demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yadonglu/anaconda3/envs/omni2/lib/python3.12/site-packages/ultralytics/nn/tasks.py:714: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  ckpt = torch.load(file, map_location=\"cpu\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "YOLO(\n",
       "  (model): DetectionModel(\n",
       "    (model): Sequential(\n",
       "      (0): Conv(\n",
       "        (conv): Conv2d(3, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn): BatchNorm2d(16, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "        (act): SiLU(inplace=True)\n",
       "      )\n",
       "      (1): Conv(\n",
       "        (conv): Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "        (act): SiLU(inplace=True)\n",
       "      )\n",
       "      (2): C2f(\n",
       "        (cv1): Conv(\n",
       "          (conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (cv2): Conv(\n",
       "          (conv): Conv2d(48, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (m): ModuleList(\n",
       "          (0): Bottleneck(\n",
       "            (cv1): Conv(\n",
       "              (conv): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (bn): BatchNorm2d(16, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "              (act): SiLU(inplace=True)\n",
       "            )\n",
       "            (cv2): Conv(\n",
       "              (conv): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (bn): BatchNorm2d(16, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "              (act): SiLU(inplace=True)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (3): Conv(\n",
       "        (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "        (act): SiLU(inplace=True)\n",
       "      )\n",
       "      (4): C2f(\n",
       "        (cv1): Conv(\n",
       "          (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (cv2): Conv(\n",
       "          (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (m): ModuleList(\n",
       "          (0-1): 2 x Bottleneck(\n",
       "            (cv1): Conv(\n",
       "              (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "              (act): SiLU(inplace=True)\n",
       "            )\n",
       "            (cv2): Conv(\n",
       "              (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "              (act): SiLU(inplace=True)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (5): Conv(\n",
       "        (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "        (act): SiLU(inplace=True)\n",
       "      )\n",
       "      (6): C2f(\n",
       "        (cv1): Conv(\n",
       "          (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (cv2): Conv(\n",
       "          (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (m): ModuleList(\n",
       "          (0-1): 2 x Bottleneck(\n",
       "            (cv1): Conv(\n",
       "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "              (act): SiLU(inplace=True)\n",
       "            )\n",
       "            (cv2): Conv(\n",
       "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "              (act): SiLU(inplace=True)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (7): Conv(\n",
       "        (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "        (act): SiLU(inplace=True)\n",
       "      )\n",
       "      (8): C2f(\n",
       "        (cv1): Conv(\n",
       "          (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (cv2): Conv(\n",
       "          (conv): Conv2d(384, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (m): ModuleList(\n",
       "          (0): Bottleneck(\n",
       "            (cv1): Conv(\n",
       "              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "              (act): SiLU(inplace=True)\n",
       "            )\n",
       "            (cv2): Conv(\n",
       "              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "              (act): SiLU(inplace=True)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (9): SPPF(\n",
       "        (cv1): Conv(\n",
       "          (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (cv2): Conv(\n",
       "          (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (m): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False)\n",
       "      )\n",
       "      (10): Upsample(scale_factor=2.0, mode='nearest')\n",
       "      (11): Concat()\n",
       "      (12): C2f(\n",
       "        (cv1): Conv(\n",
       "          (conv): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (cv2): Conv(\n",
       "          (conv): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (m): ModuleList(\n",
       "          (0): Bottleneck(\n",
       "            (cv1): Conv(\n",
       "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "              (act): SiLU(inplace=True)\n",
       "            )\n",
       "            (cv2): Conv(\n",
       "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "              (act): SiLU(inplace=True)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (13): Upsample(scale_factor=2.0, mode='nearest')\n",
       "      (14): Concat()\n",
       "      (15): C2f(\n",
       "        (cv1): Conv(\n",
       "          (conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (cv2): Conv(\n",
       "          (conv): Conv2d(96, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (m): ModuleList(\n",
       "          (0): Bottleneck(\n",
       "            (cv1): Conv(\n",
       "              (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "              (act): SiLU(inplace=True)\n",
       "            )\n",
       "            (cv2): Conv(\n",
       "              (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "              (act): SiLU(inplace=True)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (16): Conv(\n",
       "        (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "        (act): SiLU(inplace=True)\n",
       "      )\n",
       "      (17): Concat()\n",
       "      (18): C2f(\n",
       "        (cv1): Conv(\n",
       "          (conv): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (cv2): Conv(\n",
       "          (conv): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (m): ModuleList(\n",
       "          (0): Bottleneck(\n",
       "            (cv1): Conv(\n",
       "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "              (act): SiLU(inplace=True)\n",
       "            )\n",
       "            (cv2): Conv(\n",
       "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "              (act): SiLU(inplace=True)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (19): Conv(\n",
       "        (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "        (act): SiLU(inplace=True)\n",
       "      )\n",
       "      (20): Concat()\n",
       "      (21): C2f(\n",
       "        (cv1): Conv(\n",
       "          (conv): Conv2d(384, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (cv2): Conv(\n",
       "          (conv): Conv2d(384, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (m): ModuleList(\n",
       "          (0): Bottleneck(\n",
       "            (cv1): Conv(\n",
       "              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "              (act): SiLU(inplace=True)\n",
       "            )\n",
       "            (cv2): Conv(\n",
       "              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "              (act): SiLU(inplace=True)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (22): Detect(\n",
       "        (cv2): ModuleList(\n",
       "          (0): Sequential(\n",
       "            (0): Conv(\n",
       "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "              (act): SiLU(inplace=True)\n",
       "            )\n",
       "            (1): Conv(\n",
       "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "              (act): SiLU(inplace=True)\n",
       "            )\n",
       "            (2): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))\n",
       "          )\n",
       "          (1): Sequential(\n",
       "            (0): Conv(\n",
       "              (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "              (act): SiLU(inplace=True)\n",
       "            )\n",
       "            (1): Conv(\n",
       "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "              (act): SiLU(inplace=True)\n",
       "            )\n",
       "            (2): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))\n",
       "          )\n",
       "          (2): Sequential(\n",
       "            (0): Conv(\n",
       "              (conv): Conv2d(256, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "              (act): SiLU(inplace=True)\n",
       "            )\n",
       "            (1): Conv(\n",
       "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "              (act): SiLU(inplace=True)\n",
       "            )\n",
       "            (2): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))\n",
       "          )\n",
       "        )\n",
       "        (cv3): ModuleList(\n",
       "          (0): Sequential(\n",
       "            (0): Conv(\n",
       "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "              (act): SiLU(inplace=True)\n",
       "            )\n",
       "            (1): Conv(\n",
       "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "              (act): SiLU(inplace=True)\n",
       "            )\n",
       "            (2): Conv2d(64, 1, kernel_size=(1, 1), stride=(1, 1))\n",
       "          )\n",
       "          (1): Sequential(\n",
       "            (0): Conv(\n",
       "              (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "              (act): SiLU(inplace=True)\n",
       "            )\n",
       "            (1): Conv(\n",
       "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "              (act): SiLU(inplace=True)\n",
       "            )\n",
       "            (2): Conv2d(64, 1, kernel_size=(1, 1), stride=(1, 1))\n",
       "          )\n",
       "          (2): Sequential(\n",
       "            (0): Conv(\n",
       "              (conv): Conv2d(256, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "              (act): SiLU(inplace=True)\n",
       "            )\n",
       "            (1): Conv(\n",
       "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
       "              (act): SiLU(inplace=True)\n",
       "            )\n",
       "            (2): Conv2d(64, 1, kernel_size=(1, 1), stride=(1, 1))\n",
       "          )\n",
       "        )\n",
       "        (dfl): DFL(\n",
       "          (conv): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from utils import get_som_labeled_img, check_ocr_box, get_caption_model_processor, get_yolo_model\n",
    "import torch\n",
    "from ultralytics import YOLO\n",
    "from PIL import Image\n",
    "device = 'cuda'\n",
    "\n",
    "som_model = get_yolo_model(model_path='weights/icon_detect/best.pt')\n",
    "som_model.to(device)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yadonglu/anaconda3/envs/omni2/lib/python3.12/site-packages/timm/models/layers/__init__.py:48: FutureWarning: Importing from timm.models.layers is deprecated, please import via timm.layers\n",
      "  warnings.warn(f\"Importing from {__name__} is deprecated, please import via timm.layers\", FutureWarning)\n",
      "Florence2LanguageForConditionalGeneration has generative capabilities, as `prepare_inputs_for_generation` is explicitly overwritten. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.\n",
      "  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes\n",
      "  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).\n",
      "  - If you are not the owner of the model architecture class, please contact the model code owner to update it.\n"
     ]
    }
   ],
   "source": [
    "# two choices for caption model: fine-tuned blip2 or florence2\n",
    "\n",
    "# caption_model_processor = get_caption_model_processor(model_name=\"blip2\", model_name_or_path=\"weights/icon_caption_blip2\", device=device)\n",
    "caption_model_processor = get_caption_model_processor(model_name=\"florence2\", model_name_or_path=\"weights/icon_caption_florence\", device=device)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(device(type='cuda', index=0), ultralytics.models.yolo.model.YOLO)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "som_model.device, type(som_model) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "image 1/1 /home/yadonglu/OmniParser/imgs/windows_multitab.png: 448x640 215 0s, 5.5ms\n",
      "Speed: 1.9ms preprocess, 5.5ms inference, 1.1ms postprocess per image at shape (1, 3, 448, 640)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "cnt = 0\n",
    "image_path = 'imgs/google_page.png'\n",
    "# image_path = 'imgs/windows_home.png'\n",
    "image_path = 'imgs/windows_multitab.png'\n",
    "draw_bbox_config = {\n",
    "    'text_scale': 0.8,\n",
    "    'text_thickness': 2,\n",
    "    'text_padding': 3,\n",
    "    'thickness': 3,\n",
    "}\n",
    "BOX_TRESHOLD = 0.03\n",
    "\n",
    "image = Image.open(image_path)\n",
    "image_rgb = image.convert('RGB')\n",
    "\n",
    "ocr_bbox_rslt, is_goal_filtered = check_ocr_box(image_path, display_img = False, output_bb_format='xyxy', goal_filtering=None, easyocr_args={'paragraph': False, 'text_threshold':0.9})\n",
    "text, ocr_bbox = ocr_bbox_rslt\n",
    "\n",
    "dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image_path, som_model, BOX_TRESHOLD = BOX_TRESHOLD, output_coord_in_ratio=False, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=caption_model_processor, ocr_text=text,use_local_semantics=True, iou_threshold=0.1)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Text Box ID 0: Task Manager',\n",
       " 'Text Box ID 1: storage',\n",
       " 'Text Box ID 2: InPrivate',\n",
       " 'Text Box ID 3: Google',\n",
       " 'Text Box ID 4: https:/ WWW.googlecom',\n",
       " 'Text Box ID 5: Processes',\n",
       " 'Text Box ID 6: Run new task',\n",
       " 'Text Box ID 7: End task',\n",
       " 'Text Box ID 8: New folder',\n",
       " 'Text Box ID 9: Google',\n",
       " 'Text Box ID 10: finetune/ldm-ft__',\n",
       " 'Text Box ID 11: Gradio',\n",
       " 'Text Box ID 12: Pipelines',\n",
       " 'Text Box ID 13: Recent',\n",
       " 'Text Box ID 14: haotian-liu/LLaVA:',\n",
       " 'Text Box ID 15: Processes',\n",
       " 'Text Box ID 16: 67%',\n",
       " 'Text Box ID 17: 5496',\n",
       " 'Text Box ID 18: Status',\n",
       " 'Text Box ID 19: CPU',\n",
       " 'Text Box ID 20: Memory',\n",
       " 'Text Box ID 21: Disk',\n",
       " 'Text Box ID 22: About',\n",
       " 'Text Box ID 23: Store',\n",
       " 'Text Box ID 24: Gmail',\n",
       " 'Text Box ID 25: Images',\n",
       " 'Text Box ID 26: Sign in',\n",
       " 'Text Box ID 27: Performance',\n",
       " 'Text Box ID 28: Microscft',\n",
       " 'Text Box ID 29: 15.686',\n",
       " 'Text Box ID 30: 1,829,9 MB',\n",
       " 'Text Box ID 31: MBI:',\n",
       " 'Text Box ID 32: Microscft Tean;',\n",
       " 'Text Box ID 33: 142.9MB',\n",
       " 'Text Box ID 34: MBY:',\n",
       " 'Text Box ID 35: App history',\n",
       " 'Text Box ID 36: Microsoft Azure Storage Explo_',\n",
       " 'Text Box ID 37: Efficiency_',\n",
       " 'Text Box ID 38: 0.83',\n",
       " 'Text Box ID 39: 245,0 MB',\n",
       " 'Text Box ID 40: MB/s',\n",
       " 'Text Box ID 41: Startup apps',\n",
       " 'Text Box ID 42: WebViewz Manager',\n",
       " 'Text Box ID 43: 75,9MB',\n",
       " 'Text Box ID 44: MBY:',\n",
       " 'Text Box ID 45: Users',\n",
       " 'Text Box ID 46: Service Host: Storage Service',\n",
       " 'Text Box ID 47: 096',\n",
       " 'Text Box ID 48: 1,1 MB',\n",
       " 'Text Box ID 49: MBY:',\n",
       " 'Text Box ID 50: Details',\n",
       " 'Text Box ID 51: Services',\n",
       " 'Text Box ID 52: Google',\n",
       " 'Text Box ID 53: Google Search',\n",
       " 'Text Box ID 54: Feeling Lucky',\n",
       " 'Text Box ID 55: Discover',\n",
       " 'Text Box ID 56: the ways Chrome keeps you safe while you browse',\n",
       " 'Text Box ID 57: Our third decade of climate action: join us',\n",
       " 'Text Box ID 58: Settings',\n",
       " 'Text Box ID 59: Advertising',\n",
       " 'Text Box ID 60: Business',\n",
       " 'Text Box ID 61: How Search works',\n",
       " 'Text Box ID 62: Privacy',\n",
       " 'Text Box ID 63: Terms',\n",
       " 'Text Box ID 64: Settings',\n",
       " 'Text Box ID 65: 3.53 PM',\n",
       " 'Text Box ID 66: Search',\n",
       " 'Text Box ID 67: Microsoft',\n",
       " 'Text Box ID 68: 10/25/2024',\n",
       " 'Text Box ID 69: Edge',\n",
       " 'Icon Box ID 70: Microsoft Edge browser.',\n",
       " 'Icon Box ID 71: Microsoft 365.',\n",
       " 'Icon Box ID 72: Settings',\n",
       " 'Icon Box ID 73: Image',\n",
       " 'Icon Box ID 74: Image',\n",
       " 'Icon Box ID 75: Microsoft Edge browser.',\n",
       " 'Icon Box ID 76: Microsoft Edge browser.',\n",
       " 'Icon Box ID 77: Teams.',\n",
       " 'Icon Box ID 78: Uncomm&ent Selection',\n",
       " 'Icon Box ID 79: Microsoft OneNote.',\n",
       " 'Icon Box ID 80: Find',\n",
       " 'Icon Box ID 81: Microsoft Outlook.',\n",
       " 'Icon Box ID 82: Image',\n",
       " 'Icon Box ID 83: Maximize',\n",
       " 'Icon Box ID 84: Close',\n",
       " 'Icon Box ID 85: Dictate',\n",
       " 'Icon Box ID 86: Line Spacing',\n",
       " 'Icon Box ID 87: creating a new document or file.',\n",
       " 'Icon Box ID 88: Five-point star',\n",
       " 'Icon Box ID 89: a search function.',\n",
       " 'Icon Box ID 90: Increase',\n",
       " 'Icon Box ID 91: More options',\n",
       " 'Icon Box ID 92: the Windows operating system.',\n",
       " 'Icon Box ID 93: Hyperlink',\n",
       " 'Icon Box ID 94: App launcher or menu.',\n",
       " 'Icon Box ID 95: Health monitoring',\n",
       " 'Icon Box ID 96: Microsoft Outlook.',\n",
       " 'Icon Box ID 97: minimizing a window.',\n",
       " 'Icon Box ID 98: Undo',\n",
       " 'Icon Box ID 99: uBlock Origin (Ctrl+T)',\n",
       " 'Icon Box ID 100: Pentagon',\n",
       " 'Icon Box ID 101: Settings',\n",
       " 'Icon Box ID 102: 1.0%',\n",
       " 'Icon Box ID 103: Back',\n",
       " 'Icon Box ID 104: Rectangle',\n",
       " 'Icon Box ID 105: minimizing a window.',\n",
       " 'Icon Box ID 106: Redo',\n",
       " 'Icon Box ID 107: opening a folder.',\n",
       " 'Icon Box ID 108: Justified',\n",
       " 'Icon Box ID 109: a loading or progress bar.',\n",
       " 'Icon Box ID 110: Label',\n",
       " 'Icon Box ID 111: Google.',\n",
       " 'Icon Box ID 112: Maximize window',\n",
       " 'Icon Box ID 113: Close',\n",
       " 'Icon Box ID 114: Close',\n",
       " 'Icon Box ID 115: Google Chrome web browser.']"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print(label_coordinates)\n",
    "parsed_content_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fc0c41e2b10>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot dino_labled_img it is in base64\n",
    "import base64\n",
    "import matplotlib.pyplot as plt\n",
    "import io\n",
    "plt.figure(figsize=(12,12))\n",
    "\n",
    "image = Image.open(io.BytesIO(base64.b64decode(dino_labled_img)))\n",
    "plt.axis('off')\n",
    "\n",
    "plt.imshow(image)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pilot",
   "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",
   "version": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}