push work

code/gloss.ipynb

@@ -0,0 +1,135 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np, os, json, pandas as pd, matplotlib.pyplot as plt, seaborn as sns\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import cv2\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Creating Synthetic Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from synthetic import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Running the Classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<video width=\"320\" height=\"240\" controls>\n",
+       "  <source src=\"../data/videos/00376.mp4\" type=\"video/mp4\">\n",
+       "</video>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import IPython.display as ipd\n",
+    "class Vid:\n",
+    "    \n",
+    "    def __init__(self, video_path):\n",
+    "        self.video_path = video_path\n",
+    "        self.frames = self.read(video_path)\n",
+    "        \n",
+    "    def __len__(self):\n",
+    "        return len(self.frames)\n",
+    "    \n",
+    "    def __getitem__(self, idx):\n",
+    "        return self.frames[idx]\n",
+    "    \n",
+    "    def __iter__(self):\n",
+    "        for frame in self.frames:\n",
+    "            yield frame\n",
+    "    \n",
+    "    @staticmethod\n",
+    "    def read(video_path):\n",
+    "        cap = cv2.VideoCapture(video_path)\n",
+    "        frames = []\n",
+    "        while True:\n",
+    "            ret, frame = cap.read()\n",
+    "            if not ret:\n",
+    "                break\n",
+    "            frames.append(frame)\n",
+    "        cap.release()\n",
+    "        return np.array(frames)\n",
+    "\n",
+    "    def show(self):\n",
+    "        return VideoPlayer(self.video_path)\n",
+    "        \n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "HTML(\"\"\"<video width=\"320\" height=\"240\" controls>\n",
+    "  <source src=\"../data/videos/00376.mp4\" type=\"video/mp4\">\n",
+    "</video>\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.11.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

code/load.py

@@ -0,0 +1,34 @@
+import os
+import pandas as pd
+import numpy as np
+import cv2
+# Define the paths
+videos_dir = '../data/videos'
+labels_file = '../data/labels.csv'
+
+def read_video(video_path):
+    cap = cv2.VideoCapture(video_path)
+    frames = []
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+        frames.append(frame)
+    cap.release()
+    return np.array(frames)
+
+
+
+# Load the labels from the CSV file
+labels_df = pd.read_csv(labels_file)
+
+videos_fns = os.listdir(videos_dir)
+videos = []
+labels = []
+
+for video_fn in videos_fns:
+    video_id = int(video_fn.split('.')[0])
+    label = labels_df[labels_df['video_id'] == video_id]['label'].values[0]
+    full_fn = os.path.join(videos_dir, video_fn)
+    video = cv2.VideoCapture(full_fn)
+    labels.append(label)