Update app.py

app.py

@@ -36,6 +36,41 @@ utils_ops.tf = tf.compat.v1
 
 # Patch the location of gfile
 tf.gfile = tf.io.gfile
+os.system('python object_detection/builders/model_builder_tf2_test.py')
+def load_model(model_dir):
+  model = tf.saved_model.load(str(model_dir))
+  model = model.signatures['serving_default']
+  return model
+os.system('mkdir "Tortoise"')
+os.chdir('Tortoise/')
+os.system('curl -L "https://app.roboflow.com/ds/jCjxJgk04M?key=3JE38XqESy" > roboflow.zip; unzip roboflow.zip; rm roboflow.zip')
+os.chdir('..')
+os.system('mkdir "COCO"')
+os.chdir('COCO/')
+os.system('curl -L "https://app.roboflow.com/ds/Yb2OGQm2xb?key=pbWEWpS5ec" > roboflow.zip; unzip roboflow.zip; rm roboflow.zip')
+os.chdir('..')
+
+PATH_TO_TEST_IMAGES_DIR = pathlib.Path("COCO" + '/test/')
+TEST_IMAGE_PATHS = sorted(list(PATH_TO_TEST_IMAGES_DIR.glob("*.jpg")))
+dataset = 'Tortoise'
+test_record_fname = dataset + '/test/tortoise.tfrecord'
+train_record_fname = dataset + '/train/tortoise.tfrecord'
+label_map_pbtxt_fname = dataset + '/train/tortoise_label_map.pbtxt'
+PATH_TO_LABELS = dataset + '/train/tortoise_label_map.pbtxt'
+category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=False)
+
+
+test_data_json = 'COCO/test/_annotations.coco.json'
+with open(test_data_json, 'r') as f:
+  test_metadata = json.load(f)
+for im in test_metadata['images']:
+            im['date_captured'] = str(datetime.strptime(im['file_name'][6:21],"%Y%m%d-%H%M%S"))
+
+image_id_to_datetime = {im['id']:im['date_captured'] for im in test_metadata['images']}
+image_path_to_id = {im['file_name']: im['id'] 
+                    for im in test_metadata['images']}
+faster_rcnn_model = load_model('../../Faster_RCNN_SS1_imagetensor/saved_model')
+    
 def segment(image):
     pass  # Implement your image segmentation model here...