[package]]
 name = "matplotlib-inline"
 version = "0.1.6"
@@ -2415,6 +2682,20 @@ files = [
     {file = "Pympler-1.0.1.tar.gz", hash = "sha256:993f1a3599ca3f4fcd7160c7545ad06310c9e12f70174ae7ae8d4e25f6c5d3fa"},
 ]
 
+[[package]]
+name = "pyparsing"
+version = "3.0.9"
+description = "pyparsing module - Classes and methods to define and execute parsing grammars"
+optional = false
+python-versions = ">=3.6.8"
+files = [
+    {file = "pyparsing-3.0.9-py3-none-any.whl", hash = "sha256:5026bae9a10eeaefb61dab2f09052b9f4307d44aee4eda64b309723d8d206bbc"},
+    {file = "pyparsing-3.0.9.tar.gz", hash = "sha256:2b020ecf7d21b687f219b71ecad3631f644a47f01403fa1d1036b0c6416d70fb"},
+]
+
+[package.extras]
+diagrams = ["jinja2", "railroad-diagrams"]
+
 [[package]]
 name = "pyrsistent"
 version = "0.19.3"
@@ -3634,4 +3915,4 @@ testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.9.7 || >3.9.7,<4.0"
-content-hash = "88cd0d179ce1a3107e48826327a774c9ad6fd4b8b3af0a8a415745eaea71c389"
+content-hash = "5ac5752c981365446d25ad8e5aa465c9483cfc3f5ad749734d4f1d1101027425"

pyproject.toml

@@ -24,6 +24,7 @@ yt-dlp = "^2023.3.4"
 tqdm = "^4.65.0"
 opencv-python = "^4.7.0.72"
 youtube-dl = "^2021.12.17"
+matplotlib = "^3.7.1"
 
 [tool.poetry.group.dev.dependencies]
 notebook = "^6.5.4"