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app.py

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+import numpy as np
+import pandas as pd
+import os
+import matplotlib.pyplot as plt
+from matplotlib import image
+
+import deepface
+from deepface import DeepFace
+import gradio as gr
+from fns.utility_fns import empty_img, make_records
+
+def image_predict(mat_no_, student_name, img_):
+    mat_no_ = mat_no_.upper()
+    models = ['VGG-Face', 'Facenet', 'Facenet512', 'openFace', 'DeepFace', 'DeepId', 'ArcFace', 'Dlib', 'SFace']
+    backends = ['opencv', 'ssd', 'dlib', 'mtcnn', 'retinaface', 'mediapipe']
+    # df = pd.read_csv("records.csv")
+    df = make_records()
+    mat_nos = [i for i in df["matric number"].values]
+
+    if mat_no_ in mat_nos:
+        verified = True
+    else:
+        verified = False
+    
+    if verified:
+        df_sort = df[df["matric number"] == mat_no_]
+        imgs_ = df_sort["img paths"].values[0]
+        imgs = imgs_.split(" ")
+        h_start = round(0.05*img_.shape[0])
+        h_end = round(0.95*img_.shape[0])
+        w_start = round(0.05*img_.shape[1])
+        w_end = round(0.95*img_.shape[1])
+        img_ = img_[h_start:h_end, w_start:w_end]
+        verify_status = list()
+        for img in imgs:
+            result = DeepFace.verify(
+                img1_path = img_,
+                img2_path = img,
+                model_name = models[1],
+                distance_metric = 'cosine',
+                enforce_detection = False,
+                detector_backend = backends[0],
+                align = False,
+                threshold = .2
+            )
+            verify_status.append(result["verified"])
+
+        if True in verify_status:
+            response_ = f"{student_name} is verified and can proceed to vote"
+            img_match_id = imgs.index(True)
+            img_match = imgs[img_match_id]
+            img_match = image.imread(img_match)
+
+            # return response_, img_match
+        else:
+            response_ = f"{student_name} cannot verified as image does not match image in the Database"
+            img_match = empty_img()
+            # return response_, img_match
+    else:
+        response_ = f"Matric number of the student:{student_name} is not found in Database"
+        img_match = empty_img()
+        
+    return response_, img_match
+    
+
+
+with gr.Blocks() as demo:
+    m_no = gr.Textbox(placeholder = "Input Matric Number in the format (DEPT/YY/NNNN) here:", label = "MATRIC NO")
+    name_ = gr.Textbox(placeholder = "Input your name here", label = "Student Name".upper())
+    image_ = gr.Image(label = 'Input Image to be verified', source = "webcam")
+    output1 = gr.Textbox(label = 'Verification Response')
+    output2 = gr.Image(type = "filepath", label = "Database Image match")
+    btn = gr.Button('Verify')
+    btn.click(fn = image_predict, inputs = [m_no, name_, image_], outputs = [output1, output2])
+
+demo.launch(share = True)

records.csv

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+matric number,img paths
+CSC/19/0463,records/CSC-19-0463/photo_2024-08-02_07-38-10.jpg records/CSC-19-0463/photo_2024-08-02_07-38-19.jpg records/CSC-19-0463/photo_2024-08-02_07-38-26.jpg records/CSC-19-0463/photo_2024-08-02_07-38-32.jpg records/CSC-19-0463/photo_2024-08-02_07-38-36.jpg
+CYS/18/5874,records/CYS-18-5874/1721676589154.jpg records/CYS-18-5874/1721676589194.jpg
+CYS/18/5879,records/CYS-18-5879/1721675647292.jpg records/CYS-18-5879/1721675647368.jpg
+CYS/18/5882,records/CYS-18-5882/1721675648611.jpg records/CYS-18-5882/1721675648649.jpg
+CYS/18/5883,records/CYS-18-5883/1721676589269.jpg
+CYS/18/5884,records/CYS-18-5884/1721675648162.jpg
+CYS/18/5885,records/CYS-18-5885/1721675646760.jpg records/CYS-18-5885/1721675646862.jpg
+CYS/18/5893,records/CYS-18-5893/1721675646947.jpg records/CYS-18-5893/1721675647055.jpg
+CYS/18/5894,records/CYS-18-5894/1721675648229.jpg records/CYS-18-5894/1721675648364.jpg
+CYS/18/5898,records/CYS-18-5898/1721676589232.jpg
+CYS/18/5901,records/CYS-18-5901/1721675646243.jpg records/CYS-18-5901/1721675646342.jpg
+CYS/18/8472,records/CYS-18-8472/1721675648545.jpg
+CYS/18/8473,records/CYS-18-8473/1721675647715.jpg records/CYS-18-8473/1721675647976.jpg
+CYS/18/8477,records/CYS-18-8477/1721675647442.jpg records/CYS-18-8477/1721675647564.jpg
+CYS/18/8479,records/CYS-18-8479/1721675646498.jpg
+CYS/18/8484,records/CYS-18-8484/1721675647162.jpg

requirements.txt

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+tensorflow==2.15.0
+opencv-python
+gradio
+matplotlib
+deepface
+numpy
+pandas
+tqdm

script.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From c:\\Users\\Ayo Agbaje\\my_venv_\\lib\\site-packages\\tf_keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "import shutil\n",
+    "import tqdm\n",
+    "from tqdm.auto import trange, tqdm\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import image\n",
+    "\n",
+    "import deepface\n",
+    "from deepface import DeepFace\n",
+    "import gradio as gr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "models = ['VGG-Face', 'Facenet', 'Facenet512', 'openFace', 'DeepFace', 'DeepId', 'ArcFace', 'Dlib', 'SFace']\n",
+    "backends = ['opencv', 'ssd', 'dlib', 'mtcnn', 'retinaface', 'mediapipe']\n",
+    "\n",
+    "result = DeepFace.verify(\n",
+    "    img1_path = 'records/CYS-18-5884/1721675648162.jpg',\n",
+    "    img2_path = 'records/CYS-18-8479/1721675646498.jpg',\n",
+    "    model_name = models[1],\n",
+    "    distance_metric = 'cosine',\n",
+    "    enforce_detection = False,\n",
+    "    detector_backend = backends[0],\n",
+    "    align = False,\n",
+    "    threshold = .2\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'verified': False,\n",
+       " 'distance': 0.8278660258791868,\n",
+       " 'threshold': 0.2,\n",
+       " 'model': 'Facenet',\n",
+       " 'detector_backend': 'opencv',\n",
+       " 'similarity_metric': 'cosine',\n",
+       " 'facial_areas': {'img1': {'x': 0,\n",
+       "   'y': 0,\n",
+       "   'w': 1459,\n",
+       "   'h': 2210,\n",
+       "   'left_eye': None,\n",
+       "   'right_eye': None},\n",
+       "  'img2': {'x': 0,\n",
+       "   'y': 0,\n",
+       "   'w': 1664,\n",
+       "   'h': 2639,\n",
+       "   'left_eye': None,\n",
+       "   'right_eye': None}},\n",
+       " 'time': 8.36}"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d13346d4bce04924a03b289d7ff4cd44",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/15 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c1b56cc2eaf447d4b6290aef07cae2cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/15 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mat_no = list()\n",
+    "for i in tqdm(os.listdir(\"records\")):\n",
+    "    mat_no.append(\"/\".join(i.split(\"-\")))\n",
+    "\n",
+    "\n",
+    "img_path = list()\n",
+    "for i in tqdm(os.listdir(\"records\")):\n",
+    "    record_ = f\"records/{i}\"\n",
+    "    rec_ = list()\n",
+    "    for n, j in enumerate((os.listdir(record_))):\n",
+    "        rec_.append(f\"records/{i}/{j}\")\n",
+    "    img_path.append(\" \".join(rec_))  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(15, 15)"
+      ]
+     },
+     "execution_count": 187,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(img_path), len(mat_no)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "records_df = pd.DataFrame(\n",
+    "    data = {\n",
+    "        \"matric number\": mat_no,\n",
+    "        \"img paths\": img_path\n",
+    "    }\n",
+    ")\n",
+    "records_df.head()\n",
+    "records_df.to_csv(\"records.csv\", index=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Matric number found in Database\n"
+     ]
+    }
+   ],
+   "source": [
+    "# simple python workflow\n",
+    "\n",
+    "mat_no_ = input(\"Input Matric No here(DEPT/YY/NNNN).....\")\n",
+    "student_name = input(\"Input your name here.......\")\n",
+    "\n",
+    "df = pd.read_csv(\"records.csv\")\n",
+    "mat_nos = [i for i in df[\"matric number\"].values]\n",
+    "\n",
+    "if mat_no_ in mat_nos:\n",
+    "    print(\"Matric number found in Database\")\n",
+    "    verified = True\n",
+    "else:\n",
+    "    print(\"Student matric number not found in database\")\n",
+    "    verified = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if verified:\n",
+    "    df_sort = df[df[\"matric number\"] == mat_no_]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>matric number</th>\n",
+       "      <th>img paths</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CYS/18/5874</td>\n",
+       "      <td>records/CYS-18-5874/1721676589154.jpg records/...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  matric number                                          img paths\n",
+       "0   CYS/18/5874  records/CYS-18-5874/1721676589154.jpg records/..."
+      ]
+     },
+     "execution_count": 191,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_sort"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 192,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['records/CYS-18-5874/1721676589154.jpg',\n",
+       " 'records/CYS-18-5874/1721676589194.jpg']"
+      ]
+     },
+     "execution_count": 192,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "imgs_ = df_sort[\"img paths\"].values[0]\n",
+    "imgs = imgs_.split(\" \")\n",
+    "imgs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img_ = image.imread('records/CYS-18-8472/1721675648545.jpg')\n",
+    "# img_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "verify_status = list()\n",
+    "for img in imgs:\n",
+    "    result = DeepFace.verify(\n",
+    "        img1_path = img_,\n",
+    "        img2_path = img,\n",
+    "        model_name = models[1],\n",
+    "        distance_metric = 'cosine',\n",
+    "        enforce_detection = False,\n",
+    "        detector_backend = backends[0],\n",
+    "        align = False,\n",
+    "        threshold = .2\n",
+    "    )\n",
+    "\n",
+    "    verify_status.append(result[\"verified\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 206,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "verify_status.index(False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7866\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"http://127.0.0.1:7866/\" width=\"100%\" 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"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Traceback (most recent call last):\n",
+      "  File \"c:\\Users\\Ayo Agbaje\\my_venv_\\lib\\site-packages\\gradio\\routes.py\", line 422, in run_predict\n",
+      "    output = await app.get_blocks().process_api(\n",
+      "  File \"c:\\Users\\Ayo Agbaje\\my_venv_\\lib\\site-packages\\gradio\\blocks.py\", line 1326, in process_api\n",
+      "    data = self.postprocess_data(fn_index, result[\"prediction\"], state)\n",
+      "  File \"c:\\Users\\Ayo Agbaje\\my_venv_\\lib\\site-packages\\gradio\\blocks.py\", line 1260, in postprocess_data\n",
+      "    prediction_value = block.postprocess(prediction_value)\n",
+      "  File \"c:\\Users\\Ayo Agbaje\\my_venv_\\lib\\site-packages\\gradio\\components.py\", line 1868, in postprocess\n",
+      "    raise ValueError(\"Cannot process this value as an Image\")\n",
+      "ValueError: Cannot process this value as an Image\n"
+     ]
+    }
+   ],
+   "source": [
+    "def image_predict(mat_no_, student_name, img_):\n",
+    "    models = ['VGG-Face', 'Facenet', 'Facenet512', 'openFace', 'DeepFace', 'DeepId', 'ArcFace', 'Dlib', 'SFace']\n",
+    "    backends = ['opencv', 'ssd', 'dlib', 'mtcnn', 'retinaface', 'mediapipe']\n",
+    "    df = pd.read_csv(\"records.csv\")\n",
+    "    mat_nos = [i for i in df[\"matric number\"].values]\n",
+    "\n",
+    "    if mat_no_ in mat_nos:\n",
+    "        verified = True\n",
+    "    else:\n",
+    "        verified = False\n",
+    "    \n",
+    "    if verified:\n",
+    "        df_sort = df[df[\"matric number\"] == mat_no_]\n",
+    "        imgs_ = df_sort[\"img paths\"].values[0]\n",
+    "        imgs = imgs_.split(\" \")\n",
+    "\n",
+    "        verify_status = list()\n",
+    "        for img in imgs:\n",
+    "            result = DeepFace.verify(\n",
+    "                img1_path = img_,\n",
+    "                img2_path = img,\n",
+    "                model_name = models[1],\n",
+    "                distance_metric = 'cosine',\n",
+    "                enforce_detection = False,\n",
+    "                detector_backend = backends[0],\n",
+    "                align = False,\n",
+    "                threshold = .2\n",
+    "            )\n",
+    "\n",
+    "        verify_status.append(result[\"verified\"])\n",
+    "\n",
+    "        if True in verify_status:\n",
+    "            response_ = f\"{student_name} verified\"\n",
+    "            img_match_id = imgs.index(True)\n",
+    "            img_match = imgs[img_match_id]\n",
+    "            img_match = gr.Image(value = \"no_img.jpg\")\n",
+    "\n",
+    "            return response_, img_match\n",
+    "        else:\n",
+    "            response_ = f\"{student_name} not verified(does not match image in the Database)\"\n",
+    "            img_match = gr.Image(value = img_)\n",
+    "            return response_, img_match\n",
+    "    else:\n",
+    "        response__ = f\"{student_name} not found in Database\"\n",
+    "        img_match = gr.Image(value = \"no_img.jpg\")\n",
+    "        \n",
+    "        return response__, img_match\n",
+    "    \n",
+    "\n",
+    "\n",
+    "with gr.Blocks() as demo:\n",
+    "    m_no = gr.Textbox(label = \"Input Matric No here(DEPT/YY/NNNN).....\")\n",
+    "    name_ = gr.Textbox(label = \"Input your name here.......\")\n",
+    "    image_ = gr.Image(label = 'Input Image to be predicted', source = \"webcam\")\n",
+    "    output1 = gr.Textbox(label = 'Response')\n",
+    "    output2 = gr.Image()\n",
+    "    btn = gr.Button('Verify')\n",
+    "    btn.click(fn = image_predict, inputs = [m_no, name_, image_], outputs = [output1, output2])\n",
+    "\n",
+    "demo.launch(share = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "my_venv_",
+   "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": "3.10.5"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}