Update app.py

app.py

@@ -25,59 +25,59 @@ import glob
 # from intake_zenodo_fetcher import download_zenodo_files_for_entry
 
 # geospatial libraries
-import geopandas as gpd
+# import geopandas as gpd
 
-from rasterio.transform import from_origin
-import rasterio.features
+# from rasterio.transform import from_origin
+# import rasterio.features
 
-import fiona
+# import fiona
 
-from shapely.geometry import shape, mapping, box
-from shapely.geometry.multipolygon import MultiPolygon
+# from shapely.geometry import shape, mapping, box
+# from shapely.geometry.multipolygon import MultiPolygon
 
-# machine learning libraries
-from detectron2 import model_zoo
-from detectron2.engine import DefaultPredictor
-from detectron2.utils.visualizer import Visualizer, ColorMode
-from detectron2.config import get_cfg
-from detectron2.engine import DefaultTrainer
-# define the URL to retrieve the model
-fn = 'model_final.pth'
-url = f'https://zenodo.org/record/5515408/files/{fn}?download=1'
+# # machine learning libraries
+# from detectron2 import model_zoo
+# from detectron2.engine import DefaultPredictor
+# from detectron2.utils.visualizer import Visualizer, ColorMode
+# from detectron2.config import get_cfg
+# from detectron2.engine import DefaultTrainer
+# # define the URL to retrieve the model
+# fn = 'model_final.pth'
+# url = f'https://zenodo.org/record/5515408/files/{fn}?download=1'
 
-urllib.request.urlretrieve(url, config['model'] + '/' + fn)
+# urllib.request.urlretrieve(url, config['model'] + '/' + fn)
 
 # import geoviews.tile_sources as gts
 
 # import hvplot.pandas
 # import hvplot.xarray
 
-# hv.extension('bokeh', width=100)
-cfg = get_cfg()
+# # hv.extension('bokeh', width=100)
+# cfg = get_cfg()
 
-# if you want to make predictions using a CPU, run the following line. If using GPU, hash it out.
-cfg.MODEL.DEVICE='cuda'
+# # if you want to make predictions using a CPU, run the following line. If using GPU, hash it out.
+# cfg.MODEL.DEVICE='cuda'
 
-# model and hyperparameter selection
-cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml"))
-cfg.DATALOADER.NUM_WORKERS = 2
-cfg.SOLVER.IMS_PER_BATCH = 2
-cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1
+# # model and hyperparameter selection
+# cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml"))
+# cfg.DATALOADER.NUM_WORKERS = 2
+# cfg.SOLVER.IMS_PER_BATCH = 2
+# cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1
 
-### path to the saved pre-trained model weights
-cfg.MODEL.WEIGHTS = config['model'] + '/model_final.pth'
+# ### path to the saved pre-trained model weights
+# cfg.MODEL.WEIGHTS = config['model'] + '/model_final.pth'
 
-# set confidence threshold at which we predict
-cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.15
+# # set confidence threshold at which we predict
+# cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.15
 
-#### Settings for predictions using detectron config
+# #### Settings for predictions using detectron config
 
-predictor = DefaultPredictor(cfg)
-outputs = predictor(im)
-v = Visualizer(im[:, :, ::-1], scale=1.5, instance_mode=ColorMode.IMAGE_BW)   # remove the colors of unsegmented pixels
-v = v.draw_instance_predictions(outputs["instances"].to("cpu"))
-image = cv2.cvtColor(v.get_image()[:, :, :], cv2.COLOR_BGR2RGB)
-st.image(image, caption='Segmented Panoramic Image Detecttree', channels ='RGB', use_column_width=True)
+# predictor = DefaultPredictor(cfg)
+# outputs = predictor(im)
+# v = Visualizer(im[:, :, ::-1], scale=1.5, instance_mode=ColorMode.IMAGE_BW)   # remove the colors of unsegmented pixels
+# v = v.draw_instance_predictions(outputs["instances"].to("cpu"))
+# image = cv2.cvtColor(v.get_image()[:, :, :], cv2.COLOR_BGR2RGB)
+# st.image(image, caption='Segmented Panoramic Image Detecttree', channels ='RGB', use_column_width=True)
 
 
 model = main.deepforest()