{
"cells": [
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"# Imports\n",
"from PIL import Image, ImageFilter\n",
"import numpy as np\n",
"from transformers import pipeline\n",
"import gradio as gr\n",
"import os\n",
"\n",
"model = pipeline(\"image-segmentation\", model=\"facebook/detr-resnet-50-panoptic\")\n",
"\n",
"pred = []\n",
"\n",
"def img_resize(image):\n",
" width = 1000\n",
" width_percent = (width / float(image.size[0]))\n",
" height = int((float(image.size[1]) * float(width_percent)))\n",
" return image.resize((width, height))\n",
"\n",
"def image_objects(image):\n",
" global pred\n",
" image = img_resize(image)\n",
" pred = model(image)\n",
" pred_object_list = [str(i)+'_'+x['label'] for i, x in enumerate(pred)]\n",
" return gr.Dropdown.update(choices = pred_object_list, interactive = True)\n",
"\n",
"def blurr_object(image, object, blur_strength):\n",
" image = img_resize(image)\n",
"\n",
" object_number = int(object.split('_')[0])\n",
" mask_array = np.asarray(pred[object_number]['mask'])/255\n",
" image_array = np.asarray(image)\n",
"\n",
" mask_array_three_channel = np.zeros_like(image_array)\n",
" mask_array_three_channel[:,:,0] = mask_array\n",
" mask_array_three_channel[:,:,1] = mask_array\n",
" mask_array_three_channel[:,:,2] = mask_array\n",
"\n",
" segmented_image = image_array*mask_array_three_channel\n",
"\n",
" blur_image = np.asarray(image.filter(ImageFilter.GaussianBlur(radius=blur_strength)))\n",
" mask_array_three_channel_invert = 1-mask_array_three_channel\n",
" blur_image_reverse_mask = blur_image*mask_array_three_channel_invert\n",
"\n",
" blurred_output_image = Image.fromarray((blur_image_reverse_mask).astype(np.uint8)+segmented_image.astype(np.uint8))\n",
"\n",
" return blurred_output_image\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7862/\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Traceback (most recent call last):\n",
" File \"/home/nybsysml/anaconda3/envs/mediapipe3_7/lib/python3.7/site-packages/gradio/routes.py\", line 256, in run_predict\n",
" fn_index, raw_input, username, session_state\n",
" File \"/home/nybsysml/anaconda3/envs/mediapipe3_7/lib/python3.7/site-packages/gradio/blocks.py\", line 599, in process_api\n",
" predictions, duration = await self.call_function(fn_index, processed_input)\n",
" File \"/home/nybsysml/anaconda3/envs/mediapipe3_7/lib/python3.7/site-packages/gradio/blocks.py\", line 515, in call_function\n",
" block_fn.fn, *processed_input, limiter=self.limiter\n",
" File \"/home/nybsysml/anaconda3/envs/mediapipe3_7/lib/python3.7/site-packages/anyio/to_thread.py\", line 32, in run_sync\n",
" func, *args, cancellable=cancellable, limiter=limiter\n",
" File \"/home/nybsysml/anaconda3/envs/mediapipe3_7/lib/python3.7/site-packages/anyio/_backends/_asyncio.py\", line 937, in run_sync_in_worker_thread\n",
" return await future\n",
" File \"/home/nybsysml/anaconda3/envs/mediapipe3_7/lib/python3.7/site-packages/anyio/_backends/_asyncio.py\", line 867, in run\n",
" result = context.run(func, *args)\n",
" File \"/tmp/ipykernel_6335/646230931.py\", line 20, in image_objects\n",
" image = img_resize(image)\n",
" File \"/tmp/ipykernel_6335/646230931.py\", line 14, in img_resize\n",
" width_percent = (width / float(image.size[0]))\n",
"AttributeError: 'NoneType' object has no attribute 'size'\n"
]
}
],
"source": [
"app = gr.Blocks()\n",
"\n",
"\n",
"with app:\n",
" gr.Markdown(\n",
" \"\"\"\n",
" ## Portrait Photo Generator\n",
" - Create stunning portrait photos by blurring the background of your selected object.\n",
" - Adjust the blurring strength using the slider.\n",
" \"\"\")\n",
" with gr.Row():\n",
" with gr.Column():\n",
" gr.Markdown(\n",
" \"\"\"\n",
" ### Input Image\n",
" \"\"\")\n",
" image_input = gr.Image(type=\"pil\")\n",
" \n",
"\n",
" with gr.Column():\n",
" with gr.Row():\n",
" gr.Markdown(\n",
" \"\"\"\n",
" ### Found Objects\n",
" \"\"\")\n",
" with gr.Row():\n",
" blur_slider = gr.Slider(minimum=0.5, maximum=10, value=3, label=\"Adject Blur Strength\")\n",
" with gr.Row():\n",
" object_output = gr.Dropdown(label=\"Select Object From Dropdown\")\n",
"\n",
" \n",
" with gr.Row():\n",
" with gr.Column():\n",
" gr.Markdown(\n",
" \"\"\"\n",
" ### Blurred Image Output\n",
" \"\"\")\n",
" image_output = gr.Image()\n",
" with gr.Column():\n",
" gr.Markdown(\n",
" \"\"\"\n",
" ### Example Images\n",
" \"\"\")\n",
" gr.Examples(\n",
" examples=[\n",
" \"test_images/dog_horse_cowboy.jpg\",\n",
" \"test_images/woman_and_dog.jpg\",\n",
" \"test_images/family_in_sofa.jpg\",\n",
" \"test_images/group_of_friends.jpg\",\n",
" \"test_images/people_group.jpg\"\n",
" ],\n",
" fn=image_objects,\n",
" inputs=image_input, \n",
" outputs=object_output)\n",
"\n",
" image_input.change(fn=image_objects, \n",
" inputs=image_input, \n",
" outputs=object_output\n",
" )\n",
"\n",
" object_output.change(fn=blurr_object, \n",
" inputs=[image_input, object_output, blur_slider],\n",
" outputs=image_output)\n",
"\n",
" blur_slider.change(fn=blurr_object, \n",
" inputs=[image_input, object_output, blur_slider],\n",
" outputs=image_output)\n",
" \n",
"\n",
"app.launch(debug=True)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.9.7 ('base')",
"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": "3.9.7"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "d03b47a5a9bfc09262d9700ac41deff9ead9ff3e21241983433bdf2900140cd2"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}