Add finetune colab, better flood fill

.gitignore

@@ -26,3 +26,4 @@ Segmentation_Model_Output
 1_Detection_Model/runs/
 Baseline_2_Model_Output/
 Models/
+FineTuneDataset/

0_Labelling_Tools/1_Segmentation_Label_Flood_Fill.py

@@ -10,33 +10,36 @@ def parse_args():
     parser = argparse.ArgumentParser(
         formatter_class=argparse.ArgumentDefaultsHelpFormatter)
     parser.add_argument(
-        'input_dir', help='input label directory produced by EISeg')
+        'input_dir', help='input grey-scale label directory produced by EISeg')
     parser.add_argument(
         'output_dir', help='output YOLOv5 bounding box label directory')
     return parser.parse_args()
 
 
 def flood_fill(x, y, bx, ex, by, ey):
+    # curreny (x, y), begin x, end x, begin y, end y
+    global np_data
     nexts = [(1, 0, 0, 1, 0, 0), (-1, 0, 1, 0, 0, 0),
              (0, 1, 0, 0, 0, 1), (0, -1, 0, 0, 1, 0)]
-    for next_x, next_y, off_bx, off_ex, off_by, off_ey in nexts:
-        new_x = x + next_x
-        new_y = y + next_y
-
-        if new_x < 0 or new_x >= np_data.shape[0] or new_y < 0 or new_y >= np_data.shape[1]:
-            continue
-        if np_data[new_x][new_y] != 1:
-            continue
-
-        np_data[new_x][new_y] = 0
-
-        pbx, pex, pby, pey = flood_fill(
-            new_x, new_y, new_x + off_bx, new_x + off_ex, new_y + off_by, new_y + off_ey)
-
-        bx = min(bx, pbx)
-        ex = max(ex, pex)
-        by = min(by, pby)
-        ey = max(ey, pey)
+    q = [(x, y)]
+    while(len(q) != 0):
+        (x, y) = q.pop()
+        for next_x, next_y, off_bx, off_ex, off_by, off_ey in nexts:
+            new_x = x + next_x
+            new_y = y + next_y
+
+            if new_x < 0 or new_x >= np_data.shape[0] or new_y < 0 or new_y >= np_data.shape[1]:
+                continue
+            if np_data[new_x][new_y] != 1:
+                continue
+
+            np_data[new_x][new_y] = 0
+            q.append((new_x, new_y))
+
+            bx = min(bx, new_x + off_bx)
+            ex = max(ex, new_x + off_ex)
+            by = min(by, new_y + off_by)
+            ey = max(ey, new_y + off_ey)
 
     return bx, ex, by, ey
 
@@ -57,7 +60,6 @@ def segmentation_label_flood_fill(args):
 
         data = Image.open(filename)
         data = data.convert('L')
-        data.show()
         np_data = np.array(data)
         np_data[np_data != 0] = 1
 

1_Detection_Model/data/dtsegnet_finetune.yaml

@@ -0,0 +1,8 @@
+path: ../FineTuneDataset
+train: images # train images (relative to 'path')
+val: images # val images (relative to 'path')
+test: images # test images (relative to 'path')
+
+# Classes
+nc: 1  # number of classes
+names: ['precipitate']  # class names

3_Segmentation_Model/configs/dtsegnet/finetune.yml

@@ -0,0 +1,69 @@
+_base_: '../_base_/dtsegnet.yml'
+
+batch_size: 1
+iters: 2000
+
+train_dataset:
+  type: Dataset
+  dataset_root: /content/DT_SegNet/FineTuneDataset/Segmentation_Dataset
+  train_path: /content/DT_SegNet/FineTuneDataset/Segmentation_Dataset/train.txt
+  num_classes: 2
+  transforms:
+    - type: Resize
+      target_size: [512, 512]
+    - type: RandomHorizontalFlip
+    - type: RandomVerticalFlip
+    - type: Normalize
+  mode: train
+
+val_dataset:
+  type: Dataset
+  dataset_root: /content/DT_SegNet/FineTuneDataset/Segmentation_Dataset
+  val_path: /content/DT_SegNet/FineTuneDataset/Segmentation_Dataset/train.txt
+  num_classes: 2
+  transforms:
+    - type: Resize
+      target_size: [512, 512]
+    - type: Normalize
+  mode: val
+
+test_dataset:
+  type: Dataset
+  dataset_root: /content/DT_SegNet/FineTuneDataset/Segmentation_Dataset
+  val_path: /content/DT_SegNet/FineTuneDataset/Segmentation_Dataset/train.txt
+  num_classes: 2
+  transforms:
+    - type: Resize
+      target_size: [512, 512]
+    - type: Normalize
+  mode: val
+
+model:
+  type: SegFormer
+  backbone:
+    type: MixVisionTransformer_B1
+  embedding_dim: 256
+  num_classes: 2
+  pretrained: /content/DT_SegNet/Models/segmentation.pdparams
+
+optimizer:
+  _inherited_: False
+  type: AdamW
+  beta1: 0.9
+  beta2: 0.999
+  weight_decay: 0.01
+
+lr_scheduler:
+  type: PolynomialDecay
+  learning_rate: 0.00006
+  power: 1
+
+loss:
+  types:
+    - type: CrossEntropyLoss
+  coef: [1]
+
+test_config:
+    is_slide: True
+    crop_size: [1024, 1024]
+    stride: [768, 768]