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{
"cells": [
{
"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": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.9.15"
},
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