Upload Furnitre_tf.ipynb

Furnitre_tf.ipynb

@@ -0,0 +1,574 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "146BB11JpfDA"
+   },
+   "outputs": [],
+   "source": [
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "42hJEdo_pfDB"
+   },
+   "outputs": [],
+   "source": [
+    "CUSTOM_MODEL_NAME = 'my_ssd_mobnet' \n",
+    "PRETRAINED_MODEL_NAME = 'ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8'\n",
+    "PRETRAINED_MODEL_URL = 'http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8.tar.gz'\n",
+    "TF_RECORD_SCRIPT_NAME = 'generate_tfrecord.py'\n",
+    "LABEL_MAP_NAME = 'label_map.pbtxt'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "hbPhYVy_pfDB"
+   },
+   "outputs": [],
+   "source": [
+    "paths = {\n",
+    "    'WORKSPACE_PATH': os.path.join('Tensorflow', 'workspace'),\n",
+    "    'SCRIPTS_PATH': os.path.join('Tensorflow','scripts'),\n",
+    "    'APIMODEL_PATH': os.path.join('Tensorflow','models'),\n",
+    "    'ANNOTATION_PATH': os.path.join('Tensorflow', 'workspace','annotations'),\n",
+    "    'IMAGE_PATH': os.path.join('Tensorflow', 'workspace','images'),\n",
+    "    'MODEL_PATH': os.path.join('Tensorflow', 'workspace','models'),\n",
+    "    'PRETRAINED_MODEL_PATH': os.path.join('Tensorflow', 'workspace','pre-trained-models'),\n",
+    "    'CHECKPOINT_PATH': os.path.join('Tensorflow', 'workspace','models',CUSTOM_MODEL_NAME), \n",
+    "    'OUTPUT_PATH': os.path.join('Tensorflow', 'workspace','models',CUSTOM_MODEL_NAME, 'export'), \n",
+    "    'TFJS_PATH':os.path.join('Tensorflow', 'workspace','models',CUSTOM_MODEL_NAME, 'tfjsexport'), \n",
+    "    'TFLITE_PATH':os.path.join('Tensorflow', 'workspace','models',CUSTOM_MODEL_NAME, 'tfliteexport'), \n",
+    "    'PROTOC_PATH':os.path.join('Tensorflow','protoc')\n",
+    " }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "LwhWZMI0pfDC"
+   },
+   "outputs": [],
+   "source": [
+    "files = {\n",
+    "    'PIPELINE_CONFIG':os.path.join('Tensorflow', 'workspace','models', CUSTOM_MODEL_NAME, 'pipeline.config'),\n",
+    "    'TF_RECORD_SCRIPT': os.path.join(paths['SCRIPTS_PATH'], TF_RECORD_SCRIPT_NAME), \n",
+    "    'LABELMAP': os.path.join(paths['ANNOTATION_PATH'], LABEL_MAP_NAME)\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "HR-TfDGrpfDC"
+   },
+   "outputs": [],
+   "source": [
+    "for path in paths.values():\n",
+    "    if not os.path.exists(path):\n",
+    "        if os.name == 'posix':\n",
+    "            !mkdir -p {path}\n",
+    "        if os.name == 'nt':\n",
+    "            !mkdir {path}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "K-Cmz2edpfDE",
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "if os.name=='nt':\n",
+    "    !pip install wget\n",
+    "    import wget"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "iA1DIq5OpfDE"
+   },
+   "outputs": [],
+   "source": [
+    "if not os.path.exists(os.path.join(paths['APIMODEL_PATH'], 'research', 'object_detection')):\n",
+    "    !git clone https://github.com/tensorflow/models {paths['APIMODEL_PATH']}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "rJjMHbnDs3Tv"
+   },
+   "outputs": [],
+   "source": [
+    "# Install Tensorflow Object Detection \n",
+    "if os.name=='posix':  \n",
+    "    !apt-get install protobuf-compiler\n",
+    "    !cd Tensorflow/models/research && protoc object_detection/protos/*.proto --python_out=. && cp object_detection/packages/tf2/setup.py . && python -m pip install . \n",
+    "    \n",
+    "if os.name=='nt':\n",
+    "    url=\"https://github.com/protocolbuffers/protobuf/releases/download/v3.15.6/protoc-3.15.6-win64.zip\"\n",
+    "    wget.download(url)\n",
+    "    !move protoc-3.15.6-win64.zip {paths['PROTOC_PATH']}\n",
+    "    !cd {paths['PROTOC_PATH']} && tar -xf protoc-3.15.6-win64.zip\n",
+    "    os.environ['PATH'] += os.pathsep + os.path.abspath(os.path.join(paths['PROTOC_PATH'], 'bin'))   \n",
+    "    !cd Tensorflow/models/research && protoc object_detection/protos/*.proto --python_out=. && copy object_detection\\\\packages\\\\tf2\\\\setup.py setup.py && python setup.py build && python setup.py install\n",
+    "    !cd Tensorflow/models/research/slim && pip install -e . "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "VERIFICATION_SCRIPT = os.path.join(paths['APIMODEL_PATH'], 'research', 'object_detection', 'builders', 'model_builder_tf2_test.py')\n",
+    "# Verify Installation\n",
+    "!python {VERIFICATION_SCRIPT}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import object_detection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "!pip list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "csofht2npfDE",
+    "outputId": "ff5471b2-bed2-43f2-959c-327a706527b6"
+   },
+   "outputs": [],
+   "source": [
+    "if os.name =='posix':\n",
+    "    !wget {PRETRAINED_MODEL_URL}\n",
+    "    !mv {PRETRAINED_MODEL_NAME+'.tar.gz'} {paths['PRETRAINED_MODEL_PATH']}\n",
+    "    !cd {paths['PRETRAINED_MODEL_PATH']} && tar -zxvf {PRETRAINED_MODEL_NAME+'.tar.gz'}\n",
+    "if os.name == 'nt':\n",
+    "    wget.download(PRETRAINED_MODEL_URL)\n",
+    "    !move {PRETRAINED_MODEL_NAME+'.tar.gz'} {paths['PRETRAINED_MODEL_PATH']}\n",
+    "    !cd {paths['PRETRAINED_MODEL_PATH']} && tar -zxvf {PRETRAINED_MODEL_NAME+'.tar.gz'}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "p1BVDWo7pfDC"
+   },
+   "outputs": [],
+   "source": [
+    "labels = [{'name':'Density1Benign', 'id':1}, {'name':'Density1Malignant', 'id':2}]\n",
+    "\n",
+    "with open(files['LABELMAP'], 'w') as f:\n",
+    "    for label in labels:\n",
+    "        f.write('item { \\n')\n",
+    "        f.write('\\tname:\\'{}\\'\\n'.format(label['name']))\n",
+    "        f.write('\\tid:{}\\n'.format(label['id']))\n",
+    "        f.write('}\\n')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "KWpb_BVUpfDD",
+    "outputId": "56ce2a3f-3933-4ee6-8a9d-d5ec65f7d73c"
+   },
+   "outputs": [],
+   "source": [
+    "if not os.path.exists(files['TF_RECORD_SCRIPT']):\n",
+    "    !git clone https://github.com/nicknochnack/GenerateTFRecord {paths['SCRIPTS_PATH']}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "UPFToGZqpfDD",
+    "outputId": "0ebb456f-aadc-4a1f-96e6-fbfec1923e1c"
+   },
+   "outputs": [],
+   "source": [
+    "!python {files['TF_RECORD_SCRIPT']} -x {os.path.join(paths['IMAGE_PATH'], 'train')} -l {files['LABELMAP']} -o {os.path.join(paths['ANNOTATION_PATH'], 'train.record')} \n",
+    "!python {files['TF_RECORD_SCRIPT']} -x {os.path.join(paths['IMAGE_PATH'], 'test')} -l {files['LABELMAP']} -o {os.path.join(paths['ANNOTATION_PATH'], 'test.record')} "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "cOjuTFbwpfDF"
+   },
+   "outputs": [],
+   "source": [
+    "if os.name =='posix':\n",
+    "    !cp {os.path.join(paths['PRETRAINED_MODEL_PATH'], PRETRAINED_MODEL_NAME, 'pipeline.config')} {os.path.join(paths['CHECKPOINT_PATH'])}\n",
+    "if os.name == 'nt':\n",
+    "    !copy {os.path.join(paths['PRETRAINED_MODEL_PATH'], PRETRAINED_MODEL_NAME, 'pipeline.config')} {os.path.join(paths['CHECKPOINT_PATH'])}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Z9hRrO_ppfDF"
+   },
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "from object_detection.utils import config_util\n",
+    "from object_detection.protos import pipeline_pb2\n",
+    "from google.protobuf import text_format"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "c2A0mn4ipfDF"
+   },
+   "outputs": [],
+   "source": [
+    "config = config_util.get_configs_from_pipeline_file(files['PIPELINE_CONFIG'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "uQA13-afpfDF",
+    "outputId": "907496a4-a39d-4b13-8c2c-e5978ecb1f10"
+   },
+   "outputs": [],
+   "source": [
+    "config"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "9vK5lotDpfDF"
+   },
+   "outputs": [],
+   "source": [
+    "pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()\n",
+    "with tf.io.gfile.GFile(files['PIPELINE_CONFIG'], \"r\") as f:                                                                                                                                                                                                                     \n",
+    "    proto_str = f.read()                                                           \n",
+    "    text_format.Merge(proto_str, pipeline_config)  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "rP43Ph0JpfDG"
+   },
+   "outputs": [],
+   "source": [
+    "pipeline_config.model.ssd.num_classes = len(labels)\n",
+    "pipeline_config.train_config.batch_size = 4\n",
+    "pipeline_config.train_config.fine_tune_checkpoint = os.path.join(paths['PRETRAINED_MODEL_PATH'], PRETRAINED_MODEL_NAME, 'checkpoint', 'ckpt-0')\n",
+    "pipeline_config.train_config.fine_tune_checkpoint_type = \"detection\"\n",
+    "pipeline_config.train_input_reader.label_map_path= files['LABELMAP']\n",
+    "pipeline_config.train_input_reader.tf_record_input_reader.input_path[:] = [os.path.join(paths['ANNOTATION_PATH'], 'train.record')]\n",
+    "pipeline_config.eval_input_reader[0].label_map_path = files['LABELMAP']\n",
+    "pipeline_config.eval_input_reader[0].tf_record_input_reader.input_path[:] = [os.path.join(paths['ANNOTATION_PATH'], 'test.record')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "oJvfgwWqpfDG"
+   },
+   "outputs": [],
+   "source": [
+    "config_text = text_format.MessageToString(pipeline_config)                                                                                                                                                                                                        \n",
+    "with tf.io.gfile.GFile(files['PIPELINE_CONFIG'], \"wb\") as f:                                                                                                                                                                                                                     \n",
+    "    f.write(config_text)   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "B-Y2UQmQpfDG"
+   },
+   "outputs": [],
+   "source": [
+    "TRAINING_SCRIPT = os.path.join(paths['APIMODEL_PATH'], 'research', 'object_detection', 'model_main_tf2.py')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "jMP2XDfQpfDH"
+   },
+   "outputs": [],
+   "source": [
+    "command = \"python {} --model_dir={} --pipeline_config_path={} --num_train_steps=2000\".format(TRAINING_SCRIPT, paths['CHECKPOINT_PATH'],files['PIPELINE_CONFIG'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "A4OXXi-ApfDH",
+    "outputId": "117a0e83-012b-466e-b7a6-ccaa349ac5ab"
+   },
+   "outputs": [],
+   "source": [
+    "print(command)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "i3ZsJR-qpfDH",
+    "outputId": "cabec5e1-45e6-4f2f-d9cf-297d9c1d0225"
+   },
+   "outputs": [],
+   "source": [
+    "!{command}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "80L7-fdPpfDH"
+   },
+   "outputs": [],
+   "source": [
+    "command = \"python {} --model_dir={} --pipeline_config_path={} --checkpoint_dir={}\".format(TRAINING_SCRIPT, paths['CHECKPOINT_PATH'],files['PIPELINE_CONFIG'], paths['CHECKPOINT_PATH'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "lYsgEPx9pfDH",
+    "outputId": "8632d48b-91d2-45d9-bcb8-c1b172bf6eed"
+   },
+   "outputs": [],
+   "source": [
+    "print(command)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "lqTV2jGBpfDH"
+   },
+   "outputs": [],
+   "source": [
+    "!{command}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "8TYk4_oIpfDI"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import tensorflow as tf\n",
+    "from object_detection.utils import label_map_util\n",
+    "from object_detection.utils import visualization_utils as viz_utils\n",
+    "from object_detection.builders import model_builder\n",
+    "from object_detection.utils import config_util"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "tDnQg-cYpfDI"
+   },
+   "outputs": [],
+   "source": [
+    "# Load pipeline config and build a detection model\n",
+    "configs = config_util.get_configs_from_pipeline_file(files['PIPELINE_CONFIG'])\n",
+    "detection_model = model_builder.build(model_config=configs['model'], is_training=False)\n",
+    "\n",
+    "# Restore checkpoint\n",
+    "ckpt = tf.compat.v2.train.Checkpoint(model=detection_model)\n",
+    "ckpt.restore(os.path.join(paths['CHECKPOINT_PATH'], 'ckpt-9')).expect_partial()\n",
+    "\n",
+    "@tf.function\n",
+    "def detect_fn(image):\n",
+    "    image, shapes = detection_model.preprocess(image)\n",
+    "    prediction_dict = detection_model.predict(image, shapes)\n",
+    "    detections = detection_model.postprocess(prediction_dict, shapes)\n",
+    "    return detections"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Y_MKiuZ4pfDI"
+   },
+   "outputs": [],
+   "source": [
+    "import cv2 \n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "cBDbIhNapfDI"
+   },
+   "outputs": [],
+   "source": [
+    "category_index = label_map_util.create_category_index_from_labelmap(files['LABELMAP'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Lx3crOhOzITB"
+   },
+   "outputs": [],
+   "source": [
+    "IMAGE_PATH = os.path.join(paths['IMAGE_PATH'], 'test', '20587612 (36).png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 269
+    },
+    "id": "Tpzn1SMry1yK",
+    "outputId": "c392a2c5-10fe-4fc4-9998-a1d4c7db2bd3"
+   },
+   "outputs": [],
+   "source": [
+    "img = cv2.imread(IMAGE_PATH)\n",
+    "image_np = np.array(img)\n",
+    "\n",
+    "input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32)\n",
+    "detections = detect_fn(input_tensor)\n",
+    "\n",
+    "num_detections = int(detections.pop('num_detections'))\n",
+    "detections = {key: value[0, :num_detections].numpy()\n",
+    "              for key, value in detections.items()}\n",
+    "detections['num_detections'] = num_detections\n",
+    "\n",
+    "# detection_classes should be ints.\n",
+    "detections['detection_classes'] = detections['detection_classes'].astype(np.int64)\n",
+    "\n",
+    "label_id_offset = 1\n",
+    "image_np_with_detections = image_np.copy()\n",
+    "\n",
+    "viz_utils.visualize_boxes_and_labels_on_image_array(\n",
+    "            image_np_with_detections,\n",
+    "            detections['detection_boxes'],\n",
+    "            detections['detection_classes']+label_id_offset,\n",
+    "            detections['detection_scores'],\n",
+    "            category_index,\n",
+    "            use_normalized_coordinates=True,\n",
+    "            max_boxes_to_draw=5,\n",
+    "            min_score_thresh=.2,\n",
+    "            agnostic_mode=False)\n",
+    "\n",
+    "plt.imshow(cv2.cvtColor(image_np_with_detections, cv2.COLOR_BGR2RGB))\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "name": "3. Training and Detection.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "hamza1",
+   "language": "python",
+   "name": "hamza1"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}