gender_Model_new.ipynb

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    {
     "name": "stdout",
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     "text": [
      "Found 7713 images belonging to 2 classes.\n",
      "Found 2329 images belonging to 2 classes.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tahirabbas/anaconda3/lib/python3.11/site-packages/keras/src/layers/convolutional/base_conv.py:99: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tahirabbas/anaconda3/lib/python3.11/site-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:120: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
      "  self._warn_if_super_not_called()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m  1/241\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m16:41\u001b[0m 4s/step - accuracy: 0.4688 - loss: 0.7211"
     ]
    },
    {
     "name": "stderr",
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     "text": [
      "/Users/tahirabbas/anaconda3/lib/python3.11/site-packages/PIL/Image.py:996: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m241/241\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m118s\u001b[0m 476ms/step - accuracy: 0.5283 - loss: 0.7195 - val_accuracy: 0.6272 - val_loss: 0.6425\n",
      "Epoch 2/10\n",
      "\u001b[1m241/241\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 387us/step - accuracy: 0.7188 - loss: 0.5410 - val_accuracy: 0.8000 - val_loss: 0.5169\n",
      "Epoch 3/10\n"
     ]
    },
    {
     "name": "stderr",
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     "text": [
      "2024-04-13 12:36:42.538889: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n",
      "\t [[{{node IteratorGetNext}}]]\n",
      "/Users/tahirabbas/anaconda3/lib/python3.11/contextlib.py:155: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n",
      "  self.gen.throw(typ, value, traceback)\n",
      "2024-04-13 12:36:42.620322: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n",
      "\t [[{{node IteratorGetNext}}]]\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m241/241\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m180s\u001b[0m 738ms/step - accuracy: 0.7275 - loss: 0.5385 - val_accuracy: 0.7799 - val_loss: 0.5201\n",
      "Epoch 4/10\n",
      "\u001b[1m241/241\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 519us/step - accuracy: 0.7188 - loss: 0.6804 - val_accuracy: 0.8400 - val_loss: 0.4524\n",
      "Epoch 5/10\n"
     ]
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     "text": [
      "2024-04-13 12:39:43.023590: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n",
      "\t [[{{node IteratorGetNext}}]]\n",
      "2024-04-13 12:39:43.140392: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n",
      "\t [[{{node IteratorGetNext}}]]\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m241/241\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m158s\u001b[0m 645ms/step - accuracy: 0.7383 - loss: 0.5275 - val_accuracy: 0.8424 - val_loss: 0.3756\n",
      "Epoch 6/10\n",
      "\u001b[1m241/241\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 519us/step - accuracy: 0.9062 - loss: 0.3141 - val_accuracy: 0.7200 - val_loss: 0.4571\n",
      "Epoch 7/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-04-13 12:42:21.597074: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n",
      "\t [[{{node IteratorGetNext}}]]\n",
      "2024-04-13 12:42:21.713764: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n",
      "\t [[{{node IteratorGetNext}}]]\n"
     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m241/241\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m136s\u001b[0m 554ms/step - accuracy: 0.8078 - loss: 0.4139 - val_accuracy: 0.8754 - val_loss: 0.3103\n",
      "Epoch 8/10\n",
      "\u001b[1m  1/241\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m5:39\u001b[0m 1s/step - accuracy: 0.7812 - loss: 0.4487"
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     "name": "stderr",
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     "text": [
      "2024-04-13 12:44:38.757183: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n",
      "\t [[{{node IteratorGetNext}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m241/241\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.7812 - loss: 0.4487 - val_accuracy: 0.9600 - val_loss: 0.1867\n",
      "Epoch 9/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-04-13 12:44:39.333910: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n",
      "\t [[{{node IteratorGetNext}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m241/241\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m141s\u001b[0m 570ms/step - accuracy: 0.8702 - loss: 0.3078 - val_accuracy: 0.8880 - val_loss: 0.2910\n",
      "Epoch 10/10\n",
      "\u001b[1m241/241\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 493us/step - accuracy: 0.8438 - loss: 0.2058 - val_accuracy: 1.0000 - val_loss: 0.0689\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-04-13 12:47:00.868093: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n",
      "\t [[{{node IteratorGetNext}}]]\n",
      "2024-04-13 12:47:00.978451: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n",
      "\t [[{{node IteratorGetNext}}]]\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "73/73 - 8s - 112ms/step - accuracy: 0.8854 - loss: 0.2831\n",
      "\n",
      "Test accuracy: 0.8853585124015808\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
    "\n",
    "IMAGE_SIZE = (150, 150)\n",
    "BATCH_SIZE = 32\n",
    "\n",
    "train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(\n",
    "    rescale=1./255,\n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True,\n",
    "    validation_split=0.2\n",
    ")\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    '/Users/tahirabbas/Desktop/hola/train',\n",
    "    target_size=IMAGE_SIZE,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    class_mode='binary',\n",
    "    subset='training'\n",
    ")\n",
    "\n",
    "validation_generator = train_datagen.flow_from_directory(\n",
    "    '/Users/tahirabbas/Desktop/hola/val',\n",
    "    target_size=IMAGE_SIZE,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    class_mode='binary',\n",
    "    subset='validation'\n",
    ")\n",
    "\n",
    "model = Sequential([\n",
    "    Conv2D(32, (3, 3), activation='relu', input_shape=(IMAGE_SIZE[0], IMAGE_SIZE[1], 3)),\n",
    "    MaxPooling2D(pool_size=(2, 2)),\n",
    "    Conv2D(64, (3, 3), activation='relu'),\n",
    "    MaxPooling2D(pool_size=(2, 2)),\n",
    "    Conv2D(128, (3, 3), activation='relu'),\n",
    "    MaxPooling2D(pool_size=(2, 2)),\n",
    "    Flatten(),\n",
    "    Dense(128, activation='relu'),\n",
    "    Dense(1, activation='sigmoid')\n",
    "])\n",
    "\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "history = model.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch=train_generator.samples // BATCH_SIZE,\n",
    "    validation_data=validation_generator,\n",
    "    validation_steps=validation_generator.samples // BATCH_SIZE,\n",
    "    epochs=10\n",
    ")\n",
    "\n",
    "test_loss, test_acc = model.evaluate(validation_generator, verbose=2)\n",
    "print('\\nTest accuracy:', test_acc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8dde67d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    }
   ],
   "source": [
    "model.save(\"gender_model_toh_hai.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4ca77f0d",
   "metadata": {},
   "outputs": [
    {
     "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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
      "Male\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.models import load_model\n",
    "\n",
    "model = load_model('gender_model_toh_hai.h5')\n",
    "\n",
    "def preprocess_image(img_path):\n",
    "    img = image.load_img(img_path, target_size=(150, 150))\n",
    "    img = image.img_to_array(img)\n",
    "    img = img / 255.0\n",
    "    img = np.expand_dims(img, axis=0)\n",
    "    return img\n",
    "\n",
    "camera = cv2.VideoCapture(0)\n",
    "if not camera.isOpened():\n",
    "    print(\"Error: Could not open camera.\")\n",
    "    exit()\n",
    "\n",
    "return_value, img = camera.read()\n",
    "if not return_value:\n",
    "    print(\"Error: Could not capture image.\")\n",
    "    camera.release()\n",
    "    exit()\n",
    "\n",
    "cv2.imwrite('captured_image.jpg', img)\n",
    "\n",
    "camera.release()\n",
    "\n",
    "img = preprocess_image('captured_image.jpg')\n",
    "\n",
    "prediction = model.predict(img)\n",
    "\n",
    "threshold = 0.5\n",
    "if prediction > threshold:\n",
    "    print(\"Female\")\n",
    "else:\n",
    "    print(\"Male\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3452ff97",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1e6855f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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