{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Multistage VLM video captioning\n", "\n", "Inspired by the approach used in the [CogX Video model](https://github.com/THUDM/CogVideo) this notebook:\n", "\n", "1. Uses a VLM to generate frame-specific captions.\n", "2. Compile these captions into a textual summary using an LLM.\n", "\n", "> 💡 This method is most effective for shorter clips with minimal scene changes. \n", "\n", "CogVideoX leverages these captioned videos to train text-to-video models, and similar workflows can be applied to other multimodal generative AI applications." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 1: Set Up Environment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Installation\n", "\n", "Ensure that you install:\n", "- The `encord-agents` library.\n", "- The `openai` library." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!python -m pip install encord-agents openai" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Authentication\n", "\n", "The Encord agents library authenticates using ssh keys, and OpenAI using an API key. The following code cell for setting the `ENCORD_SSH_KEY` and `OPENAI_API_KEY`environment variables. It must contain the raw content of your private ssh key file and OpenAI API key.\n", "\n", "> - Replace `private_key_file_content` with your Encord private key.\n", "> - Replace `api_key_file_content` with your OpenAI API key.\n", "\n", "If you have not yet setup an ssh key, please follow the [documentation](https://agents-docs.encord.com/authentication/).\n", "\n", "> 💡 In the colab notebook, you can set the key once in the secrets in the left sidebar and load it in new notebooks. IF YOU ARE NOT RUNNING THE CODE IN THE COLLAB NOTEBOOK, you must set the environment variable directly.\n", "> ```python\n", "> import os\n", "> from google.colab import userdata\n", "> os.environ[\"ENCORD_SSH_KEY\"] = userdata.get(\"ENCORD_SSH_KEY\")\n", "> os.environ[\"OPENAI_API_KEY\"] = userdata.get(\"OPENAI_API_KEY\")\n", "> ```" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "os.environ[\"ENCORD_SSH_KEY\"] = \"private_key_file_content\"\n", "os.environ[\"OPENAI_API_KEY\"] = \"api_key_file_content\"\n", "\n", "# or you can set a path to a file\n", "# os.environ[\"ENCORD_SSH_KEY_FILE\"] = \"/path/to/your/private/key\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 2: Set up Encord environment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.1: Set up an Ontology\n", "\n", "Set up a simple Ontology containing:\n", "\n", "- A text classification to summerise the entire task.\n", "- A text classification to summerise a single frame." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![Screenshot 2025-02-17 at 15.32.38.png](data:image/png;base64,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)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2.2: Create a Workflow Template\n", "\n", "Employ a simple Workflow with 2 separate Agent stages. The first \"Frame Captioning\" stage summerises individual frame, and the second \"Semating Summarization\" stage summerises the entire task." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![Screenshot 2025-02-17 at 15.17.16.png](data:image/png;base64,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] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3: Defining the Agents\n", "\n", "Both agents can be defined together. When using the runner, it iterates through the defined agents, fetching all tasks at their respective stages and processing them. The system follows the natural ordering of the Workflow Graph, prioritizing earlier tasks to ensure they progress to later stages efficiently." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from dataclasses import dataclass\n", "\n", "from openai import OpenAI\n", "\n", "\n", "# Data class to hold predictions from our model\n", "@dataclass\n", "class ModelPrediction:\n", " caption: str\n", " conf: float\n", "\n", "\n", "def caption_from_openai(base64_img_url: str) -> str:\n", " openai_client = OpenAI()\n", " prompt = \"Please summarise the following frame from a video. Be succinct and start immediately. Describe the frame and only the frame directly\"\n", " response = openai_client.chat.completions.create(\n", " model=\"gpt-4o-mini\",\n", " messages=[\n", " {\n", " \"role\": \"user\",\n", " \"content\": [\n", " {\"type\": \"text\", \"text\": prompt},\n", " {\"type\": \"image_url\", \"image_url\": {\"url\": base64_img_url}},\n", " ],\n", " }\n", " ],\n", " )\n", " return response.choices[0].message.content or \"Failed to summarise\"\n", "\n", "\n", "def caption_frame(base64_img_url: str) -> ModelPrediction:\n", " caption = caption_from_openai(base64_img_url)\n", " conf = 0.5 # One could try to ask VLM on their confidence of the individual caption\n", " return ModelPrediction(caption=caption, conf=conf)\n", "\n", "\n", "model = caption_frame" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ensure that you replace `` with the unique ID of your Encord Project." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from typing import Iterable\n", "\n", "from encord.objects.classification import Classification\n", "from encord.objects.classification_instance import ClassificationInstance\n", "from encord.objects.ontology_labels_impl import LabelRowV2\n", "from encord.project import Project\n", "from typing_extensions import Annotated\n", "\n", "from encord_agents.core.data_model import Frame\n", "from encord_agents.tasks import Depends, Runner\n", "from encord_agents.tasks.dependencies import dep_video_iterator\n", "\n", "# a. Define a runner that executes the agent on every task in the agent stage\n", "runner = Runner(project_hash=\"\")\n", "\n", "\n", "# b. Specify the logic that goes into the \"pre-label\" agent node.\n", "@runner.stage(stage=\"Frame Captioning\")\n", "def caption_video(\n", " lr: LabelRowV2,\n", " project: Project,\n", " frames: Annotated[Iterable[Frame], Depends(dep_video_iterator)],\n", ") -> str:\n", " ontology = project.ontology_structure\n", " captions: dict[int, ModelPrediction] = {}\n", " # c. Loop over the frames in the video\n", " ontology_element: Classification = ontology.get_child_by_title(\"Frame by frame captioning\")\n", " for frame_idx, frame in enumerate(frames):\n", " if frame_idx % 48 != 0: # For every 48th frame in the video\n", " continue\n", " frame_dense_summarisation: ClassificationInstance = ontology_element.create_instance()\n", " frame_caption = model(frame.b64_encoding())\n", " captions[frame_idx] = frame_caption\n", " frame_dense_summarisation.set_answer(frame_caption.caption)\n", " frame_dense_summarisation.set_for_frames(frame_idx, overwrite=True)\n", " lr.add_classification_instance(frame_dense_summarisation, force=True)\n", "\n", " lr.save()\n", " return \"Captioned\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we define the summarization component of the agent. This step takes the frame-tagged captions and processes them through an LLM (which does not need to be vision-based) to generate a concise summary of the entire video. \n", "\n", "The prompt used for this process is directly influenced by the CogX paper, ensuring effective aggregation of individual frame captions into a coherent summary." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def get_prompt(dict_frame_caption: dict[int, str]) -> str:\n", " summarisation_prompt = f\"\"\"\n", " We extracted several frames from this video and described\n", " each frame using an image understanding model, stored\n", " in the dictionary variable ‘image_captions: Dict[str: str]‘.\n", " In ‘image_captions‘, the key is the frame at which the image\n", " appears in the video, and the value is a detailed description\n", " of the image at that moment. Please describe the content of\n", " this video in as much detail as possible, based on the\n", " information provided by ‘image_captions‘, including\n", " the objects, scenery, animals, characters, and camera\n", " movements within the video. \\n image_captions={dict_frame_caption}\\n\n", " You should output your summary directly, and not mention\n", " variables like ‘image_captions‘ in your response.\n", " Do not include ‘\\\\n’ and the word ’video’ in your response.\n", " Do not use introductory phrases such as: \\\"The video\n", " presents\\\", \\\"The video depicts\\\", \\\"This video showcases\\\",\n", " \\\"The video captures\\\" and so on.\\n Please start the\n", " description with the video content directly, such as \\\"A man\n", " first sits in a chair, then stands up and walks to the\n", " kitchen....\\\"\\n Do not use phrases like: \\\"as the video\n", " progressed\\\" and \\\"Throughout the video\\\".\\n\n", " the content of the video and the changes that occur, in\n", " chronological order.\\n Please keep the description of this\n", " video within 100 English words.\n", " \"\"\"\n", " return summarisation_prompt\n", "\n", "\n", "def get_summaerised_caption(dict_frame_caption: dict[int, str]) -> str:\n", " prompt = get_prompt(dict_frame_caption)\n", " openai_client = OpenAI()\n", " resp = openai_client.chat.completions.create(\n", " messages=[\n", " {\"role\": \"user\", \"content\": prompt},\n", " ],\n", " model=\"gpt-4o\",\n", " )\n", " return resp.choices[0].message.content or \"Failed to get summarisation\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "@runner.stage(stage=\"Semantic Summarization\")\n", "def summarise_captions(lr: LabelRowV2, project: Project) -> str:\n", " ontology = project.ontology_structure\n", " ontology_element: Classification = ontology.get_child_by_title(\"Frame by frame captioning\")\n", " classification_elements = lr.get_classification_instances(filter_ontology_classification=ontology_element)\n", " dict_frame_caption: dict[int, str] = {}\n", " for inst in classification_elements:\n", " anno = inst.get_annotations()[0]\n", " dict_frame_caption[anno.frame] = str(inst.get_answer())\n", " summarised_caption = get_summaerised_caption(dict_frame_caption)\n", " succint_ontology_element: Classification = ontology.get_child_by_title(\"Summarisation of entire clip\")\n", " succ_inst = succint_ontology_element.create_instance()\n", " succ_inst.set_answer(summarised_caption)\n", " succ_inst.set_for_frames(frames=0)\n", " lr.add_classification_instance(succ_inst)\n", " lr.save()\n", " return \"Summarised\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Running the Agent\n", "\n", "With the project, workflow, and agent defined, it's time to put everything into action. The `runner` object is callable, allowing you to execute it directly to prioritize and process your tasks efficiently." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Run the agent\n", "runner()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Your agent now assigns labels to videos and routes them through the workflow to the annotation stage. As a result, each annotation task includes pre-existing labels (predictions). \n", "\n", "> 💡*Hint:* To run this as a Python script, place the above code in an `agents.py` file and change: \n", "> ```python\n", "> runner()\n", "> ```\n", "> to \n", "> ```python\n", "> if __name__ == \"__main__\":\n", "> runner.run()\n", "> ```\n", "> This allows you to set parameters like the project hash using the command line: \n", "> ```bash\n", "> python agent.py --project-hash \"...\"\n", "> ```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Outcome\n", "\n", "With these video summaries generated, you can now use them to train your model. As mentioned earlier, a notable example is the CogX paper, which presents an open-source generative text-to-video model, though other approaches are also viable. \n", "\n", "Consider leveraging your internal video corpus, using this workflow to generate captions, and fine-tuning your model for your specific use case." ] } ], "metadata": { "colab": { "provenance": [], "toc_visible": true }, "kernelspec": { "display_name": "cogxvideodemo-yb5agPqU-py3.11", "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" } }, "nbformat": 4, "nbformat_minor": 0 }