GroundingDINO_Trial2.ipynb

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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd drive/MyDrive/GroundingDINO/"
      ],
      "metadata": {
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        },
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      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/GroundingDINO\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q -e ."
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      "execution_count": 3,
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m200.9/200.9 kB\u001b[0m \u001b[31m25.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m549.1/549.1 kB\u001b[0m \u001b[31m48.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m224.5/224.5 kB\u001b[0m \u001b[31m26.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m75.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from groundingdino.util.inference import load_model, load_image, predict, annotate\n",
        "import cv2\n",
        "\n",
        "model = load_model(\"groundingdino/config/GroundingDINO_SwinT_OGC.py\", \"weights/groundingdino_swint_ogc.pth\")\n",
        "IMAGE_PATH = \"im1.jpg\"\n",
        "TEXT_PROMPT = \"persons . sofas . fans\"\n",
        "BOX_TRESHOLD = 0.35\n",
        "TEXT_TRESHOLD = 0.25\n",
        "\n",
        "image_source, image = load_image(IMAGE_PATH)\n",
        "\n",
        "boxes, logits, phrases = predict(\n",
        "    model=model,\n",
        "    image=image,\n",
        "    caption=TEXT_PROMPT,\n",
        "    box_threshold=BOX_TRESHOLD,\n",
        "    text_threshold=TEXT_TRESHOLD\n",
        ")\n",
        "\n",
        "annotated_frame = annotate(image_source=image_source, boxes=boxes, logits=logits, phrases=phrases)\n",
        "cv2.imwrite(\"annotated_image.jpg\", annotated_frame)\n"
      ],
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      "execution_count": 4,
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3483.)\n",
            "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "final text_encoder_type: bert-base-uncased\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "92111a4bf1804454860c874a7eb96248"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py:862: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
            "  warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = load_model(\"groundingdino/config/GroundingDINO_SwinT_OGC.py\", \"weights/groundingdino_swint_ogc.pth\")\n",
        "IMAGE_PATH = \"im2.jpg\"\n",
        "TEXT_PROMPT = \"water patches\"\n",
        "BOX_TRESHOLD = 0.35\n",
        "TEXT_TRESHOLD = 0.25\n",
        "\n",
        "image_source, image = load_image(IMAGE_PATH)\n",
        "\n",
        "boxes, logits, phrases = predict(\n",
        "    model=model,\n",
        "    image=image,\n",
        "    caption=TEXT_PROMPT,\n",
        "    box_threshold=BOX_TRESHOLD,\n",
        "    text_threshold=TEXT_TRESHOLD\n",
        ")\n",
        "\n",
        "annotated_frame = annotate(image_source=image_source, boxes=boxes, logits=logits, phrases=phrases)\n",
        "cv2.imwrite(\"annotated_image.jpg\", annotated_frame)\n"
      ],
      "metadata": {
        "id": "KxbH9rKcHdjn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = load_model(\"groundingdino/config/GroundingDINO_SwinT_OGC.py\", \"weights/groundingdino_swint_ogc.pth\")\n",
        "IMAGE_PATH = \"im3.jpg\"\n",
        "TEXT_PROMPT = \"Can you see any houses and mountains?\"\n",
        "BOX_TRESHOLD = 0.35\n",
        "TEXT_TRESHOLD = 0.25\n",
        "\n",
        "image_source, image = load_image(IMAGE_PATH)\n",
        "\n",
        "boxes, logits, phrases = predict(\n",
        "    model=model,\n",
        "    image=image,\n",
        "    caption=TEXT_PROMPT,\n",
        "    box_threshold=BOX_TRESHOLD,\n",
        "    text_threshold=TEXT_TRESHOLD\n",
        ")\n",
        "\n",
        "annotated_frame = annotate(image_source=image_source, boxes=boxes, logits=logits, phrases=phrases)\n",
        "cv2.imwrite(\"annotated_image.jpg\", annotated_frame)"
      ],
      "metadata": {
        "id": "3v7WroJiLTy-"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}