Update app.py

app.py

@@ -1,278 +1,72 @@
-{
- "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": [
-    "pip install scipy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pip install keras==2.4.0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pip install tf-models-official"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pip install Pillow"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pip install matplotlib==3.2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pip install tensorflow_io"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pip install scipy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pip install protobuf==3.20.*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install tensorflow --upgrade"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    }
-   },
-   "outputs": [],
-   "source": [
-    "!pip uninstall protobuf matplotlib -y\n",
-    "!pip install protobuf matplotlib==3.2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pip install tensorflow-object-detection-api"
-   ]
-  }
- ],
- "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
+import streamlit as st
+import cv2
+from PIL import Image
+import os
+import numpy as np
+import tensorflow as tf
+from object_detection.utils import label_map_util
+from object_detection.utils import visualization_utils as viz_utils
+from object_detection.builders import model_builder
+from object_detection.utils import config_util
+
+
+CUSTOM_MODEL_NAME = 'my_ssd_mobnet'
+paths = {
+    'CHECKPOINT_PATH': os.path.join('Tensorflow', 'workspace', 'models', CUSTOM_MODEL_NAME),
+    'LABELMAP': os.path.join('Tensorflow', 'workspace', 'annotations', 'label_map.pbtxt')
 }
+
+configs = config_util.get_configs_from_pipeline_file(os.path.join(paths['CHECKPOINT_PATH'], 'pipeline.config'))
+detection_model = model_builder.build(model_config=configs['model'], is_training=False)
+ckpt = tf.compat.v2.train.Checkpoint(model=detection_model)
+ckpt.restore(os.path.join(paths['CHECKPOINT_PATH'], 'ckpt-3')).expect_partial()
+category_index = label_map_util.create_category_index_from_labelmap(paths['LABELMAP'])
+
+
+@tf.function
+def detect_fn(image):
+    image, shapes = detection_model.preprocess(image)
+    prediction_dict = detection_model.predict(image, shapes)
+    detections = detection_model.postprocess(prediction_dict, shapes)
+    return detections
+
+
+def main():
+    st.title('Furniture Detection')
+
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
+
+    if uploaded_file is not None:
+        image = np.array(Image.open(uploaded_file))
+        st.image(image, caption='Uploaded Image', use_column_width=True)
+        st.write("")
+        st.write("Detection In Process...")
+
+        
+        input_tensor = tf.convert_to_tensor(np.expand_dims(image, 0), dtype=tf.float32)
+        detections = detect_fn(input_tensor)
+
+        num_detections = int(detections.pop('num_detections'))
+        detections = {key: value[0, :num_detections].numpy() for key, value in detections.items()}
+        detections['num_detections'] = num_detections
+        detections['detection_classes'] = detections['detection_classes'].astype(np.int64)
+
+        label_id_offset = 1
+        image_np_with_detections = image.copy()
+
+        viz_utils.visualize_boxes_and_labels_on_image_array(
+            image_np_with_detections,
+            detections['detection_boxes'],
+            detections['detection_classes'] + label_id_offset,
+            detections['detection_scores'],
+            category_index,
+            use_normalized_coordinates=True,
+            max_boxes_to_draw=5,
+            min_score_thresh=.3,
+            agnostic_mode=False
+        )
+
+        st.image(image_np_with_detections, caption='Detected Teeth', use_column_width=True)
+
+if __name__ == "__main__":
+    main()