remove Black Sea 200k data, waiting on approval of Zenodo repository

README.md

@@ -92,8 +92,8 @@ conda env create -f environment_linux.yml
 | ------------------------- | ------------------------------------------------------------------------------------------------------------------------ | ---------------- |
 | **Train (10k)**           | [![](badges/google_drive_badge.svg)](https://drive.google.com/file/d/1Qko68JTZT-JLHKwSJJvvKUQEjmcy0V0j/view?usp=sharing) | -                |
 | **Train (20k)**           | [![](badges/google_drive_badge.svg)](https://drive.google.com/file/d/1uUcSW5s_jm5W-AQeeNxJKbIr6CR5fJIP/view?usp=sharing) | -                |
-| **Test (1k)**             | Upcoming | -                |
-| **Black Sea (200k)**      | Upcoming | -                |
+| **Test (1k)**             | [![](badges/google_drive_badge.svg)](https://drive.google.com/file/d/1A6IK9IQjFN9JMNx3bUkcWdlO8YN8PbaC/view?usp=sharing) | -                |
+| **Black Sea (200k)**      | **Upcoming** | -                |
 | **La Jument 2019 (10k)** | **Upcoming**                                                                                                             | -                |
 
 ## 3. Training

segmentation/extract_detections_by_labelling.py

@@ -173,7 +173,7 @@
             mk1 = np.zeros(frame.shape)
             mk1[df_frm["i"].values, df_frm["j"].values] = 1
 
-            # lalbe the mask
+            # label the mask
             lbl = label(mk1, connectivity=connectivity)
 
             # get region properties

segmentation/predict.py

@@ -0,0 +1,118 @@
+"""
+Use a pre-trained segmentation model. Make sure your input is 256x256.
+
+PROGRAM   : predict.py
+POURPOSE  : Get the regions in an image where waves are actively breaking
+AUTHOR    : Caio Eadi Stringari
+EMAIL     : [email protected]
+V2.0      : 06/10/2020 [Caio Stringari]
+"""
+
+import os
+import argparse
+
+from glob import glob
+from natsort import natsorted
+
+import numpy as np
+
+from skimage.io import imread
+from skimage.color import grey2rgb
+
+import tensorflow as tf
+
+# progress bar
+from tqdm import tqdm
+
+# quite skimage warnings
+import warnings
+
+# plot
+import matplotlib.pyplot as plt
+
+tf.get_logger().setLevel('INFO')
+warnings.filterwarnings("ignore")
+
+
+def display_mask(val_preds, i):
+    """Display a model's prediction."""
+    mask = np.argmax(val_preds[i], axis=-1)
+    mask = np.expand_dims(mask, axis=-1)
+    return mask
+
+
+def main():
+    """Call the main program."""
+    # i/o
+    model = args.model[0]  # pre-trained model
+    inp_data = args.input[0]  # frames to be segmented
+    out_data = args.output[0]  # output csv file
+
+    # create output
+    os.makedirs(out_data, exist_ok=True)
+
+    # load the model
+    M = tf.keras.models.load_model(model)
+
+    # verify if the input path exists,
+    # if it does, then get the frame names
+    if os.path.isdir(inp_data):
+        images = natsorted(glob(inp_data + "/*"))
+    else:
+        raise IOError("No such file or directory \"{}\"".format(inp_data))
+
+    # --- loop over frames ---
+    pbar = tqdm(total=len(images))
+
+    for k, image in enumerate(images):
+
+        # print("-- plotting frame {} of {}".format(k+1, total_frames), end="\r")
+
+        # load image
+        img = grey2rgb(imread(image))
+
+        # predict
+        pred = M.predict(np.expand_dims(img/255, axis=0))  # very important to normalize your data !
+        prd = np.squeeze(np.argmax(pred, axis=-1))
+
+        # plot
+        fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6),
+                                       sharex=True, sharey=True)
+        ax1.imshow(np.squeeze(img))
+        ax2.imshow(np.squeeze(prd))
+        fig.tight_layout()
+        plt.savefig(os.path.join(out_data, str(k).zfill(6) + ".png"),
+                    pad_inches=0.1, bbox_inches='tight')
+        plt.close()
+
+        pbar.update()
+
+
+if __name__ == '__main__':
+
+    parser = argparse.ArgumentParser(description='Predict active wave breaking segmentation')
+
+    parser.add_argument('--model', "-M",
+                        nargs=1,
+                        dest='model',
+                        help='pre-trained model in .h5 format',
+                        required=True,
+                        action='store')
+
+    parser.add_argument("--input", "-i", "--frames", "-frames",
+                        nargs=1,
+                        action="store",
+                        dest="input",
+                        required=True,
+                        help="Input path with data.",)
+
+    parser.add_argument("--output", "-o",
+                        nargs=1,
+                        action="store",
+                        dest="output",
+                        required=True,
+                        help="Output path.",)
+
+    args = parser.parse_args()
+
+    main()

segmentation/predict_on_sequence.py

@@ -102,9 +102,12 @@ def display_mask(val_preds, i):
 def make_plot(img, df, roi_patch=False, total_frames=-1, out_path="plt",
               block_shape=[256, 256]):
     """Plot the results."""
-    # crate a mask
-    brk_mask = np.zeros([img.shape[0], img.shape[0]]).astype(int)
-    brk_mask[df["i"].values, df["j"].values] = 1
+    # create a mask
+    try:
+        brk_mask = np.zeros([img.shape[0], img.shape[0]]).astype(int)
+        brk_mask[df["i"].values, df["j"].values] = 1
+    except Exception:
+        brk_mask = np.zeros([img.shape[0], img.shape[0]]).astype(int)
     brk_mask = np.ma.masked_less(brk_mask, 1)
     binmap = mpl.colors.ListedColormap("red")