demo work with demo button

.gitattributes

@@ -31,3 +31,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.tif filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -1,2 +1,4 @@
 venv
 __pycache__
+uploaded_samples
+demo_data/results

app.py

@@ -1,5 +1,41 @@
+import os
 import streamlit as st
 import zipfile
+import torch
+from utils import *
+import matplotlib.pyplot as plt
+
+# Load Model
+# @title Load pretrained weights
+
+best_model_daily_file_name = "best_model_daily.pth"
+best_model_annual_file_name = "best_model_annual.pth"
+
+first_input_batch = torch.zeros(71, 9, 5, 48, 48)
+# first_input_batch = first_input_batch.view(-1, *first_input_batch.shape[2:])
+daily_model = FPN(opt, first_input_batch, opt.win_size)
+annual_model = SimpleNN(opt)
+
+if torch.cuda.is_available():
+    daily_model = torch.nn.DataParallel(daily_model).cuda()
+    annual_model = torch.nn.DataParallel(annual_model).cuda()
+    daily_model = torch.nn.DataParallel(daily_model).cuda()
+    annual_model = torch.nn.DataParallel(annual_model).cuda()
+else:
+    daily_model = torch.nn.DataParallel(daily_model).cpu()
+    annual_model = torch.nn.DataParallel(annual_model).cpu()
+    daily_model = torch.nn.DataParallel(daily_model).cpu()
+    annual_model = torch.nn.DataParallel(annual_model).cpu()
+
+print('trying to resume previous saved models...')
+state = resume(
+    os.path.join(opt.resume_path, best_model_daily_file_name),
+    model=daily_model, optimizer=None)
+state = resume(
+    os.path.join(opt.resume_path, best_model_annual_file_name),
+    model=annual_model, optimizer=None)
+daily_model = daily_model.eval()
+annual_model = annual_model.eval()
 
 st.title('Sentinel 2 Crop Mapping')
 st.markdown('Using a <model> and giving a zip that contains 32 tiff named correctly you can reach prediction of crop mapping og the area.')
@@ -9,6 +45,117 @@ file_uploaded = st.file_uploader(
     type=["zip"],
     accept_multiple_files=False,
 )
+sample_path = None
 if file_uploaded is not None:
     with zipfile.ZipFile(file_uploaded, "r") as z:
-        z.extractall(".")
+        z.extractall("uploaded_samples")
+    sample_path = "uploaded_samples/" + file_uploaded.name[:-4]
+st.markdown('or use a demo sample')
+if st.button('sample 1'):
+    sample_path = 'demo_data/lombardia'
+
+if sample_path is not None:
+    st.markdown(f'elaborating {sample_path}...')
+
+    validationdataset = SentinelDailyAnnualDatasetNoLabel(
+        sample_path,
+        opt.years,
+        opt.classes_path,
+        opt.sample_duration,
+        opt.win_size,
+        tileids=None)
+    validationdataloader = torch.utils.data.DataLoader(
+        validationdataset, batch_size=opt.batch_size, shuffle=False, num_workers=opt.workers)
+
+    st.markdown(f'predict in progress...')
+
+    out_dir = os.path.join(opt.result_path, "seg_maps")
+    if not os.path.exists(out_dir):
+        os.makedirs(out_dir)
+    for i, (x_dailies, dates, dirs_path) in enumerate(validationdataloader):
+        with torch.no_grad():
+            # x_dailies, dates, dirs_path = next(iter(validationdataloader))
+            # reshape merging the first two dimensions
+            x_dailies = x_dailies.view(-1, *x_dailies.shape[2:])
+            if torch.cuda.is_available():
+                x_dailies = x_dailies.cuda()
+
+            feat_daily, outs_daily = daily_model.forward(x_dailies)
+            # return to original size of batch and year
+            outs_daily = outs_daily.view(
+                opt.batch_size, opt.sample_duration, *outs_daily.shape[1:])
+            feat_daily = feat_daily.view(
+                opt.batch_size, opt.sample_duration, *feat_daily.shape[1:])
+
+            _, out_annual = annual_model.forward(feat_daily)
+            pred_annual = torch.argmax(out_annual, dim=1).squeeze(1)
+            pred_annual = pred_annual.cpu().numpy()
+            # Remapping the labels
+            pred_annual_nn = ids_to_labels(
+                validationdataloader, pred_annual).astype(numpy.uint8)
+
+            for batch in range(feat_daily.shape[0]):
+                # _, profile = read(os.path.join(dirs_path[batch], '20191230_MSAVI.tif'))  # todo get the last image
+                _, tmp_path = get_patch_id(validationdataset.samples, 0)
+                dates = get_all_dates(
+                    tmp_path, validationdataset.max_seq_length)
+                last_tif_path = os.path.join(tmp_path, dates[-1] + ".tif")
+                _, profile = read(last_tif_path)
+                profile["name"] = dirs_path[batch]
+
+                pth = dirs_path[batch].split(os.path.sep)[-3:]
+                full_pth_patch = os.path.join(
+                    out_dir, pth[1] + '-' + pth[0], pth[2])
+
+                if not os.path.exists(full_pth_patch):
+                    os.makedirs(full_pth_patch)
+                full_pth_pred = os.path.join(
+                    full_pth_patch, 'patch-pred-nn.tif')
+                profile.update({
+                    'nodata': None,
+                    'dtype': 'uint8',
+                    'count': 1})
+                with rasterio.open(full_pth_pred, 'w', **profile) as dst:
+                    dst.write_band(1, pred_annual_nn[batch])
+
+                # patch_predictions = None
+                for ch in range(len(dates)):
+                    soft_seg = outs_daily[batch, ch, :, :, :]
+                    # transform probs into a hard segmentation
+                    pred_daily = torch.argmax(soft_seg, dim=0)
+                    pred_daily = pred_daily.cpu()
+                    daily_pred = ids_to_labels(
+                        validationdataloader, pred_daily).astype(numpy.uint8)
+                    # if patch_predictions is None:
+                    #     patch_predictions = numpy.expand_dims(daily_pred, axis=0)
+                    # else:
+                    #     patch_predictions = numpy.concatenate((patch_predictions, numpy.expand_dims(daily_pred, axis=0)),
+                    #                                           axis=0)
+
+                    # save GT image in  opt.root_path
+                    full_pth_date = os.path.join(
+                        full_pth_patch, dates[ch][batch] + f'-ch{ch}-b{batch}-daily-pred.tif')
+                    profile.update({
+                        'nodata': None,
+                        'dtype': 'uint8',
+                        'count': 1})
+                    with rasterio.open(full_pth_date, 'w', **profile) as dst:
+                        dst.write_band(1, daily_pred)
+
+    st.markdown('End prediction')
+
+    folder = "demo_data/results/seg_maps/example-lombardia/2"
+    paths = os.listdir(folder)
+
+    file_picker = st.selectbox("Select day predict (annual is patch-pred-nn.tif)", paths, index=paths.index('patch-pred-nn.tif'))
+
+    file_path = os.path.join(folder, file_picker)
+    print(file_path)
+    target, profile = read(file_path)
+    target = np.squeeze(target)
+    target = [classes_color_map[p] for p in target]
+
+    fig, ax = plt.subplots()
+    ax.imshow(target)
+    st.pyplot(fig)
+

demo_data/best_model_annual.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6c4d7a0be5d24a55e19f257b37b5bd034f8dceb82e4355e2c98c35995b7bce36
+size 326134860

demo_data/best_model_daily.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:820efc0edf6e22b12049a0dcce37880808d3e5c8773ed7b30441ab2893515253
+size 646423341

demo_data/classes-newmapping.txt

@@ -0,0 +1,8 @@
+0|Unknown|20180201|20181230
+2|Other cereals|20180324|20180512
+4|Woods and other tree crops|20180521|20180610
+7|Forage|20180402|20180504
+9|Corn|20180612|20180729
+12|Rice|20180606|20180902
+1,3,5,6,8,10,11,13,14,15,16,20|Unknow cropland|20180201|20181230
+17,18,19|No arable land|20180201|20181230