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"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "f8d9c166-c713-4e3e-900b-df2d6c6df3b2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:TensorFlow GPU support is not available on native Windows for TensorFlow >= 2.11. Even if CUDA/cuDNN are installed, GPU will not be used. Please use WSL2 or the TensorFlow-DirectML plugin.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
]
},
{
"data": {
"text/plain": [
"<Sequential name=sequential, built=True>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from tensorflow.keras.models import load_model\n",
"\n",
"model=load_model(\"Face_Detection_Model.h5\")\n",
"model"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "92f623a0-76d0-463e-a7d8-4783296ef2e4",
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.preprocessing import image\n",
"import numpy as np\n",
"\n",
"test_image = image.load_img(\n",
" r\"C:\\Users\\abhis\\Desktop\\Deep Learning\\Deep Learning Project\\Face Detection Project\\images\\train\\Face\\00af34930b6f21da.jpg\",\n",
" target_size=(128,128))\n",
"\n",
"test_image = image.img_to_array(test_image)\n",
"test_image = np.expand_dims(test_image, axis=0)\n",
"test_image = test_image / 255.0"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "6df15bba-8afd-479f-9e56-8abffffe46b5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 4s/step\n",
"[[0.12947011]]\n"
]
}
],
"source": [
"pred = model.predict(test_image)\n",
"print(pred)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "031710e6-077c-482d-aa39-9412ab838578",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Face Detected\n"
]
}
],
"source": [
"if pred [0][0]>0.5:\n",
" print(\"Face Not Detected\")\n",
"else:\n",
" print(\"Face Detected\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "de1d4df7-38de-4bfd-8d82-605a8a904cc9",
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.preprocessing import image\n",
"import numpy as np\n",
"\n",
"test_image = image.load_img(\n",
" r\"C:\\Users\\abhis\\Desktop\\Deep Learning\\Deep Learning Project\\Face Detection Project\\images\\train\\No_Face\\001_0001.jpg\",\n",
" target_size=(128,128))\n",
"\n",
"test_image = image.img_to_array(test_image)\n",
"test_image = np.expand_dims(test_image, axis=0)\n",
"test_image = test_image / 255.0"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "79dba6a6-479c-403a-bfa7-768e2be56564",
"metadata": {},
"outputs": [],
"source": [
"pred = model.predict(test_image)\n",
"print(pred)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "80198a51-50d1-4a47-9595-54f5330f728b",
"metadata": {},
"outputs": [],
"source": [
"if pred [0][0]>0.5:\n",
" print(\"Face Not Detected\")\n",
"else:\n",
" print(\"Face Detected\")"
]
},
{
"cell_type": "markdown",
"id": "70d6e1d0-4cca-470e-9bc0-9e5099139aaa",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"id": "8aa5e9f5-2fe5-4d84-9f12-dca2f7834335",
"metadata": {},
"source": [
"**WEB CAM OR LIVE FACE DETECTING**"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "571476e8-d9ab-4f1a-bad6-43238be047f6",
"metadata": {},
"outputs": [],
"source": [
"import cv2\n",
"\n",
"#Load Face Detector\n",
"face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')\n",
"\n",
"#Open Webcam\n",
"cam = cv2.VideoCapture(1) #1 Means Portable Webcam. In Default Is 0 Means Inbuilt Camera.\n",
"\n",
"while True:\n",
" ret, frame = cam.read()\n",
"\n",
" if not ret:\n",
" break\n",
" \n",
" #Convert To Grayscale\n",
" gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)\n",
"\n",
" #Detect Faces\n",
" faces = face_cascade.detectMultiScale(gray, scaleFactor = 1.1, minNeighbors = 5)\n",
"\n",
" #If Face Detected\n",
" if len(faces)>0:\n",
" text = \"Face Detected\"\n",
" else:\n",
" text = \"Face Not Detected\"\n",
"\n",
" cv2.putText(frame, text, (20,40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255,0), 2)\n",
"\n",
" #Draw Rectangle Around Faces\n",
" for (x,y,w,h) in faces:\n",
" cv2.rectangle(frame, (x,y), (x+w,y+h), (255,0,0), 2)\n",
"\n",
" cv2.imshow(\"Live Face Detection\", frame)\n",
"\n",
" if cv2.waitKey(1) == 27:\n",
" break\n",
"\n",
"cam.release()\n",
"cv2.destroyAllWindows()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2a0764de-c404-4af1-86e1-1fbe751698db",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:base] *",
"language": "python",
"name": "conda-base-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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