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+ } + } + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "#### This notebook is created by [Nitin Tiwari](https://linkedin.com/in/tiwari-nitin).\n", + "\n", + "#### **Social links:**\n", + "* [LinkedIn](https://linkedin.com/in/tiwari-nitin)\n", + "* [GitHub](https://github.com/NSTiwari)\n", + "* [Twitter](https://x.com/NSTiwari21)" + ], + "metadata": { + "id": "sSVXJYfVvCJB" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Referring Expression Segmentation in videos" + ], + "metadata": { + "id": "W634WCRovEdo" + } + }, + { + "cell_type": "markdown", + "source": [ + "This notebook guides you to perform referring expression segmentation on videos using [PaliGemma](https://ai.google.dev/gemma/docs/paligemma) and draw the inferences using OpenCV and PIL.\n", + "\n", + "\n", + " \n", + "
\n", + " Run in Google Colab\n", + "
" + ], + "metadata": { + "id": "NTGOdrsTvHHw" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Get access to PaliGemma\n", + "\n", + "Before using PaliGemma for the first time, you must request access to the model through Hugging Face by completing the following steps:\n", + "\n", + "1. Log in to [Hugging Face](https://huggingface.co), or create a new Hugging Face account if you don't already have one.\n", + "2. Go to the [PaliGemma model card](https://huggingface.co/google/paligemma-3b-mix-224) to get access to the model.\n", + "3. Complete the consent form and accept the terms and conditions.\n", + "\n", + "To generate a Hugging Face token, open your [**Settings** page in Hugging Face](https://huggingface.co/settings), choose **Access Tokens** option in the left pane and click **New token**. In the next window that appears, give a name to your token and choose the type as **Write** to get the write access.\n", + "\n", + "Then, in Colab, select **Secrets** (🔑) in the left pane and add your Hugging Face token. Store your Hugging Face token under the name `HF_TOKEN`." + ], + "metadata": { + "id": "X4YM125TvdjZ" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Select the runtime\n", + "\n", + "To complete this tutorial, you'll need to have a Colab runtime with sufficient resources to run the PaliGemma model. In this case, you can use a T4 GPU:\n", + "\n", + "1. In the upper-right of the Colab window, click the **▾ (Additional connection options)** dropdown menu.\n", + "1. Select **Change runtime type**.\n", + "1. Under **Hardware accelerator**, select **T4 GPU**." + ], + "metadata": { + "id": "GmCMot7Gvfcg" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Step 1: Install libraries" + ], + "metadata": { + "id": "tatYlRwvbDNY" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DeBbVm6pa-Lt", + "outputId": "89d0de6d-f2d9-45cc-c80a-689c5649a85f" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m119.8/119.8 MB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m309.4/309.4 kB\u001b[0m \u001b[31m21.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m251.6/251.6 kB\u001b[0m \u001b[31m19.2 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wcwidth in /usr/local/lib/python3.10/dist-packages (from prompt-toolkit<4.0.0,>=3.0.1->InquirerPy==0.3.4->huggingface_hub[cli]) (0.2.13)\n", + "Installing collected packages: pfzy, InquirerPy\n", + "Successfully installed InquirerPy-0.3.4 pfzy-0.3.4\n" + ] + } + ], + "source": [ + "!pip install bitsandbytes transformers accelerate peft -q\n", + "!pip3 install -q \"overrides\" \"ml_collections\" \"einops~=0.7\" \"sentencepiece\"" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 2: Import libraries" + ], + "metadata": { + "id": "H6gLDltJbTpc" + } + }, + { + "cell_type": "code", + "source": [ + "from transformers import AutoTokenizer, PaliGemmaForConditionalGeneration, PaliGemmaProcessor\n", + "import torch\n", + "import numpy as np\n", + "import cv2\n", + "import os\n", + "import re\n", + "import matplotlib.pyplot as plt\n", + "import sys\n", + "from PIL import Image, ImageDraw, ImageFont" + ], + "metadata": { + "id": "NLXxDAQvbVhk" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 3: Fetch the `big_vision` repository" + ], + "metadata": { + "id": "ShmfbLUabZHr" + } + }, + { + "cell_type": "code", + "source": [ + "if not os.path.exists(\"big_vision_repo\"):\n", + " !git clone --quiet --branch=main --depth=1 \\\n", + " https://github.com/google-research/big_vision big_vision_repo\n", + "\n", + "# Append big_vision code to Python import path.\n", + "if \"big_vision_repo\" not in sys.path:\n", + " sys.path.append(\"big_vision_repo\")" + ], + "metadata": { + "id": "6_Cd2CZEba25" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 4: Set environment variables for Hugging Face token" + ], + "metadata": { + "id": "HKJruSCIbcGb" + } + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "from google.colab import userdata\n", + "\n", + "os.environ[\"HF_TOKEN\"] = userdata.get('HF_TOKEN')" + ], + "metadata": { + "id": "0KDj2WylvoRB" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 5: Load pre-trained PaliGemma model" + ], + "metadata": { + "id": "Lpinx3KIbhMA" + } + }, + { + "cell_type": "code", + "source": [ + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "model_id = \"google/paligemma-3b-mix-224\"\n", + "model = PaliGemmaForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.bfloat16)\n", + "processor = PaliGemmaProcessor.from_pretrained(model_id)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 534, + "referenced_widgets": [ + "4eb98e73425a42f09d8166252784f226", + "3a69b195e33644a4b06c115a2824c88e", + "6a5a65af48414514aceb9e1533335e5d", + "ede4720c358349f9b8c576786a3148cd", + "fd4ec8cf4fe546b78d43b3407d6c4316", + "87831c7dfd6d43738769452387b5585b", + "0dd1ced1ac9649389a3cb72f990f9bff", + "b04f17aad8464d3c872460def659cb13", + 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np.array(masked_image.convert('RGB'))\n", + "\n", + " # Overlay the legend on the image.\n", + " legend_y = int(height * 0.03)\n", + " legend_box_width = int(width * 0.05) # Add padding for text\n", + " legend_box_height = int(height * 0.04)\n", + " for idx, (label, color) in enumerate(label_colors.items()):\n", + " legend_entry_x1 = int(width * 0.84)\n", + " legend_entry_y1 = legend_y\n", + " legend_entry_x2 = legend_entry_x1 + legend_box_width\n", + " legend_entry_y2 = legend_y + legend_box_height\n", + " cv2.rectangle(masked_output, (legend_entry_x1, legend_entry_y1), (legend_entry_x2, legend_entry_y2), color[:3], -1)\n", + "\n", + " text = label\n", + " font_scale = min(1, legend_box_height / 20)\n", + " font_thickness = max(1, int(font_scale * 2)) # Adjust font thickness proportionally\n", + " text_size = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, font_scale, font_thickness)[0]\n", + " text_x = int(width * 0.90) # Adjust for padding\n", + " text_y = int((legend_entry_y1 + legend_entry_y2)/2 + legend_box_height*0.1)\n", + "\n", + " cv2.putText(masked_output, text, (text_x, text_y), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (0, 0, 0), 2)\n", + " legend_y += legend_box_height + max(height // 200, 5)\n", + "\n", + " return masked_output\n", + "\n", + "def parse_segments(detokenized_output: str) -> tuple[np.ndarray, np.ndarray]:\n", + " matches = re.finditer(\n", + " '\\d\\d\\d\\d)>\\d\\d\\d\\d)>\\d\\d\\d\\d)>\\d\\d\\d\\d)>'\n", + " + ''.join(f'\\d\\d\\d)>' for i in range(16)),\n", + " detokenized_output,\n", + " )\n", + " boxes, segs = [], []\n", + " fmt_box = lambda x: float(x) / 1024.0\n", + " for m in matches:\n", + " d = m.groupdict()\n", + " boxes.append([fmt_box(d['y0']), fmt_box(d['x0']), fmt_box(d['y1']), fmt_box(d['x1'])])\n", + " segs.append([int(d[f's{i}']) for i in range(16)])\n", + "\n", + " coordinates = boxes[0]\n", + " mask = np.array(reconstruct_masks(np.array(segs)))\n", + "\n", + " return coordinates, mask" + ], + "metadata": { + "id": "BJH5zjHVcBBX" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 7: Configure the input video and text prompt" + ], + "metadata": { + "id": "aDqVc7zevy96" + } + }, + { + "cell_type": "code", + "source": [ + "input_video = 'input_video.mp4' # @param {type:\"string\"}\n", + "\n", + "prompt = \"segment person, mug, book\" # @param {type: \"string\"}\n", + "prompt = prompt.replace(',', '\\n')\n", + "\n", + "output_file = 'segmentation_output_video.avi' # @param {type: \"string\"}" + ], + "metadata": { + "id": "M372oAWXv3gB" + }, + "execution_count": 1, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Step 8: Pass the input video and text prompt to PaliGemma" + ], + "metadata": { + "id": "5qIdmXoAwI1q" + } + }, + { + "cell_type": "code", + "source": [ + "# Open the input video file.\n", + "cap = cv2.VideoCapture(input_video)\n", + "\n", + "# Define output video codec and file name.\n", + "fourcc = cv2.VideoWriter_fourcc(*'XVID')\n", + "\n", + "out = cv2.VideoWriter(output_file, fourcc, 20.0, (int(cap.get(3)), int(cap.get(4))))\n", + "\n", + "label_colors = {}\n", + "\n", + "while(True):\n", + " ret, frame = cap.read()\n", + " if not ret:\n", + " break\n", + "\n", + " # Convert the frame to a PIL Image.\n", + " img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n", + "\n", + " # Send text prompt and image as input.\n", + " inputs = processor(text=prompt, images=img,\n", + " padding=\"longest\", do_convert_rgb=True, return_tensors=\"pt\").to(\"cuda\")\n", + " model.to(device)\n", + " inputs = inputs.to(dtype=model.dtype)\n", + "\n", + " # Get output.\n", + " with torch.no_grad():\n", + " output = model.generate(**inputs, max_length=496)\n", + "\n", + " paligemma_response = processor.decode(output[0], skip_special_tokens=True)[len(prompt):].lstrip(\"\\n\")\n", + " detections = paligemma_response.split(\" ; \")\n", + "\n", + " # Parse the output bounding box coordinates\n", + " coordinates_list = []\n", + " labels = []\n", + " segment_masks = []\n", + "\n", + " for detection in detections:\n", + " detection = detection.split(\" \")\n", + " locations, segmentations, label = detection[0], detection[1], detection[2]\n", + " paligemma_output = locations + segmentations\n", + " bbox, seg_output = parse_segments(paligemma_output)\n", + " segment_masks.append(seg_output[0])\n", + " coordinates_list.append(bbox)\n", + " labels.append(label)\n", + "\n", + " width = img.size[0]\n", + " height = img.size[1]\n", + "\n", + " # Draw bounding boxes on the frame\n", + " image = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)\n", + "\n", + " for coordinates, label in zip(coordinates_list, labels):\n", + " output_frame = show_segmentation_output(image, segment_masks, (height, width), coordinates_list, labels)\n", + "\n", + " # Write the frame to the output video\n", + " out.write(output_frame)\n", + "\n", + " # Exit on pressing 'q'\n", + " if cv2.waitKey(1) & 0xFF == ord('q'):\n", + " break\n", + "\n", + "# Release the video capture, output video writer, and destroy the window\n", + "cap.release()\n", + "out.release()\n", + "cv2.destroyAllWindows()\n", + "print(\"Output video \" + output_file + \" saved to disk.\")" + ], + "metadata": { + "id": "pCJ63nSncZsw", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "800be38a-988b-4282-d3ae-d2a2aad92c43" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Output video segmentation_output_video.avi saved to disk.\n" + ] + } + ] + } + ] +} \ No newline at end of file