solved merge

Dockerfile

@@ -59,7 +59,6 @@ RUN pip3 install openmim
 
 RUN conda install -c conda-forge gradio -y 
 
-
 WORKDIR /home/user
 
 RUN --mount=type=secret,id=git_token,mode=0444,required=true \
@@ -72,7 +71,7 @@ RUN pip3 install -e .
 
 RUN mim install mmcv-full==1.6.2 -f https://download.openmmlab.com/mmcv/dist/11.5/1.11.0/index.html
 
-RUN pip3 install rasterio
+RUN pip3 install rasterio scikit-image
 # Set the working directory to the user's home directory
 WORKDIR $HOME/app
 

README.md

@@ -8,4 +8,4 @@ pinned: false
 license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -5,7 +5,11 @@ config_path=hf_hub_download(repo_id="ibm-nasa-geospatial/Prithvi-100M-multi-temp
                             filename="multi_temporal_crop_classification_Prithvi_100M.py", 
                             token=os.environ.get("token"))
 ckpt=hf_hub_download(repo_id="ibm-nasa-geospatial/Prithvi-100M-multi-temporal-crop-classification", 
+<<<<<<< HEAD
                      filename='multi_temporal_crop_classification_Prithvi_100M.pth', 
+=======
+                     filename='multi_temporal_crop_classification_best_mIoU_epoch_66.pth', 
+>>>>>>> 889a651 (add files)
                      token=os.environ.get("token"))
 ##########
 import argparse
@@ -133,6 +137,7 @@ def inference_on_file(target_image, model, custom_test_pipeline):
 
     # output_image = target_image.replace('.tif', '_pred.tif')
     time_taken=-1
+<<<<<<< HEAD
     st = time.time()
     print('Running inference...')
     result = inference_segmentor(model, target_image, custom_test_pipeline)
@@ -162,6 +167,41 @@ def inference_on_file(target_image, model, custom_test_pipeline):
     print(f'Inference completed in {str(time_taken)} seconds')
         
     return rgb1,rgb2,rgb3, result[0][0]
+=======
+    try:
+        st = time.time()
+        print('Running inference...')
+        result = inference_segmentor(model, target_image, custom_test_pipeline)
+        print("Output has shape: " + str(result[0].shape))
+        
+        ##### get metadata mask
+        mask = open_tiff(target_image)
+        # rgb = mask[[2, 1, 0], :, :].transpose((1,2,0))
+        rgb1 = mask[[2, 1, 0], :, :].transpose((1,2,0))
+        rgb2 = mask[[8, 7, 6], :, :].transpose((1,2,0))
+        rgb3 = mask[[14, 13, 12], :, :].transpose((1,2,0))
+        meta = get_meta(target_image)
+        mask = np.where(mask == meta['nodata'], 1, 0)
+        mask = np.max(mask, axis=0)[None]
+        
+        result[0] = np.where(mask == 1, -1, result[0])
+        
+        ##### Save file to disk
+        meta["count"] = 1
+        meta["dtype"] = "int16"
+        meta["compress"] = "lzw"
+        meta["nodata"] = -1
+        print('Saving output...')
+        # write_tiff(result[0], output_image, meta)
+        et = time.time()
+        time_taken = np.round(et - st, 1)
+        print(f'Inference completed in {str(time_taken)} seconds')
+        
+    except:
+        print(f'Error on image {target_image} \nContinue to next input')
+        
+    return rgb, result[0][0]*255
+>>>>>>> 889a651 (add files)
 
 def process_test_pipeline(custom_test_pipeline, bands=None):
     
@@ -184,7 +224,10 @@ def process_test_pipeline(custom_test_pipeline, bands=None):
     
     return custom_test_pipeline
 
+<<<<<<< HEAD
 
+=======
+>>>>>>> 889a651 (add files)
 config = Config.fromfile(config_path)
 config.model.backbone.pretrained=None
 model = init_segmentor(config, ckpt, device='cpu')
@@ -217,6 +260,7 @@ with gr.Blocks() as demo:
     
     btn.click(fn=func, inputs=inp, outputs=[inp1, inp2, inp3, out])
     
+<<<<<<< HEAD
     # with gr.Row():
     #     gr.Examples(examples=["chip_102_345_merged.tif", "chip_104_104_merged.tif", "chip_109_421_merged.tif"],
     #                 inputs=inp,
@@ -225,5 +269,17 @@ with gr.Blocks() as demo:
     #                 fn=func,
     #                 cache_examples=True,
     # )
+=======
+    with gr.Row():
+        gr.Examples(examples=["chip_102_345_merged.tif",
+                     "chip_104_104_merged.tif",
+                     "chip_109_421_merged.tif"],
+                    inputs=inp,
+                    outputs=[inp1, inp2, inp3, out],
+                    preprocess=preprocess_example,
+                    fn=func,
+                    cache_examples=True,
+    )
+>>>>>>> 889a651 (add files)
 
 demo.launch()