Delete app.py

app.py

@@ -1,270 +0,0 @@
-######### pull files
-import os
-from huggingface_hub import hf_hub_download
-config_path=hf_hub_download(repo_id="ibm-nasa-geospatial/Prithvi-100M-multi-temporal-crop-classification", 
-                            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", 
-                     filename='multi_temporal_crop_classification_Prithvi_100M.pth', 
-                     token=os.environ.get("token"))
-##########
-import argparse
-from mmcv import Config
-
-from mmseg.models import build_segmentor
-
-from mmseg.datasets.pipelines import Compose, LoadImageFromFile
-
-import rasterio
-import torch
-
-from mmseg.apis import init_segmentor
-
-from mmcv.parallel import collate, scatter
-
-import numpy as np
-import glob
-import os
-
-import time
-
-import numpy as np
-import gradio as gr
-from functools import partial
-
-import pdb
-
-import matplotlib.pyplot as plt
-
-from skimage import exposure 
-
-cdl_color_map = [{'value': 1, 'label': 'Natural vegetation', 'rgb': (233,255,190)},
-                 {'value': 2, 'label': 'Forest', 'rgb': (149,206,147)},
-                 {'value': 3, 'label': 'Corn', 'rgb': (255,212,0)},
-                 {'value': 4, 'label': 'Soybeans', 'rgb': (38,115,0)},
-                 {'value': 5, 'label': 'Wetlands', 'rgb': (128,179,179)},
-                 {'value': 6, 'label': 'Developed/Barren', 'rgb': (156,156,156)},
-                 {'value': 7, 'label': 'Open Water', 'rgb': (77,112,163)},
-                 {'value': 8, 'label': 'Winter Wheat', 'rgb': (168,112,0)},
-                 {'value': 9, 'label': 'Alfalfa', 'rgb': (255,168,227)},
-                 {'value': 10, 'label': 'Fallow/Idle cropland', 'rgb': (191,191,122)},
-                 {'value': 11, 'label': 'Cotton', 'rgb':(255,38,38)},
-                 {'value': 12, 'label': 'Sorghum', 'rgb':(255,158,15)},
-                 {'value': 13, 'label': 'Other', 'rgb':(0,175,77)}]
-
-
-def apply_color_map(rgb, color_map=cdl_color_map):
-    
-    
-    rgb_mapped = rgb.copy()
-    
-    for map_tmp in cdl_color_map:
-        
-        for i in range(3):
-            rgb_mapped[i] = np.where((rgb[0] == map_tmp['value']) & (rgb[1] == map_tmp['value']) & (rgb[2] == map_tmp['value']), map_tmp['rgb'][i], rgb_mapped[i])
-    
-    return rgb_mapped    
-    
-
-def stretch_rgb(rgb):
-    
-    ls_pct=0
-    pLow, pHigh = np.percentile(rgb[~np.isnan(rgb)], (ls_pct,100-ls_pct))
-    img_rescale = exposure.rescale_intensity(rgb, in_range=(pLow,pHigh))
-    
-    return img_rescale
-
-def open_tiff(fname):
-    
-    with rasterio.open(fname, "r") as src:
-        
-        data = src.read()
-        
-    return data
-
-def write_tiff(img_wrt, filename, metadata):
-
-    """
-    It writes a raster image to file.
-
-    :param img_wrt: numpy array containing the data (can be 2D for single band or 3D for multiple bands)
-    :param filename: file path to the output file
-    :param metadata: metadata to use to write the raster to disk
-    :return:
-    """
-
-    with rasterio.open(filename, "w", **metadata) as dest:
-
-        if len(img_wrt.shape) == 2:
-            
-            img_wrt = img_wrt[None]
-
-        for i in range(img_wrt.shape[0]):
-            dest.write(img_wrt[i, :, :], i + 1)
-    
-    return filename
-            
-
-def get_meta(fname):
-    
-    with rasterio.open(fname, "r") as src:
-        
-        meta = src.meta
-        
-    return meta
-
-def preprocess_example(example_list):
-    
-    example_list = [os.path.join(os.path.abspath(''), x) for x in example_list]
-    
-    return example_list
-
-
-def inference_segmentor(model, imgs, custom_test_pipeline=None):
-    """Inference image(s) with the segmentor.
-
-    Args:
-        model (nn.Module): The loaded segmentor.
-        imgs (str/ndarray or list[str/ndarray]): Either image files or loaded
-            images.
-
-    Returns:
-        (list[Tensor]): The segmentation result.
-    """
-    cfg = model.cfg
-    device = next(model.parameters()).device  # model device
-    # build the data pipeline
-    test_pipeline = [LoadImageFromFile()] + cfg.data.test.pipeline[1:] if custom_test_pipeline == None else custom_test_pipeline
-    test_pipeline = Compose(test_pipeline)
-    # prepare data
-    data = []
-    imgs = imgs if isinstance(imgs, list) else [imgs]
-    for img in imgs:
-        img_data = {'img_info': {'filename': img}}
-        img_data = test_pipeline(img_data)
-        data.append(img_data)
-    # print(data.shape)
-    
-    data = collate(data, samples_per_gpu=len(imgs))
-    if next(model.parameters()).is_cuda:
-        # data = collate(data, samples_per_gpu=len(imgs))
-        # scatter to specified GPU
-        data = scatter(data, [device])[0]
-    else:
-        # img_metas = scatter(data['img_metas'],'cpu')
-        # data['img_metas'] = [i.data[0] for i in data['img_metas']]
-        
-        img_metas = data['img_metas'].data[0]
-        img = data['img']
-        data = {'img': img, 'img_metas':img_metas}
-    
-    with torch.no_grad():
-        result = model(return_loss=False, rescale=True, **data)
-    return result
-
-
-def process_rgb(input, mask, indexes):
-
-    
-    rgb = stretch_rgb((input[indexes, :, :].transpose((1,2,0))/10000*255).astype(np.uint8))
-    rgb = np.where(mask.transpose((1,2,0)) == 1, 0, rgb)
-    rgb = np.where(rgb < 0, 0, rgb)
-    rgb = np.where(rgb > 255, 255, rgb)
-
-    return rgb
-
-def inference_on_file(target_image, model, custom_test_pipeline):
-
-    target_image = target_image.name
-    time_taken=-1
-    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
-    input = open_tiff(target_image)
-    meta = get_meta(target_image)
-    mask = np.where(input == meta['nodata'], 1, 0)
-    mask = np.max(mask, axis=0)[None]
-    
-    rgb1 = process_rgb(input, mask, [2, 1, 0])
-    rgb2 = process_rgb(input, mask, [8, 7, 6])
-    rgb3 = process_rgb(input, mask, [14, 13, 12])
-
-    result[0] = np.where(mask == 1, 0, result[0])
-
-    et = time.time()
-    time_taken = np.round(et - st, 1)
-    print(f'Inference completed in {str(time_taken)} seconds')
-    
-    output=result[0][0] + 1
-    output = np.vstack([output[None], output[None], output[None]]).astype(np.uint8)
-    output=apply_color_map(output).transpose((1,2,0))
-        
-    return rgb1,rgb2,rgb3,output
-
-def process_test_pipeline(custom_test_pipeline, bands=None):
-    
-    # change extracted bands if necessary
-    if bands is not None:
-        
-        extract_index = [i for i, x in enumerate(custom_test_pipeline) if x['type'] == 'BandsExtract' ]
-        
-        if len(extract_index) > 0:
-            
-            custom_test_pipeline[extract_index[0]]['bands'] = eval(bands)
-            
-    collect_index = [i for i, x in enumerate(custom_test_pipeline) if x['type'].find('Collect') > -1]
-    
-    # adapt collected keys if necessary
-    if len(collect_index) > 0:
-        
-        keys = ['img_info', 'filename', 'ori_filename', 'img', 'img_shape', 'ori_shape', 'pad_shape', 'scale_factor', 'img_norm_cfg']
-        custom_test_pipeline[collect_index[0]]['meta_keys'] = keys
-    
-    return custom_test_pipeline
-
-config = Config.fromfile(config_path)
-config.model.backbone.pretrained=None
-model = init_segmentor(config, ckpt, device='cpu')
-custom_test_pipeline=process_test_pipeline(model.cfg.data.test.pipeline, None)
-
-func = partial(inference_on_file, model=model, custom_test_pipeline=custom_test_pipeline)
-
-with gr.Blocks() as demo:
-   
-    gr.Markdown(value='# Prithvi multi temporal crop classification')
-    gr.Markdown(value='''Prithvi is a first-of-its-kind temporal Vision transformer pretrained by the IBM and NASA team on continental US Harmonised Landsat Sentinel 2 (HLS) data. This demo showcases how the model was finetuned to classify crop and other land use categories using multi temporal data. More detailes can be found [here](https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M-multi-temporal-crop-classification).\n
-    The user needs to provide an HLS geotiff image, including 18 bands for 3 time-step, and each time-step includes the channels described above (Blue, Green, Red, Narrow NIR, SWIR, SWIR 2) in order.
-    ''')
-    with gr.Row():
-        with gr.Column():
-            inp = gr.File()
-            btn = gr.Button("Submit")
-            
-    with gr.Row():
-        inp1=gr.Image(image_mode='RGB', scale=10, label='T1')
-        inp2=gr.Image(image_mode='RGB', scale=10, label='T2')
-        inp3=gr.Image(image_mode='RGB', scale=10, label='T3')
-        out = gr.Image(image_mode='RGB', scale=10, label='Model prediction')
-        # gr.Image(value='Legend.png', image_mode='RGB', scale=2, show_label=False)
-    
-    btn.click(fn=func, inputs=inp, outputs=[inp1, inp2, inp3, out])
-    
-    with gr.Row():
-        with gr.Column():
-            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)
-        with gr.Column():
-            gr.Markdown(value='### Model prediction legend')
-            gr.Image(value='Legend.png', image_mode='RGB', show_label=False)
-    
-
-demo.launch()