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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2023-10-03 09:26:15.259389: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
"To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
"2023-10-03 09:26:16.687334: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
]
}
],
"source": [
"import os\n",
"from deepface import DeepFace"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"dir_path = '/fsx/homes/mshuvi/IP-Adapter/maayan_face_aligned'\n",
"img_path = 'assets/my_imgs/maayan_face.jpg'\n",
"test_files = sorted(os.listdir(dir_path))\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"models = [\n",
" \"VGG-Face\", \n",
" \"Facenet\", \n",
" \"Facenet512\", \n",
" \"OpenFace\", \n",
" \"DeepFace\", \n",
" \"DeepID\", \n",
" \"ArcFace\", \n",
" \"Dlib\", \n",
" \"SFace\",\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'models' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m/fsx/homes/mshuvi/IP-Adapter/face_recon.ipynb Cell 4\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell://ssh-remote%2Bafruchtman-a100-genapparel.generative-ai-prod.snapaws/fsx/homes/mshuvi/IP-Adapter/face_recon.ipynb#W3sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m model \u001b[39m=\u001b[39m models[\u001b[39m2\u001b[39m]\n",
"\u001b[0;31mNameError\u001b[0m: name 'models' is not defined"
]
}
],
"source": [
"model = models[2]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"ename": "",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
]
}
],
"source": [
"dict = {}\n",
"for f in test_files:\n",
" cur_path = os.path.join(dir_path, f)\n",
" if not f in dict.keys():\n",
" try:\n",
" result = DeepFace.verify(img1_path = img_path, img2_path = cur_path, model_name = model)\n",
" dict[f] = result\n",
" print(f\"{f}: verified: {result['verified']} distance: {result['distance']}\" )\n",
" except:\n",
" print(f\"{f}: FACE NOT FOUND\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dict"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame.from_dict(dict, orient='index')\n",
"df = df.sort_values(by=['distance'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df[:10]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df[-10:]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"# open a file for writing\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"class CustomJSONizer(json.JSONEncoder):\n",
" def default(self, obj):\n",
" return super().encode(bool(obj)) \\\n",
" if isinstance(obj, np.bool_) \\\n",
" else super().default(obj)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with open('kobi_face_similarity.json', 'w') as f:\n",
" # write the dictionary to the file in JSON format\n",
" json.dump(dict, f, cls=CustomJSONizer)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dir_path = '/fsx/homes/mshuvi/IP-Adapter/maayan_face'\n",
"img_path = '/fsx/homes/mshuvi/IP-Adapter/assets/my_imgs/maayan_face.jpg'\n",
"test_files = sorted(os.listdir(dir_path))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dict_maayan = {}\n",
"for f in test_files:\n",
" cur_path = os.path.join(dir_path, f)\n",
" if not f in dict_maayan.keys():\n",
" try:\n",
" result = DeepFace.verify(img1_path = img_path, img2_path = cur_path, model_name = model, enforce_detection=False)\n",
" dict_maayan[f] = result\n",
" print(f\"{f}: verified: {result['verified']} distance: {result['distance']}\" )\n",
" except:\n",
" print(f\"{f}: FACE NOT FOUND\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_maayan = pd.DataFrame.from_dict(dict_maayan, orient='index')\n",
"df_maayan = df_maayan.sort_values(by=['distance'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with open('maayan_face_similarity.json', 'w') as f:\n",
" # write the dictionary to the file in JSON format\n",
" json.dump(dict_maayan, f, cls=CustomJSONizer)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_maayan[-10:]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dir_path = '/fsx/homes/mshuvi/IP-Adapter/maayan_face_nonplus'\n",
"img_path = '/fsx/homes/mshuvi/IP-Adapter/assets/my_imgs/maayan_face.jpg'\n",
"test_files = sorted(os.listdir(dir_path))\n",
"\n",
"dict_maayan = {}\n",
"for f in test_files:\n",
" cur_path = os.path.join(dir_path, f)\n",
" if not f in dict_maayan.keys():\n",
" try:\n",
" result = DeepFace.verify(img1_path = img_path, img2_path = cur_path, model_name = model, enforce_detection=False)\n",
" dict_maayan[f] = result\n",
" print(f\"{f}: verified: {result['verified']} distance: {result['distance']}\" )\n",
" except:\n",
" print(f\"{f}: FACE NOT FOUND\")\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_maayan = pd.DataFrame.from_dict(dict_maayan, orient='index')\n",
"df_maayan = df_maayan.sort_values(by=['distance'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_maayan[-10:]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "ms_env",
"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": "3.10.12"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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