remove fog and shadow augmentations

add install script

.gitignore

@@ -7,4 +7,6 @@ models/
 *.pt
 *.onnx
 *.rknn
-*.mp4
+*.mp4
+venv
+*.engine

augmentation.py

@@ -24,17 +24,10 @@ class Augmentator:
         self.transform = A.Compose([
             # Flips, rotations and brightness
             A.HorizontalFlip(p=0.6),
-            A.RandomBrightnessContrast(p=0.4, brightness_limit=(-0.1, 0.1), contrast_limit=(-0.1, 0.1)),
+            A.RandomBrightnessContrast(p=0.4, brightness_limit=(-0.3, 0.3), contrast_limit=(-0.05, 0.05)),
-            A.Affine(p=0.7, scale=(0.8, 1.2), rotate=(-20, 20), shear=(-10, 10), translate_percent=0.2),
+            A.Affine(p=0.8, scale=(0.8, 1.2), rotate=(-35, 35), shear=(-10, 10)),
 
-            # Weather
+            A.MotionBlur(p=0.1, blur_limit=(1, 2)),
-            A.RandomFog(p=0.3, fog_coef_range=(0, 0.3)),
-            A.RandomShadow(p=0.2),
-
-            # Image Quality/Noise
-            A.MotionBlur(p=0.2, blur_limit=(3, 5)),
-
-            # Color Variations
             A.HueSaturationValue(p=0.4, hue_shift_limit=10, sat_shift_limit=10, val_shift_limit=10)
         ], bbox_params=A.BboxParams(format='yolo'))
 

install.sh

@@ -0,0 +1,8 @@
+echo install python and dependencies
+if not exist venv (
+    python -m venv venv
+)
+
+venv/bin/python -m pip install --upgrade pip
+venv/bin/pip install -r requirements.txt
+venv\Scripts\pip install --upgrade pyinstaller pyinstaller-hooks-contrib

preprocess-train.py

@@ -1,5 +1,5 @@
 from augmentation import Augmentator
 from train import train_dataset, convert2rknn
 
-Augmentator().augment_annotations(from_scratch=True)
+Augmentator().augment_annotations()
 train_dataset(from_scratch=True)

train.py

@@ -38,7 +38,6 @@ DEFAULT_CLASS_NUM = 80
 total_files_copied = 0
 
 def form_dataset(from_date: datetime):
-
     makedirs(today_dataset, exist_ok=True)
     images = []
     old_images = []
@@ -180,10 +179,10 @@ def train_dataset(existing_date=None, from_scratch=False):
         cur_folder = f'{prefix}{existing_date}'
         cur_dataset = path.join(datasets_dir, f'{prefix}{existing_date}')
     else:
-        if from_scratch:
+        # if from_scratch and Path(today_dataset).exists():
-            shutil.rmtree(today_dataset)
+        #     shutil.rmtree(today_dataset)
-        form_dataset(latest_date)
+        # form_dataset(latest_date)
-        create_yaml()
+        # create_yaml()
         cur_folder = today_folder
         cur_dataset = today_dataset