{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# GSI Technology Video Search Demo - Embedding Videos Notebook:\n",
"\n",
"The following Notebook will include code that demonstrates the process of video embedding.
\n",
"It specifically focuses on embedding a single video using the [Searchium-ai/clip4clip-webvid150k](https://huggingface.co/Searchium-ai/clip4clip-webvid150k) model."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"example = './example/34721191.mp4'"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize, InterpolationMode\n",
"from PIL import Image\n",
"import cv2\n",
"import numpy as np\n",
"import torch\n",
"\n",
"# Code to convert one video to few images. \n",
"def video2image(video_path, frame_rate=1.0, size=224):\n",
" def preprocess(size, n_px):\n",
" return Compose([\n",
" Resize(size, interpolation=InterpolationMode.BICUBIC), \n",
" CenterCrop(size),\n",
" lambda image: image.convert(\"RGB\"),\n",
" ToTensor(),\n",
" Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),\n",
" ])(n_px)\n",
" \n",
" cap = cv2.VideoCapture(video_path)\n",
" cap = cv2.VideoCapture(video_path, cv2.CAP_FFMPEG)\n",
" frameCount = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
" fps = int(cap.get(cv2.CAP_PROP_FPS))\n",
" if fps < 1:\n",
" images = np.zeros([3, size, size], dtype=np.float32) \n",
" print(\"ERROR: problem reading video file: \", video_path)\n",
" else:\n",
" total_duration = (frameCount + fps - 1) // fps\n",
" start_sec, end_sec = 0, total_duration\n",
" interval = fps / frame_rate\n",
" frames_idx = np.floor(np.arange(start_sec*fps, end_sec*fps, interval))\n",
" ret = True \n",
" images = np.zeros([len(frames_idx), 3, size, size], dtype=np.float32)\n",
" \n",
" for i, idx in enumerate(frames_idx):\n",
" cap.set(cv2.CAP_PROP_POS_FRAMES , idx)\n",
" ret, frame = cap.read() \n",
" if not ret: break\n",
" frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) \n",
" last_frame = i\n",
" images[i,:,:,:] = preprocess(size, Image.fromarray(frame).convert(\"RGB\"))\n",
" \n",
" images = images[:last_frame+1]\n",
" cap.release()\n",
" video_frames = torch.tensor(images)\n",
" return video_frames\n",
" \n",
"video = video2image(example)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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" 7.1489e-03, 7.0376e-02, -4.2558e-02, -5.4193e-02, 6.0990e-03,\n",
" 1.5232e-02, 1.3667e-02, -1.5016e-02, -1.0382e-03, -6.4072e-03,\n",
" 2.3970e-03, 3.7884e-02, -1.7684e-02, 2.0192e-02, -2.1400e-02,\n",
" 1.6529e-02, 1.8982e-02, 1.6748e-02, -2.0919e-02, 1.2904e-02,\n",
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" -6.7086e-02, 2.4510e-02, 4.0885e-02, -1.6748e-03, 1.2575e-02,\n",
" -2.0675e-04, -1.1889e-02, 4.2555e-03, -2.6686e-02, -9.5006e-03,\n",
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" 5.5876e-03, 2.4495e-02, 3.9814e-03, 2.8102e-02, 4.3181e-02,\n",
" -1.7406e-02, -4.2736e-02, -8.1578e-03, -5.3989e-03, 2.9429e-03,\n",
" 4.3196e-02, -2.0857e-02, -3.0203e-02, -4.0288e-03, -4.4894e-02,\n",
" 2.7039e-02, 3.5724e-02, -1.4012e-02, -2.3949e-03, 1.4861e-02,\n",
" 3.1610e-02, 4.8555e-02, 1.8550e-02, 1.2663e-02, -6.1358e-03,\n",
" -4.1771e-02, 2.8252e-02, -1.1711e-02, -4.0601e-03, -2.9267e-02,\n",
" -3.0001e-02, 1.6215e-02], grad_fn=)\n"
]
}
],
"source": [
"from transformers import CLIPVisionModelWithProjection\n",
"\n",
"model = CLIPVisionModelWithProjection.from_pretrained(\"Searchium-ai/clip4clip-webvid150k\")\n",
"model = model.eval()\n",
"visual_output = model(video)\n",
"\n",
"# Normalizing the embeddings and calculating mean between all embeddings. \n",
"visual_output = visual_output[\"image_embeds\"]\n",
"visual_output = visual_output / visual_output.norm(dim=-1, keepdim=True)\n",
"visual_output = torch.mean(visual_output, dim=0)\n",
"visual_output = visual_output / visual_output.norm(dim=-1, keepdim=True)\n",
"print(visual_output)\n",
"\n",
" "
]
}
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