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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "# @title 1. Install Dependencies\n",
        "!pip install -q autodistill autodistill-grounded-sam supervision roboflow nvidia-ml-py psutil"
      ],
      "metadata": {
        "id": "EIs7ZOOccoTf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Grounding DINO + SAM2"
      ],
      "metadata": {
        "id": "d1r0Hp8XNz1F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title 2. Auto-Annotate with Hardware Stats (Fixed)\n",
        "import cv2\n",
        "import time\n",
        "import threading\n",
        "import psutil\n",
        "import pynvml\n",
        "import numpy as np\n",
        "import supervision as sv\n",
        "from autodistill_grounded_sam import GroundedSAM\n",
        "from autodistill.detection import CaptionOntology\n",
        "from google.colab.patches import cv2_imshow\n",
        "\n",
        "# --- CONFIGURATION ---\n",
        "IMAGE_PATH = \"/content/image_1.jpg\"\n",
        "ONTOLOGY = CaptionOntology({\n",
        "    \"shingled roof\": \"roof\",\n",
        "    \"house rooftop\": \"roof\",\n",
        "    \"car\": \"car\"\n",
        "})\n",
        "# ---------------------\n",
        "\n",
        "class HardwareMonitor:\n",
        "    def __init__(self):\n",
        "        self.running = False\n",
        "        self.stats = {\n",
        "            'cpu_usage': [],\n",
        "            'ram_used_gb': [],\n",
        "            'gpu_util': [],\n",
        "            'gpu_mem_gb': []\n",
        "        }\n",
        "        self.has_gpu = False\n",
        "        self.gpu_name = \"Unknown GPU\"\n",
        "\n",
        "        # Initialize GPU Monitor with stricter checks\n",
        "        try:\n",
        "            pynvml.nvmlInit()\n",
        "            self.handle = pynvml.nvmlDeviceGetHandleByIndex(0)\n",
        "            name = pynvml.nvmlDeviceGetName(self.handle)\n",
        "            # Handle bytes vs string decoding safely\n",
        "            if isinstance(name, bytes):\n",
        "                self.gpu_name = name.decode('utf-8')\n",
        "            else:\n",
        "                self.gpu_name = name\n",
        "\n",
        "            # Only set this to True if everything above succeeded\n",
        "            self.has_gpu = True\n",
        "        except Exception as e:\n",
        "            self.has_gpu = False # Force False on any error\n",
        "            print(f\"Hardware Monitor: GPU not active or accessible ({e}). Using CPU stats only.\")\n",
        "\n",
        "    def _monitor_loop(self):\n",
        "        while self.running:\n",
        "            try:\n",
        "                # CPU & RAM\n",
        "                self.stats['cpu_usage'].append(psutil.cpu_percent(interval=None))\n",
        "                self.stats['ram_used_gb'].append(psutil.virtual_memory().used / (1024**3))\n",
        "\n",
        "                # GPU\n",
        "                if self.has_gpu:\n",
        "                    util = pynvml.nvmlDeviceGetUtilizationRates(self.handle)\n",
        "                    mem = pynvml.nvmlDeviceGetMemoryInfo(self.handle)\n",
        "                    self.stats['gpu_util'].append(util.gpu)\n",
        "                    self.stats['gpu_mem_gb'].append(mem.used / (1024**3))\n",
        "            except:\n",
        "                pass # Ignore monitoring glitches\n",
        "            time.sleep(0.1)\n",
        "\n",
        "    def start(self):\n",
        "        self.running = True\n",
        "        self.thread = threading.Thread(target=self._monitor_loop)\n",
        "        self.thread.start()\n",
        "\n",
        "    def stop(self):\n",
        "        self.running = False\n",
        "        if hasattr(self, 'thread'):\n",
        "            self.thread.join()\n",
        "\n",
        "    def report(self, duration):\n",
        "        print(f\"\\n{'='*10} HARDWARE PERFORMANCE REPORT {'='*10}\")\n",
        "        print(f\"⏱️  Time Taken:       {duration:.4f} seconds\")\n",
        "\n",
        "        # CPU\n",
        "        if self.stats['cpu_usage']:\n",
        "            avg_cpu = np.mean(self.stats['cpu_usage'])\n",
        "            max_cpu = np.max(self.stats['cpu_usage'])\n",
        "            print(f\"💻 Avg CPU Usage:    {avg_cpu:.1f}% (Max: {max_cpu:.1f}%)\")\n",
        "            print(f\"🧠 RAM Usage:        {np.max(self.stats['ram_used_gb']):.2f} GB (Peak)\")\n",
        "\n",
        "        # GPU\n",
        "        if self.has_gpu and self.stats['gpu_util']:\n",
        "            avg_gpu = np.mean(self.stats['gpu_util'])\n",
        "            max_gpu = np.max(self.stats['gpu_util'])\n",
        "            max_vram = np.max(self.stats['gpu_mem_gb'])\n",
        "            print(f\"🎮 GPU Model:        {self.gpu_name}\")\n",
        "            print(f\"⚡ Avg GPU Util:     {avg_gpu:.1f}% (Max: {max_gpu:.1f}%)\")\n",
        "            print(f\"💾 VRAM Usage:       {max_vram:.2f} GB (Peak)\")\n",
        "        print(\"=\"*48 + \"\\n\")\n",
        "\n",
        "# --- MAIN EXECUTION ---\n",
        "\n",
        "print(\"Loading Grounding DINO + SAM...\")\n",
        "base_model = GroundedSAM(ontology=ONTOLOGY)\n",
        "\n",
        "monitor = HardwareMonitor()\n",
        "\n",
        "print(f\"Processing {IMAGE_PATH}...\")\n",
        "start_time = time.time()\n",
        "monitor.start()\n",
        "\n",
        "try:\n",
        "    predictions = base_model.predict(IMAGE_PATH)\n",
        "finally:\n",
        "    monitor.stop()\n",
        "    end_time = time.time()\n",
        "\n",
        "monitor.report(end_time - start_time)\n",
        "\n",
        "# Visualization\n",
        "print(\"Generating image...\")\n",
        "image = cv2.imread(IMAGE_PATH)\n",
        "labels = [\n",
        "    f\"{base_model.ontology.classes()[class_id]} {confidence:0.8f}\"\n",
        "    for class_id, confidence\n",
        "    in zip(predictions.class_id, predictions.confidence)\n",
        "]\n",
        "\n",
        "box_annotator = sv.BoxAnnotator()\n",
        "annotated_image = box_annotator.annotate(scene=image.copy(), detections=predictions)\n",
        "\n",
        "label_annotator = sv.LabelAnnotator()\n",
        "annotated_image = label_annotator.annotate(scene=annotated_image, detections=predictions, labels=labels)\n",
        "\n",
        "cv2_imshow(annotated_image)\n",
        "cv2.imwrite(\"performance_annotated.jpg\", annotated_image)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "fg-GlRskcrD9",
        "outputId": "79e1c977-53eb-4352-fbd7-c99914d2151a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading Grounding DINO + SAM...\n",
            "trying to load grounding dino directly\n",
            "final text_encoder_type: bert-base-uncased\n",
            "Processing /content/image_1.jpg...\n",
            "\n",
            "========== HARDWARE PERFORMANCE REPORT ==========\n",
            "⏱️  Time Taken:       3.9749 seconds\n",
            "💻 Avg CPU Usage:    76.8% (Max: 100.0%)\n",
            "🧠 RAM Usage:        3.65 GB (Peak)\n",
            "🎮 GPU Model:        Tesla T4\n",
            "⚡ Avg GPU Util:     84.5% (Max: 99.0%)\n",
            "💾 VRAM Usage:       10.34 GB (Peak)\n",
            "================================================\n",
            "\n",
            "Generating image...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=874x1425>"
            ],
            "image/png": "[encoded data, 2279440 characters]\n",
            "image/jpeg": "[encoded data, 296572 characters]\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Moondream V2 Testing"
      ],
      "metadata": {
        "id": "Irlh-0IxkP0q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pillow torchao"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ve4x0APdgk_0",
        "outputId": "faa38850-b593-464d-ebc7-c34d7200d042"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pillow in /usr/local/lib/python3.12/dist-packages (11.3.0)\n",
            "Requirement already satisfied: torchao in /usr/local/lib/python3.12/dist-packages (0.10.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Run Moondream (Stable Float16 Version)\n",
        "import torch\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "from PIL import Image\n",
        "\n",
        "# 1. Load the Standard Model (Not the 4-bit specific repo)\n",
        "# We use torch.float16 which fits easily on T4 GPU (approx 5GB VRAM usage)\n",
        "model_id = \"vikhyatk/moondream2\"\n",
        "\n",
        "print(f\"Loading {model_id} in float16...\")\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_id,\n",
        "    trust_remote_code=True,\n",
        "    torch_dtype=torch.float16, # <--- The fix: Use standard FP16\n",
        "    device_map={\"\": \"cuda\"}\n",
        ")\n",
        "\n",
        "# 2. Load Image\n",
        "image_path = \"/content/image_1.jpg\"\n",
        "image = Image.open(image_path)\n",
        "\n",
        "# 3. Run Inference\n",
        "\n",
        "# Captioning\n",
        "print(\"\\n--- Short Caption ---\")\n",
        "print(model.caption(image, length=\"short\")[\"caption\"])\n",
        "\n",
        "print(\"\\n--- Normal Caption ---\")\n",
        "print(model.caption(image, length=\"normal\")[\"caption\"])\n",
        "\n",
        "# Visual Querying\n",
        "print(\"\\n--- Visual Query ---\")\n",
        "question = \"How many people are in the image?\"\n",
        "# Note: Newer moondream versions often prefer 'query' or 'answer_question'\n",
        "try:\n",
        "    answer = model.query(image, question)[\"answer\"]\n",
        "except:\n",
        "    # Fallback if API changed in the base model\n",
        "    encoded_image = model.encode_image(image)\n",
        "    answer = model.answer_question(encoded_image, question, tokenizer=None)\n",
        "print(f\"Q: {question}\\nA: {answer}\")\n",
        "\n",
        "# Object Detection\n",
        "print(\"\\n--- Object Detection ---\")\n",
        "try:\n",
        "    # Detect 'face' (or 'car', 'person' etc)\n",
        "    target = \"face\"\n",
        "    detection_result = model.detect(image, target)\n",
        "    objects = detection_result[\"objects\"]\n",
        "    print(f\"Found {len(objects)} {target}(s)\")\n",
        "\n",
        "    # Print first few matches\n",
        "    for i, obj in enumerate(objects[:3]):\n",
        "        print(f\" {i+1}. {obj}\")\n",
        "except Exception as e:\n",
        "    print(f\"Detection not supported by this specific model revision: {e}\")\n",
        "\n",
        "# Pointing\n",
        "print(\"\\n--- Pointing ---\")\n",
        "try:\n",
        "    target = \"person\"\n",
        "    pointing_result = model.point(image, target)\n",
        "    points = pointing_result[\"points\"]\n",
        "    print(f\"Found {len(points)} point(s) for '{target}'\")\n",
        "except Exception as e:\n",
        "    print(f\"Pointing not supported by this specific model revision: {e}\")"
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        "id": "UfINsRXEcy2x",
        "outputId": "dd0ffbe4-d5a1-45cc-aaad-18140920e479"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading vikhyatk/moondream2 in float16...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/277 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9d933ecc089f498fae586ecb7fd9be87"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "hf_moondream.py: 0.00B [00:00, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "577085fe62c847468e746028821b137b"
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/vikhyatk/moondream2:\n",
            "- config.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/vikhyatk/moondream2:\n",
            "- image_crops.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/vikhyatk/moondream2:\n",
            "- lora.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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        {
          "output_type": "stream",
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          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/vikhyatk/moondream2:\n",
            "- layers.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n",
            "A new version of the following files was downloaded from https://huggingface.co/vikhyatk/moondream2:\n",
            "- vision.py\n",
            "- layers.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/vikhyatk/moondream2:\n",
            "- utils.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/vikhyatk/moondream2:\n",
            "- region.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/vikhyatk/moondream2:\n",
            "- rope.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n",
            "A new version of the following files was downloaded from https://huggingface.co/vikhyatk/moondream2:\n",
            "- text.py\n",
            "- rope.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n",
            "A new version of the following files was downloaded from https://huggingface.co/vikhyatk/moondream2:\n",
            "- moondream.py\n",
            "- config.py\n",
            "- image_crops.py\n",
            "- lora.py\n",
            "- vision.py\n",
            "- utils.py\n",
            "- region.py\n",
            "- text.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n",
            "A new version of the following files was downloaded from https://huggingface.co/vikhyatk/moondream2:\n",
            "- hf_moondream.py\n",
            "- moondream.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n",
            "`torch_dtype` is deprecated! Use `dtype` instead!\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "model.safetensors:   0%|          | 0.00/3.85G [00:00<?, ?B/s]"
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          "text": [
            "\n",
            "--- Short Caption ---\n",
            "A two-story gray house with a gray roof, white garage door, and white picket fence is situated in a suburban neighborhood, with a car parked in the driveway and a bare tree nearby.\n",
            "\n",
            "--- Normal Caption ---\n",
            "A gray house with a dark gray or black roof stands as the central focus in a residential area. The house is situated in a residential neighborhood with mostly bare trees, suggesting it might be winter or early spring. Two cars are parked in the driveway, which also has a portion of a garage or carport. A chain-link fence separates the house from a paved area that appears to be a driveway or street. Other houses in the area share a similar architectural style with some gray roofs. The neighborhood also features bare trees, a paved street, and utility poles and wires.\n",
            "\n",
            "--- Visual Query ---\n",
            "Q: How many people are in the image?\n",
            "A: There are no people in the image.\n",
            "\n",
            "--- Object Detection ---\n",
            "Found 0 face(s)\n",
            "\n",
            "--- Pointing ---\n",
            "Found 0 point(s) for 'person'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Run Moondream (Stable Float16 Version)\n",
        "import torch\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "from PIL import Image\n",
        "\n",
        "# 1. Load the Standard Model (Not the 4-bit specific repo)\n",
        "# We use torch.float16 which fits easily on T4 GPU (approx 5GB VRAM usage)\n",
        "model_id = \"vikhyatk/moondream2\"\n",
        "\n",
        "print(f\"Loading {model_id} in float16...\")\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_id,\n",
        "    trust_remote_code=True,\n",
        "    torch_dtype=torch.float16, # <--- The fix: Use standard FP16\n",
        "    device_map={\"\": \"cuda\"}\n",
        ")\n",
        "\n",
        "# 2. Load Image\n",
        "image_path = \"/content/image_1.jpg\"\n",
        "image = Image.open(image_path)\n",
        "\n",
        "# 3. Run Inference\n",
        "\n",
        "# # Captioning\n",
        "# print(\"\\n--- Short Caption ---\")\n",
        "# print(model.caption(image, length=\"short\")[\"caption\"])\n",
        "\n",
        "# print(\"\\n--- Normal Caption ---\")\n",
        "# print(model.caption(image, length=\"normal\")[\"caption\"])\n",
        "\n",
        "# # Visual Querying\n",
        "# print(\"\\n--- Visual Query ---\")\n",
        "# question = \"How many people are in the image?\"\n",
        "# # Note: Newer moondream versions often prefer 'query' or 'answer_question'\n",
        "# try:\n",
        "#     answer = model.query(image, question)[\"answer\"]\n",
        "# except:\n",
        "#     # Fallback if API changed in the base model\n",
        "#     encoded_image = model.encode_image(image)\n",
        "#     answer = model.answer_question(encoded_image, question, tokenizer=None)\n",
        "# print(f\"Q: {question}\\nA: {answer}\")\n",
        "\n",
        "# Object Detection\n",
        "print(\"\\n--- Object Detection ---\")\n",
        "try:\n",
        "    # Detect 'face' (or 'car', 'person' etc)\n",
        "    target = \"rooftop\"\n",
        "    detection_result = model.detect(image, target)\n",
        "    objects = detection_result[\"objects\"]\n",
        "    print(f\"Found {len(objects)} {target}(s)\")\n",
        "\n",
        "    # Print first few matches\n",
        "    for i, obj in enumerate(objects[:3]):\n",
        "        print(f\" {i+1}. {obj}\")\n",
        "except Exception as e:\n",
        "    print(f\"Detection not supported by this specific model revision: {e}\")\n",
        "\n",
        "# Pointing\n",
        "# print(\"\\n--- Pointing ---\")\n",
        "# try:\n",
        "#     target = \"person\"\n",
        "#     pointing_result = model.point(image, target)\n",
        "#     points = pointing_result[\"points\"]\n",
        "#     print(f\"Found {len(points)} point(s) for '{target}'\")\n",
        "# except Exception as e:\n",
        "#     print(f\"Pointing not supported by this specific model revision: {e}\")"
      ],
      "metadata": {
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        "outputId": "6ba182c5-43f9-49aa-eb07-8bb0a43d8217"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading vikhyatk/moondream2 in float16...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/277 [00:00<?, ?B/s]"
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          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/vikhyatk/moondream2:\n",
            "- layers.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
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        },
        {
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          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/vikhyatk/moondream2:\n",
            "- config.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
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          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/vikhyatk/moondream2:\n",
            "- image_crops.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n",
            "A new version of the following files was downloaded from https://huggingface.co/vikhyatk/moondream2:\n",
            "- vision.py\n",
            "- image_crops.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
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          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/vikhyatk/moondream2:\n",
            "- utils.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
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          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/vikhyatk/moondream2:\n",
            "- lora.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/vikhyatk/moondream2:\n",
            "- rope.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n",
            "A new version of the following files was downloaded from https://huggingface.co/vikhyatk/moondream2:\n",
            "- text.py\n",
            "- rope.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
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        {
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        {
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          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/vikhyatk/moondream2:\n",
            "- region.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n",
            "A new version of the following files was downloaded from https://huggingface.co/vikhyatk/moondream2:\n",
            "- moondream.py\n",
            "- layers.py\n",
            "- config.py\n",
            "- vision.py\n",
            "- utils.py\n",
            "- lora.py\n",
            "- text.py\n",
            "- region.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n",
            "A new version of the following files was downloaded from https://huggingface.co/vikhyatk/moondream2:\n",
            "- hf_moondream.py\n",
            "- moondream.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n",
            "`torch_dtype` is deprecated! Use `dtype` instead!\n"
          ]
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        {
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        {
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          "text": [
            "\n",
            "--- Object Detection ---\n",
            "Found 5 rooftop(s)\n",
            " 1. {'x_min': 0.2136922925710678, 'y_min': 0.5827633067965508, 'x_max': 0.4991983324289322, 'y_max': 0.7297366932034492}\n",
            " 2. {'x_min': 0.30088645219802856, 'y_min': 0.23957382142543793, 'x_max': 0.7596604228019714, 'y_max': 0.4323011785745621}\n",
            " 3. {'x_min': 0.48814424127340317, 'y_min': 0.43696559593081474, 'x_max': 0.6622463837265968, 'y_max': 0.5395969040691853}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title 4. Visualization (Overlay Bounding Boxes)\n",
        "import cv2\n",
        "import numpy as np\n",
        "from google.colab.patches import cv2_imshow\n",
        "\n",
        "# 1. Convert PIL Image to OpenCV Format\n",
        "# Moondream uses PIL (RGB), OpenCV uses BGR, so we convert it\n",
        "open_cv_image = np.array(image)\n",
        "open_cv_image = cv2.cvtColor(open_cv_image, cv2.COLOR_RGB2BGR)\n",
        "\n",
        "# Get dimensions to calculate pixel coordinates\n",
        "height, width, _ = open_cv_image.shape\n",
        "\n",
        "print(f\"Drawing {len(objects)} boxes on image size: {width}x{height}...\")\n",
        "\n",
        "# 2. Iterate and Draw\n",
        "for i, obj in enumerate(objects):\n",
        "    # Convert normalized (0-1) to pixels\n",
        "    x1 = int(obj['x_min'] * width)\n",
        "    y1 = int(obj['y_min'] * height)\n",
        "    x2 = int(obj['x_max'] * width)\n",
        "    y2 = int(obj['y_max'] * height)\n",
        "\n",
        "    # Draw Rectangle (Green, thickness=2)\n",
        "    cv2.rectangle(open_cv_image, (x1, y1), (x2, y2), (0, 255, 0), 2)\n",
        "\n",
        "    # Add Label\n",
        "    label_text = f\"Rooftop #{i+1}\"\n",
        "\n",
        "    # Text background (for better visibility)\n",
        "    (text_w, text_h), _ = cv2.getTextSize(label_text, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 2)\n",
        "    cv2.rectangle(open_cv_image, (x1, y1 - 20), (x1 + text_w, y1), (0, 255, 0), -1)\n",
        "\n",
        "    # Text\n",
        "    cv2.putText(open_cv_image, label_text, (x1, y1 - 5),\n",
        "                cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)\n",
        "\n",
        "# 3. Display and Save\n",
        "cv2_imshow(open_cv_image)\n",
        "cv2.imwrite(\"rooftop_annotated.jpg\", open_cv_image)\n",
        "print(\"Saved to rooftop_annotated.jpg\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "wXyhBLFhjFIW",
        "outputId": "7f6b65eb-8ff6-4398-c476-b72e604d5b24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drawing 5 boxes on image size: 874x1425...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=874x1425>"
            ],
            "image/png": "[encoded data, 2309612 characters]\n",
            "image/jpeg": "[encoded data, 299852 characters]\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved to rooftop_annotated.jpg\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title 1. Install Segment Anything (SAM)\n",
        "import sys\n",
        "print(\"Installed and downloaded weights.\")"
      ],
      "metadata": {
        "id": "SoQtryJKkZ5e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q segment-anything torchvision\n",
        "!wget -q https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth"
      ],
      "metadata": {
        "id": "Zi4VBEwmmKxX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # @title Fix: Re-download SAM Weights\n",
        "# import os\n",
        "\n",
        "# # 1. Delete the corrupted file\n",
        "# if os.path.exists(\"sam_vit_h_4b8939.pth\"):\n",
        "#     print(\"Deleting corrupted file...\")\n",
        "#     os.remove(\"sam_vit_h_4b8939.pth\")\n",
        "\n",
        "# # 2. Download again (approx 2.4 GB)\n",
        "# print(\"Downloading SAM weights (this may take 1-2 minutes)...\")\n",
        "# !wget -q https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth\n",
        "\n",
        "# # 3. Verify file size (Should be ~2.5 GB)\n",
        "# file_size = os.path.getsize(\"sam_vit_h_4b8939.pth\") / (1024 * 1024)\n",
        "# print(f\"Download complete. File size: {file_size:.2f} MB\")\n",
        "\n",
        "# if file_size < 2000:\n",
        "#     print(\"⚠️ WARNING: File looks too small. The download might have failed again.\")\n",
        "# else:\n",
        "#     print(\"✅ File looks good. You can run the segmentation code below.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "86aOsAUDmSUl",
        "outputId": "35c078f0-807f-4ffb-def7-c3912a9853b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Deleting corrupted file...\n",
            "Downloading SAM weights (this may take 1-2 minutes)...\n",
            "Download complete. File size: 2445.75 MB\n",
            "✅ File looks good. You can run the segmentation code below.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title 2. Generate \"Hugging\" Contours (Segmentation)\n",
        "import torch\n",
        "import cv2\n",
        "import numpy as np\n",
        "from segment_anything import sam_model_registry, SamPredictor\n",
        "from google.colab.patches import cv2_imshow\n",
        "\n",
        "# --- CONFIGURATION ---\n",
        "# We use the 'Huge' model (vit_h) for best possible edge adherence\n",
        "SAM_CHECKPOINT = \"sam_vit_h_4b8939.pth\"\n",
        "MODEL_TYPE = \"vit_h\"\n",
        "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "\n",
        "# 1. Initialize SAM\n",
        "print(f\"Loading SAM ({MODEL_TYPE})...\")\n",
        "sam = sam_model_registry[MODEL_TYPE](checkpoint=SAM_CHECKPOINT)\n",
        "sam.to(device=DEVICE)\n",
        "predictor = SamPredictor(sam)\n",
        "\n",
        "# 2. Prepare Image\n",
        "# SAM expects RGB (0-255)\n",
        "image_rgb = np.array(image)\n",
        "predictor.set_image(image_rgb)\n",
        "\n",
        "# 3. Process Moondream Boxes\n",
        "# Moondream gave us normalized (0-1) boxes: {'x_min': 0.2, ...}\n",
        "# SAM needs absolute pixels: [x1, y1, x2, y2]\n",
        "height, width = image_rgb.shape[:2]\n",
        "input_boxes = []\n",
        "\n",
        "for obj in objects:\n",
        "    x1 = int(obj['x_min'] * width)\n",
        "    y1 = int(obj['y_min'] * height)\n",
        "    x2 = int(obj['x_max'] * width)\n",
        "    y2 = int(obj['y_max'] * height)\n",
        "    input_boxes.append([x1, y1, x2, y2])\n",
        "\n",
        "input_boxes_tensor = torch.tensor(input_boxes, device=DEVICE)\n",
        "transformed_boxes = predictor.transform.apply_boxes_torch(input_boxes_tensor, image_rgb.shape[:2])\n",
        "\n",
        "# 4. Predict Masks\n",
        "# We feed all boxes at once for speed\n",
        "print(f\"Segmenting {len(input_boxes)} rooftops...\")\n",
        "masks, _, _ = predictor.predict_torch(\n",
        "    point_coords=None,\n",
        "    point_labels=None,\n",
        "    boxes=transformed_boxes,\n",
        "    multimask_output=False,\n",
        ")\n",
        "\n",
        "# 5. Draw the \"Hugging Lines\" (Contours)\n",
        "output_image = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2BGR)\n",
        "overlay = output_image.copy()\n",
        "\n",
        "# Iterate through the generated masks\n",
        "for i, mask in enumerate(masks):\n",
        "    # Mask is a boolean tensor, convert to numpy uint8\n",
        "    mask_np = mask[0].cpu().numpy().astype(np.uint8)\n",
        "\n",
        "    # A. Draw Filled Semi-Transparent Layer\n",
        "    # Color: Green (0, 255, 0)\n",
        "    color = (0, 255, 0)\n",
        "    output_image[mask_np > 0] = color\n",
        "\n",
        "    # B. Draw the \"Hugging Line\" (Contour)\n",
        "    contours, _ = cv2.findContours(mask_np, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
        "    # Thickness = 2, Color = White (for contrast)\n",
        "    cv2.drawContours(overlay, contours, -1, (255, 255, 255), 2)\n",
        "\n",
        "# Blend for transparency effect (0.6 original + 0.4 overlay)\n",
        "final_image = cv2.addWeighted(overlay, 1.0, output_image, 0.4, 0)\n",
        "\n",
        "# 6. Display\n",
        "cv2_imshow(final_image)\n",
        "cv2.imwrite(\"rooftop_segmented.jpg\", final_image)\n",
        "print(\"Saved result to 'rooftop_segmented.jpg'\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "sqHxwDFOmLa0",
        "outputId": "49cebe4b-09da-446f-a7c6-8ec8c6388fff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mThis cell output is too large and can only be displayed while logged in.\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "S8DbH1iOmlF8"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}