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Backend/SF__Dinuka_Pest_Damages_identification_V01.ipynb

@@ -485,6 +485,128 @@
    "source": [
     "plot_loss_curve(resnet_hist)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ydvjG5N-IG7a"
+   },
+   "source": [
+    "# 🟢 Make Predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "j1mHbvSWKJFn"
+   },
+   "outputs": [],
+   "source": [
+    "#Create a function to improt an image and resize it to be able to used with our model\n",
+    "def load_and_prep_image(filename , img_shape=448):\n",
+    "  \"\"\"\n",
+    "  Reads an image from file name , turns it into a tensor and reshapes it to (img_shape , color_channel\n",
+    "  \"\"\"\n",
+    "  print('FILe Name type - ', type(filename))\n",
+    "  #Read in the image\n",
+    "  img = tf.io.read_file(filename)\n",
+    "  print('img 1- ', type(img))\n",
+    "\n",
+    "  #Decode the read file into a tensor\n",
+    "  img = tf.image.decode_image(img)\n",
+    "  print('img 2- ', type(img))\n",
+    "\n",
+    "  #Resiz the Image\n",
+    "  img = tf.image.resize(img , size=[img_shape , img_shape])\n",
+    "\n",
+    "  #Re scale the image - get all values between 0 and 255\n",
+    "  img = img/255.\n",
+    "\n",
+    "  return img\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "j1mHbvSWKJFn"
+   },
+   "outputs": [],
+   "source": [
+    "#Create a function to improt an image and resize it to be able to used with our model\n",
+    "def load_and_prep_image(filename , img_shape=448):\n",
+    "  \"\"\"\n",
+    "  Reads an image from file name , turns it into a tensor and reshapes it to (img_shape , color_channel\n",
+    "  \"\"\"\n",
+    "  print('FILe Name type - ', type(filename))\n",
+    "  #Read in the image\n",
+    "  img = tf.io.read_file(filename)\n",
+    "  print('img 1- ', type(img))\n",
+    "\n",
+    "  #Decode the read file into a tensor\n",
+    "  img = tf.image.decode_image(img)\n",
+    "  print('img 2- ', type(img))\n",
+    "\n",
+    "  #Resiz the Image\n",
+    "  img = tf.image.resize(img , size=[img_shape , img_shape])\n",
+    "\n",
+    "  #Re scale the image - get all values between 0 and 255\n",
+    "  img = img/255.\n",
+    "\n",
+    "  return img\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 351
+    },
+    "id": "VZv8GfDMIZoJ",
+    "outputId": "9af472d3-9570-4779-a185-809cae5032f0"
+   },
+   "outputs": [],
+   "source": [
+    "image_path = \"/content/Damage Data/Testing/Mealybug/5436367-PPT.jpg\"\n",
+    "pred_and_plot(resnet_model , image_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 351
+    },
+    "id": "mYHZo1fW5Das",
+    "outputId": "7af9c19c-6bc4-4123-f7ae-74b94b78f9f0"
+   },
+   "outputs": [],
+   "source": [
+    "image_path = \"/content/Damage Data/Testing/Leaf miner/1223124-PPT.jpg\"\n",
+    "pred_and_plot(resnet_model , image_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 351
+    },
+    "id": "mYHZo1fW5Das",
+    "outputId": "7af9c19c-6bc4-4123-f7ae-74b94b78f9f0"
+   },
+   "outputs": [],
+   "source": [
+    "image_path = \"/content/Damage Data/Testing/Leaf miner/1223124-PPT.jpg\"\n",
+    "pred_and_plot(resnet_model , image_path)"
+   ]
   }
  ],
  "metadata": {