Skin Oiliness Severity Detection system.ipynb

{
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     "text": [
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      "Installing collected packages: numpy, opencv-python\n",
      "Successfully installed numpy-1.26.4 opencv-python-4.9.0.80\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
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    "pip install opencv-python"
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  {
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   "id": "89956e7d",
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   "outputs": [
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     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in c:\\users\\basuru yasaruwan\\contacts\\desktop\\ipcv practicals\\oildetection\\lib\\site-packages (1.26.4)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install numpy"
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   "source": [
    "pip install tensorflow"
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     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\Basuru Yasaruwan\\Contacts\\Desktop\\IPCV Practicals\\OILDetection\\Lib\\site-packages\\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",
      "WARNING:tensorflow:From C:\\Users\\Basuru Yasaruwan\\Contacts\\Desktop\\IPCV Practicals\\OILDetection\\Lib\\site-packages\\keras\\src\\backend.py:1398: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Basuru Yasaruwan\\Contacts\\Desktop\\IPCV Practicals\\OILDetection\\Lib\\site-packages\\keras\\src\\layers\\pooling\\max_pooling2d.py:161: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "from tensorflow.keras.applications import VGG16\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.applications.vgg16 import preprocess_input\n",
    "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D\n",
    "from tensorflow.keras.models import Model\n",
    "\n",
    "# Function to preprocess a frame for VGG16\n",
    "def preprocess_frame(frame):\n",
    "    # Preprocess the frame for VGG16\n",
    "    frame = cv2.resize(frame, (224, 224))\n",
    "    frame = image.img_to_array(frame)\n",
    "    frame = np.expand_dims(frame, axis=0)\n",
    "    frame = preprocess_input(frame)\n",
    "    return frame\n",
    "\n",
    "# Function to build the oiliness detection model\n",
    "def build_model():\n",
    "    # Load the pre-trained VGG16 model without the top classification layer\n",
    "    base_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
    "    \n",
    "    # Freeze the layers of the pre-trained model\n",
    "    for layer in base_model.layers:\n",
    "        layer.trainable = False\n",
    "    \n",
    "    # Add custom layers for binary classification\n",
    "    x = base_model.output\n",
    "    x = GlobalAveragePooling2D()(x)\n",
    "    x = Dense(256, activation='relu')(x)\n",
    "    predictions = Dense(1, activation='sigmoid')(x)\n",
    "    \n",
    "    # Build the complete model\n",
    "    model = Model(inputs=base_model.input, outputs=predictions)\n",
    "    return model\n",
    "\n",
    "# Function to detect oiliness severity in a given frame\n",
    "def detect_oiliness(frame, model):\n",
    "    # Preprocess the frame\n",
    "    frame_array = preprocess_frame(frame)\n",
    "    \n",
    "    # Make predictions using the model\n",
    "    prediction = model.predict(frame_array)\n",
    "    \n",
    "    # Classify severity based on the prediction\n",
    "    severity_level = 1 if prediction < 0.5 else 3  # Binary classification\n",
    "    return severity_level\n",
    "\n",
    "# Build the model\n",
    "oiliness_model = build_model()\n",
    "\n",
    "# Open a connection to the webcam (usually index 0, but it may vary)\n",
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "while True:\n",
    "    # Read a frame from the webcam\n",
    "    ret, frame = cap.read()\n",
    "    \n",
    "    # Detect oiliness severity\n",
    "    severity_level = detect_oiliness(frame, oiliness_model)\n",
    "    \n",
    "    # Display the severity level on the frame\n",
    "    cv2.putText(frame, f\"Oiliness Severity: {severity_level}\", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)\n",
    "    \n",
    "    # Display the frame\n",
    "    cv2.imshow('Oiliness Detection', frame)\n",
    "    \n",
    "    # Break the loop if 'q' is pressed\n",
    "    if cv2.waitKey(1) & 0xFF == ord('q'):\n",
    "        break\n",
    "\n",
    "# Release the webcam and close the window\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd971e5e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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