demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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": 6,
     "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",
    "# dino_model = get_dino_model(load_hf_model=True, device=device)\n",
    "som_model = get_yolo_model(model_path='omniparser/weights/best.pt')\n",
    "\n",
    "# caption_model_processor = get_caption_model_processor(\"Salesforce/blip2-opt-2.7b\", device=device)\n",
    "# caption_model_processor['model'].to(torch.float32)\n",
    "som_model.to(device)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(device(type='cuda', index=0), ultralytics.models.yolo.model.YOLO)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "som_model.device, type(som_model) #, type(dino_model['model']), isinstance(som_model, YOLO) dino_model['model'].device, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "image 1/1 /home/yadonglu/sandbox/OmniParser/imgs/pc_1.png: 800x1280 211 icons, 51.2ms\n",
      "Speed: 3.7ms preprocess, 51.2ms inference, 160.7ms postprocess per image at shape (1, 3, 800, 1280)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "platform = 'pc'\n",
    "cnt = 0\n",
    "image_path = 'imgs/pc_1.png'\n",
    "# image_path = 'imgs/mobile_4.png'\n",
    "# get dino labeled image\n",
    "if platform == 'pc':\n",
    "    draw_bbox_config = {\n",
    "        'text_scale': 0.8,\n",
    "        'text_thickness': 2,\n",
    "        'text_padding': 3,\n",
    "        'thickness': 3,\n",
    "    }\n",
    "    BOX_TRESHOLD = 0.05\n",
    "elif platform == 'web':\n",
    "    draw_bbox_config = {\n",
    "        'text_scale': 0.8,\n",
    "        'text_thickness': 2,\n",
    "        'text_padding': 3,\n",
    "        'thickness': 3,\n",
    "    }\n",
    "    BOX_TRESHOLD = 0.05\n",
    "elif platform == 'mobile':\n",
    "    draw_bbox_config = {\n",
    "        'text_scale': 0.8,\n",
    "        'text_thickness': 2,\n",
    "        'text_padding': 3,\n",
    "        'thickness': 3,\n",
    "    }\n",
    "    BOX_TRESHOLD = 0.05\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=None, ocr_text=text,use_local_semantics=False)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'0': array([         94,          38,          95,          24], dtype=float32),\n",
       "  '1': array([        628,          35,         153,          30], dtype=float32),\n",
       "  '2': array([        813,          35,         125,          27], dtype=float32),\n",
       "  '3': array([        985,          35,         100,          27], dtype=float32),\n",
       "  '4': array([       1362,          33,          85,          29], dtype=float32),\n",
       "  '5': array([       2578,          34,          77,          32], dtype=float32),\n",
       "  '6': array([         38,         114,          47,          29], dtype=float32),\n",
       "  '7': array([        129,         114,          80,          29], dtype=float32),\n",
       "  '8': array([        241,         114,          74,          29], dtype=float32),\n",
       "  '9': array([        347,         114,          70,          29], dtype=float32),\n",
       "  '10': array([        445,         113,          92,          36], dtype=float32),\n",
       "  '11': array([        570,         111,         133,          32], dtype=float32),\n",
       "  '12': array([        739,         111,         141,          32], dtype=float32),\n",
       "  '13': array([        911,         111,         138,          32], dtype=float32),\n",
       "  '14': array([       1081,         114,          87,          29], dtype=float32),\n",
       "  '15': array([       1202,         114,          90,          29], dtype=float32),\n",
       "  '16': array([       1321,         114,          65,          29], dtype=float32),\n",
       "  '17': array([       1415,         114,          62,          32], dtype=float32),\n",
       "  '18': array([       2584,         114,          88,          27], dtype=float32),\n",
       "  '19': array([       2736,         111,         204,          30], dtype=float32),\n",
       "  '20': array([       3063,         111,          72,          30], dtype=float32),\n",
       "  '21': array([        456,         176,          86,          36], dtype=float32),\n",
       "  '22': array([       1109,         180,          97,          51], dtype=float32),\n",
       "  '23': array([       1404,         184,         175,          44], dtype=float32),\n",
       "  '24': array([       2030,         174,          77,          38], dtype=float32),\n",
       "  '25': array([       2296,         177,          55,          27], dtype=float32),\n",
       "  '26': array([         51,         246,          62,          26], dtype=float32),\n",
       "  '27': array([        256,         243,          54,          27], dtype=float32),\n",
       "  '28': array([        337,         246,          67,          24], dtype=float32),\n",
       "  '29': array([        461,         230,          64,          27], dtype=float32),\n",
       "  '30': array([       1594,         222,          46,          41], dtype=float32),\n",
       "  '31': array([       1704,         241,         183,          37], dtype=float32),\n",
       "  '32': array([       1904,         243,          69,          27], dtype=float32),\n",
       "  '33': array([       2030,         226,         165,          37], dtype=float32),\n",
       "  '34': array([       2298,         230,          88,          30], dtype=float32),\n",
       "  '35': array([       2465,         243,          80,          29], dtype=float32),\n",
       "  '36': array([       2577,         238,         120,          39], dtype=float32),\n",
       "  '37': array([       2731,         243,          90,          27], dtype=float32),\n",
       "  '38': array([       2853,         243,         194,          32], dtype=float32),\n",
       "  '39': array([        726,         250,          43,          39], dtype=float32),\n",
       "  '40': array([        964,         256,          68,          29], dtype=float32),\n",
       "  '41': array([       1345,         246,         162,          51], dtype=float32),\n",
       "  '42': array([        238,         273,          62,          30], dtype=float32),\n",
       "  '43': array([        334,         273,          70,          30], dtype=float32),\n",
       "  '44': array([        458,         281,          88,          27], dtype=float32),\n",
       "  '45': array([       1891,         276,          70,          29], dtype=float32),\n",
       "  '46': array([       2106,         281,          77,          27], dtype=float32),\n",
       "  '47': array([       2296,         281,          75,          27], dtype=float32),\n",
       "  '48': array([         58,         324,          98,          27], dtype=float32),\n",
       "  '49': array([        380,         324,          59,          27], dtype=float32),\n",
       "  '50': array([        874,         328,          45,          19], dtype=float32),\n",
       "  '51': array([       1390,         324,         100,          29], dtype=float32),\n",
       "  '52': array([       1905,         322,          85,          32], dtype=float32),\n",
       "  '53': array([       2305,         322,          74,          32], dtype=float32),\n",
       "  '54': array([       2476,         324,          57,          27], dtype=float32),\n",
       "  '55': array([       2587,         324,         103,          27], dtype=float32),\n",
       "  '56': array([       2734,         324,          80,          27], dtype=float32),\n",
       "  '57': array([       1367,         978,        1067,         146], dtype=float32),\n",
       "  '58': array([       1617,        1187,         564,          63], dtype=float32),\n",
       "  '59': array([        602,        1944,         242,          32], dtype=float32),\n",
       "  '60': array([       2034,         277,          68,          39], dtype=float32),\n",
       "  '61': array([     2965.8,      11.372,       73.33,      65.827], dtype=float32),\n",
       "  '62': array([     2963.6,      104.42,      45.381,      45.756], dtype=float32),\n",
       "  '63': array([     198.97,      28.516,      79.805,      38.483], dtype=float32),\n",
       "  '64': array([     608.18,      181.26,      342.97,      50.053], dtype=float32),\n",
       "  '65': array([     1300.6,         250,      33.767,      35.427], dtype=float32),\n",
       "  '66': array([     304.13,      30.349,      37.342,      36.602], dtype=float32),\n",
       "  '67': array([     667.74,      241.55,      47.529,      53.978], dtype=float32),\n",
       "  '68': array([     822.62,       244.9,      47.754,      51.829], dtype=float32),\n",
       "  '69': array([     770.31,      244.16,      46.905,      51.143], dtype=float32),\n",
       "  '70': array([     1248.1,      251.22,       31.97,       32.78], dtype=float32),\n",
       "  '71': array([     1048.9,      244.81,      45.524,          47], dtype=float32),\n",
       "  '72': array([     438.99,      28.466,      35.963,      37.508], dtype=float32),\n",
       "  '73': array([     954.49,      181.67,      94.131,      49.485], dtype=float32),\n",
       "  '74': array([     363.65,      29.569,      32.883,      36.315], dtype=float32),\n",
       "  '75': array([     497.99,      28.813,      32.513,      34.289], dtype=float32),\n",
       "  '76': array([     1332.3,       188.4,      32.144,      34.772], dtype=float32),\n",
       "  '77': array([     1137.5,      251.92,      34.499,      34.152], dtype=float32),\n",
       "  '78': array([     880.41,      249.27,      39.986,       37.76], dtype=float32),\n",
       "  '79': array([     954.73,      33.853,      23.966,      27.856], dtype=float32),\n",
       "  '80': array([       2888,      21.725,      45.384,        44.9], dtype=float32),\n",
       "  '81': array([     1997.3,      222.31,      34.341,      34.824], dtype=float32),\n",
       "  '82': array([     625.14,      249.45,      29.211,      36.318], dtype=float32),\n",
       "  '83': array([     554.33,      25.136,       36.99,       42.99], dtype=float32),\n",
       "  '84': array([     2786.6,      22.758,      52.631,      49.663], dtype=float32),\n",
       "  '85': array([     1812.5,       176.7,      57.761,      59.091], dtype=float32),\n",
       "  '86': array([     3170.5,      26.987,      44.986,      46.376], dtype=float32),\n",
       "  '87': array([     1284.7,      182.54,      32.466,      49.088], dtype=float32),\n",
       "  '88': array([     423.68,       280.2,      28.832,      30.261], dtype=float32),\n",
       "  '89': array([     1716.8,      179.54,      59.374,      50.265], dtype=float32),\n",
       "  '90': array([      344.3,      185.95,      54.543,      42.996], dtype=float32),\n",
       "  '91': array([     1515.7,      252.05,      33.876,      33.092], dtype=float32),\n",
       "  '92': array([     1090.8,      243.67,      36.191,      50.504], dtype=float32),\n",
       "  '93': array([     1248.6,      189.02,      32.175,      33.261], dtype=float32),\n",
       "  '94': array([     963.23,      254.24,      40.774,      33.985], dtype=float32),\n",
       "  '95': array([     1717.4,      174.03,      52.504,      48.792], dtype=float32),\n",
       "  '96': array([     3075.8,      30.369,      39.574,      38.682], dtype=float32),\n",
       "  '97': array([     3187.4,      107.39,      33.734,       40.21], dtype=float32),\n",
       "  '98': array([     2966.8,       168.2,      91.261,       105.9], dtype=float32),\n",
       "  '99': array([     30.782,      33.378,        33.3,      31.807], dtype=float32),\n",
       "  '100': array([     1196.9,      324.51,      27.192,      25.869], dtype=float32),\n",
       "  '101': array([     3172.2,      310.76,      44.785,      39.261], dtype=float32),\n",
       "  '102': array([     1998.4,      173.44,      30.627,      32.274], dtype=float32),\n",
       "  '103': array([     787.33,      241.95,      64.931,      57.226], dtype=float32),\n",
       "  '104': array([     2692.7,      21.129,      51.957,      55.543], dtype=float32),\n",
       "  '105': array([     1170.2,      247.59,      34.892,      42.735], dtype=float32),\n",
       "  '106': array([     1910.2,      174.37,      58.716,      53.398], dtype=float32),\n",
       "  '107': array([     2259.9,      225.75,      31.152,       32.17], dtype=float32),\n",
       "  '108': array([     254.78,       181.7,      56.533,      44.474], dtype=float32),\n",
       "  '109': array([     1047.4,      182.75,      71.279,      49.489], dtype=float32),\n",
       "  '110': array([     424.87,      175.44,      29.783,      28.272], dtype=float32),\n",
       "  '111': array([     892.42,      233.03,      55.596,      76.345], dtype=float32),\n",
       "  '112': array([     2873.1,      180.61,      62.642,      47.626], dtype=float32),\n",
       "  '113': array([     1996.8,      278.17,      31.037,      31.622], dtype=float32),\n",
       "  '114': array([     11.883,      368.91,      564.39,      1578.2], dtype=float32),\n",
       "  '115': array([     422.86,      225.74,      29.434,      28.735], dtype=float32),\n",
       "  '116': array([     1794.2,      209.55,      92.729,       62.57], dtype=float32),\n",
       "  '117': array([     1702.6,      235.84,      84.464,      33.964], dtype=float32)},\n",
       " 'AutoSave')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_coordinates, parsed_content_list[0].split(': ')[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7c1b13b5f5b0>"
      ]
     },
     "execution_count": 9,
     "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": "markdown",
   "metadata": {},
   "source": [
    "# wrapped Omniparser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parsing image: examples/pc_1.png\n",
      "\n",
      "image 1/1 /home/yadonglu/sandbox/screenparsing_collab/screenparsing/omniparser/examples/pc_1.png: 800x1280 210 icons, 55.6ms\n",
      "Speed: 7.7ms preprocess, 55.6ms inference, 1.5ms postprocess per image at shape (1, 3, 800, 1280)\n",
      "boxes cpu\n",
      "Time taken for Omniparser on cpu: 2.029506206512451\n"
     ]
    }
   ],
   "source": [
    "from utils import get_som_labeled_img, check_ocr_box, get_caption_model_processor,  get_dino_model, get_yolo_model\n",
    "import torch\n",
    "from ultralytics import YOLO\n",
    "from PIL import Image\n",
    "from typing import Dict, Tuple, List\n",
    "import io\n",
    "import base64\n",
    "\n",
    "\n",
    "config = {\n",
    "    'som_model_path': 'finetuned_icon_detect.pt',\n",
    "    'device': 'cpu',\n",
    "    'caption_model_path': 'Salesforce/blip2-opt-2.7b',\n",
    "    'draw_bbox_config': {\n",
    "        'text_scale': 0.8,\n",
    "        'text_thickness': 2,\n",
    "        'text_padding': 3,\n",
    "        'thickness': 3,\n",
    "    },\n",
    "    'BOX_TRESHOLD': 0.05\n",
    "}\n",
    "\n",
    "\n",
    "class Omniparser(object):\n",
    "    def __init__(self, config: Dict):\n",
    "        self.config = config\n",
    "        \n",
    "        self.som_model = get_yolo_model(model_path=config['som_model_path'])\n",
    "        # self.caption_model_processor = get_caption_model_processor(config['caption_model_path'], device=cofig['device'])\n",
    "        # self.caption_model_processor['model'].to(torch.float32)\n",
    "\n",
    "    def parse(self, image_path: str):\n",
    "        print('Parsing image:', image_path)\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",
    "        draw_bbox_config = self.config['draw_bbox_config']\n",
    "        BOX_TRESHOLD = self.config['BOX_TRESHOLD']\n",
    "        dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image_path, self.som_model, BOX_TRESHOLD = BOX_TRESHOLD, output_coord_in_ratio=False, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=None, ocr_text=text,use_local_semantics=False)\n",
    "        \n",
    "        image = Image.open(io.BytesIO(base64.b64decode(dino_labled_img)))\n",
    "        # formating output\n",
    "        return_list = [{'from': 'omniparser', 'shape': {'x':coord[0], 'y':coord[1], 'width':coord[2], 'height':coord[3]},\n",
    "                        'text': parsed_content_list[i].split(': ')[1], 'type':'text'} for i, (k, coord) in enumerate(label_coordinates.items()) if i < len(parsed_content_list)]\n",
    "        return_list.extend(\n",
    "            [{'from': 'omniparser', 'shape': {'x':coord[0], 'y':coord[1], 'width':coord[2], 'height':coord[3]},\n",
    "                        'text': 'None', 'type':'icon'} for i, (k, coord) in enumerate(label_coordinates.items()) if i >= len(parsed_content_list)]\n",
    "              )\n",
    "\n",
    "        return [image, return_list]\n",
    "    \n",
    "parser = Omniparser(config)\n",
    "image_path = 'imgs/pc_1.png'\n",
    "\n",
    "#  time the parser\n",
    "import time\n",
    "s = time.time()\n",
    "image, parsed_content_list = parser.parse(image_path)\n",
    "device = config['device']\n",
    "print(f'Time taken for Omniparser on {device}:', time.time() - s)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0: 800x1280 210 icons, 49.4ms\n",
      "Speed: 5.7ms preprocess, 49.4ms inference, 1.1ms postprocess per image at shape (1, 3, 800, 1280)\n",
      "boxes cpu\n",
      "Time taken for Omniparser finetuned YOLO module on cpu: 0.2898883819580078\n"
     ]
    }
   ],
   "source": [
    "from utils import get_som_labeled_img, check_ocr_box, get_caption_model_processor,  get_dino_model, get_yolo_model, predict_yolo\n",
    "import torch\n",
    "from ultralytics import YOLO\n",
    "from PIL import Image\n",
    "from typing import Dict, Tuple, List\n",
    "import io\n",
    "import base64\n",
    "\n",
    "\n",
    "config = {\n",
    "    'som_model_path': 'finetuned_icon_detect.pt',\n",
    "    'device': 'cpu',\n",
    "    'caption_model_path': 'Salesforce/blip2-opt-2.7b',\n",
    "    'draw_bbox_config': {\n",
    "        'text_scale': 0.8,\n",
    "        'text_thickness': 2,\n",
    "        'text_padding': 3,\n",
    "        'thickness': 3,\n",
    "    },\n",
    "    'BOX_TRESHOLD': 0.05\n",
    "}\n",
    "\n",
    "class OmniparserYOLO(object):\n",
    "    def __init__(self, config: Dict):\n",
    "        self.config = config\n",
    "        self.som_model = get_yolo_model(model_path=config['som_model_path'])\n",
    "\n",
    "    def parse(self, image):\n",
    "        draw_bbox_config = self.config['draw_bbox_config']\n",
    "        BOX_TRESHOLD = self.config['BOX_TRESHOLD']\n",
    "        xyxy, logits, phrases = predict_yolo(model=self.som_model, image_path=image, box_threshold=BOX_TRESHOLD)\n",
    "        # print('xyxy:', xyxy)\n",
    "        xyxy = xyxy.tolist()\n",
    "        # formating output\n",
    "        return_list = [{'from': 'omniparserYOLO', 'shape': {'x':coord[0], 'y':coord[1], 'width':coord[2]-coord[0], 'height':coord[3] - coord[1]},\n",
    "                        'text': 'None', 'type':'icon'} for i, coord in enumerate(xyxy)]\n",
    "        \n",
    "        return [None, return_list]\n",
    "    \n",
    "parser = OmniparserYOLO(config)\n",
    "image_path = 'imgs/pc_1.png'\n",
    "image = Image.open(image_path)\n",
    "\n",
    "#  time the parser\n",
    "import time\n",
    "s = time.time()\n",
    "_, parsed_content_list = parser.parse(image)\n",
    "device = config['device']\n",
    "print(f'Time taken for Omniparser finetuned YOLO module on {device}:', time.time() - s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# florence caption model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yadonglu/anaconda3/envs/pilot/lib/python3.9/site-packages/transformers/utils/generic.py:342: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
      "  _torch_pytree._register_pytree_node(\n",
      "/home/yadonglu/anaconda3/envs/pilot/lib/python3.9/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoProcessor, AutoModelForCausalLM \n",
    "import torch\n",
    "device = 'cpu'\n",
    "torch_dtype = torch.float16 if device == 'cuda' else torch.float32\n",
    "model = AutoModelForCausalLM.from_pretrained(\"/home/yadonglu/sandbox/data/orca/florence-2-base-ft-fft_rai_win_ep5/epoch_5\", torch_dtype=torch_dtype, trust_remote_code=True).to(device)\n",
    "processor = AutoProcessor.from_pretrained(\"microsoft/Florence-2-base\", trust_remote_code=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['settings or configuration options.']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from PIL import Image\n",
    "prompt = \"<CAPTION>\"\n",
    "image_path = 'imgs/settings.png'\n",
    "image = [Image.open(image_path).convert('RGB')]\n",
    "inputs = processor(images=image, text=[prompt]*len(image), return_tensors=\"pt\").to(device=device)\n",
    "generated_ids = model.generate(input_ids=inputs[\"input_ids\"],pixel_values=inputs[\"pixel_values\"],max_new_tokens=1024,num_beams=3, do_sample=False)\n",
    "generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)\n",
    "generated_text = [gen.strip() for gen in generated_text]\n",
    "generated_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2"
   ]
  },
  {
   "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": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}