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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Created TensorFlow Lite XNNPACK delegate for CPU.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from f_segment_img import *\n",
    "from f_measurents import *\n",
    "import gradio as gr\n",
    "import dotenv\n",
    "import ast\n",
    "dotenv.load_dotenv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_sam():\n",
    "    sam_checkpoint = \"sam_vit_h_4b8939.pth\"\n",
    "    model_type = \"vit_h\"; device = \"cuda\"\n",
    "    sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)\n",
    "    return sam\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plt2arr(fig, draw=True):\n",
    "    if draw: fig.canvas.draw()\n",
    "    rgba_buf = fig.canvas.buffer_rgba()\n",
    "    (w,h) = fig.canvas.get_width_height()\n",
    "    rgba_arr = np.frombuffer(rgba_buf, dtype=np.uint8).reshape((h,w,4))\n",
    "    return rgba_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def frame_size_width_mm(dropdown_label):\n",
    "    if dropdown_label == 'Small (142 mm)': frame_width_px = 142\n",
    "    elif dropdown_label == 'Medium (xx mm)': frame_width_px = 150\n",
    "    elif dropdown_label == 'Large (xx mm)': frame_width_px = 155\n",
    "    return frame_width_px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ipd_app(image,dropdown_label):\n",
    "    # \n",
    "    landmarks = ast.literal_eval(os.environ['landmarks'])\n",
    "    frame_processed, measurements = measure_landmarks_img(image, landmarks, plot_landmarks_on_img = True, plot_data_on_img = True)\n",
    "    # \n",
    "    image, img_cropped, masks_selection, objects_segmented = segment_frame_from_img(image, landmarks, create_sam())\n",
    "    # \n",
    "    frame_width_px = get_frame_width(masks_selection)\n",
    "    frame_width_mm = frame_size_width_mm(dropdown_label)\n",
    "    ipd_mm = ipd_calibration(measurements['ipd_px'], frame_width_px, frame_width_mm)\n",
    "    text_ipd = 'IPD: ' + str(round(ipd_mm,2)) + ' mm'\n",
    "    # \n",
    "    sam_check = plot_sam_check_segmentation_frame(image, img_cropped, objects_segmented)\n",
    "    sam_check_numpy = plt2arr(sam_check, draw = True)\n",
    "    # \n",
    "    return text_ipd, frame_processed, str(measurements), sam_check_numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_test = '/Users/danielfiuzadosil/Documents/GitHub_Repo/Bryant_Medical/eCommerce/App_IPD [Master]/ipd_app/data/raw/segmentation/sample_w_frames.jpeg'\n",
    "image = cv2.cvtColor(cv2.imread(image_test),cv2.COLOR_BGR2RGB)\n",
    "dropdown_label = \"Small (xx mm)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/lib/python3.9/site-packages/gradio/deprecation.py:43: UserWarning: You have unused kwarg parameters in Dropdown, please remove them: {'info': 'For calibration'}\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IMPORTANT: You are using gradio version 3.7, however version 3.14.0 is available, please upgrade.\n",
      "--------\n",
      "Running on local URL:  http://127.0.0.1:7860\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7860/\" width=\"900\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dropdown = gr.Dropdown([\"Small (142 mm)\", \"Medium (xx mm)\", \"Large (xx mm)\"], label=\"Refractives Frame Size\", info=\"For calibration\")\n",
    "demo = gr.Interface(fn=ipd_app, inputs=[\"image\",dropdown], outputs=[\"text\", \"image\", \"text\", \"image\"])\n",
    "demo.launch(debug=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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