Update app.py

app.py

@@ -13,12 +13,72 @@ import tempfile
 from inference import split_image_from_dataframe
 from datetime import datetime
 from predict import extract_features, predict_similarity, compare_features, extract_features_cp
+import cv2
+from PIL import Image
+import os
+import numpy as np
+import urllib.request
+import glob
 
+# intake library and plugin
+# import intake
+# from intake_zenodo_fetcher import download_zenodo_files_for_entry
 
+# geospatial libraries
+import geopandas as gpd
 
+from rasterio.transform import from_origin
+import rasterio.features
+
+import fiona
+
+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'
+
+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()
+
+# 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
+
+### 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
+
+#### 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)
 
-import rasterio
-import geopandas as gpd
 
 model = main.deepforest()
 model.use_release()