[AZ-165] [AZ-166] [AZ-167] [AZ-168] [AZ-169] Complete refactoring: delete dead augmentation.py, move tasks to done

- Delete src/augmentation.py (dead code with broken processed_dir refs after AZ-168)
- Remove dead Augmentator import from manual_run.py
- Move all 5 refactoring tasks from todo/ to done/
- Update autopilot state: Step 7 Refactor complete, advance to Step 8 New Task
- Strengthen tracker.mdc: NEVER use ADO MCP

Made-with: Cursor

.cursor/rules/tracker.mdc

@@ -1,7 +1,8 @@
 # Work Item Tracker
 
 - Use **Jira** as the sole work item tracker (MCP server: `user-Jira-MCP-Server`)
-- Do NOT use Azure DevOps for work item management
+- **NEVER** use Azure DevOps (ADO) MCP for any purpose — no reads, no writes, no queries
+- Before interacting with any tracker, read this rule file first
 - Jira cloud ID: `denyspopov.atlassian.net`
 - Project key: `AZ`
 - Project name: AZAION

_docs/_autopilot_state.md

@@ -2,8 +2,8 @@
 
 ## Current Step
 flow: existing-code
-step: 7
+step: 8
-name: Refactor
+name: New Task
-status: in_progress
+status: not_started
-sub_step: 4 — Execution (All batches done: AZ-165, AZ-166, AZ-167, AZ-168, AZ-169; pending: final test run, commit)
+sub_step: 0
 retry_count: 0

src/augmentation.py

@@ -1,152 +0,0 @@
-import concurrent.futures
-import os.path
-import shutil
-import time
-from datetime import datetime
-from pathlib import Path
-
-import albumentations as A
-import cv2
-import numpy as np
-
-import constants
-from dto.imageLabel import ImageLabel
-
-
-class Augmentator:
-    def __init__(self):
-        self.total_files_processed = 0
-        self.total_images_to_process = 0
-
-        self.correct_margin = 0.0005
-        self.correct_min_bbox_size = 0.01
-
-        self.transform = A.Compose([
-            A.HorizontalFlip(p=0.6),
-            A.RandomBrightnessContrast(p=0.4, brightness_limit=(-0.3, 0.3), contrast_limit=(-0.05, 0.05)),
-            A.Affine(p=0.8, scale=(0.8, 1.2), rotate=(-35, 35), shear=(-10, 10)),
-
-            A.MotionBlur(p=0.1, blur_limit=(1, 2)),
-            A.HueSaturationValue(p=0.4, hue_shift_limit=10, sat_shift_limit=10, val_shift_limit=10)
-        ], bbox_params=A.BboxParams(format='yolo'))
-
-    def correct_bboxes(self, labels):
-        res = []
-        for bboxes in labels:
-            x = bboxes[0]
-            y = bboxes[1]
-            half_width = 0.5*bboxes[2]
-            half_height = 0.5*bboxes[3]
-
-            # calc how much bboxes are outside borders ( +small margin ).
-            # value should be negative. If it's positive, then put 0, as no correction
-            w_diff = min((1 - self.correct_margin) - (x + half_width), (x - half_width) - self.correct_margin, 0)
-            w = bboxes[2] + 2*w_diff
-            if w < self.correct_min_bbox_size:
-                continue
-            h_diff = min((1 - self.correct_margin) - (y + half_height), ((y - half_height) - self.correct_margin), 0)
-            h = bboxes[3] + 2 * h_diff
-            if h < self.correct_min_bbox_size:
-                continue
-            res.append([x, y, w, h, bboxes[4]])
-        return res
-        pass
-
-    def augment_inner(self, img_ann: ImageLabel) -> [ImageLabel]:
-        results = []
-        labels = self.correct_bboxes(img_ann.labels)
-        if len(labels) == 0 and len(img_ann.labels) != 0:
-            print('no labels but was!!!')
-        results.append(ImageLabel(
-                image=img_ann.image,
-                labels=img_ann.labels,
-                image_path=os.path.join(constants.config.processed_images_dir, Path(img_ann.image_path).name),
-                labels_path=os.path.join(constants.config.processed_labels_dir, Path(img_ann.labels_path).name)
-            )
-        )
-        for i in range(7):
-            try:
-                res = self.transform(image=img_ann.image, bboxes=labels)
-                path = Path(img_ann.image_path)
-                name = f'{path.stem}_{i + 1}'
-                img = ImageLabel(
-                    image=res['image'],
-                    labels=res['bboxes'],
-                    image_path=os.path.join(constants.config.processed_images_dir, f'{name}{path.suffix}'),
-                    labels_path=os.path.join(constants.config.processed_labels_dir, f'{name}.txt')
-                )
-                results.append(img)
-            except Exception as e:
-                print(f'Error during transformation: {e}')
-        return results
-
-    def read_labels(self, labels_path) -> [[]]:
-        with open(labels_path, 'r') as f:
-            rows = f.readlines()
-            arr = []
-            for row in rows:
-                str_coordinates = row.split(' ')
-                class_num = str_coordinates.pop(0)
-                coordinates = [float(n.replace(',', '.')) for n in str_coordinates]
-                # noinspection PyTypeChecker
-                coordinates.append(class_num)
-                arr.append(coordinates)
-            return arr
-
-    def augment_annotation(self, image_file):
-        try:
-            image_path = os.path.join(constants.config.images_dir, image_file.name)
-            labels_path = os.path.join(constants.config.labels_dir, f'{Path(str(image_path)).stem}.txt')
-            image = cv2.imdecode(np.fromfile(image_path, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
-
-            img_ann = ImageLabel(
-                image_path=image_path,
-                image=image,
-                labels_path=labels_path,
-                labels=self.read_labels(labels_path)
-            )
-            try:
-                results = self.augment_inner(img_ann)
-                for annotation in results:
-                    cv2.imencode('.jpg', annotation.image)[1].tofile(annotation.image_path)
-                    with open(annotation.labels_path, 'w') as f:
-                        lines = [f'{l[4]} {round(l[0], 5)} {round(l[1], 5)} {round(l[2], 5)} {round(l[3], 5)}\n' for l in
-                                 annotation.labels]
-                        f.writelines(lines)
-                        f.close()
-
-                print(f'{datetime.now():{"%Y-%m-%d %H:%M:%S"}}: {self.total_files_processed + 1}/{self.total_images_to_process} : {image_file.name} has augmented')
-            except Exception as e:
-                print(e)
-            self.total_files_processed += 1
-        except Exception as e:
-            print(f'Error appeared in thread for {image_file.name}: {e}')
-
-    def augment_annotations(self, from_scratch=False):
-        self.total_files_processed = 0
-
-        if from_scratch:
-            shutil.rmtree(constants.config.processed_dir)
-
-        os.makedirs(constants.config.processed_images_dir, exist_ok=True)
-        os.makedirs(constants.config.processed_labels_dir, exist_ok=True)
-
-
-        processed_images = set(f.name for f in os.scandir(constants.config.processed_images_dir))
-        images = []
-        with os.scandir(constants.config.images_dir) as imd:
-            for image_file in imd:
-                if image_file.is_file() and image_file.name not in processed_images:
-                    images.append(image_file)
-        self.total_images_to_process = len(images)
-
-        with concurrent.futures.ThreadPoolExecutor() as executor:
-            executor.map(self.augment_annotation, images)
-
-
-if __name__ == '__main__':
-    augmentator = Augmentator()
-    while True:
-        augmentator.augment_annotations()
-        print('All processed, waiting for 5 minutes...')
-        time.sleep(300)

src/manual_run.py

@@ -5,9 +5,7 @@ from os import path
 
 import constants
 import train
-from augmentation import Augmentator
 
-# Augmentator().augment_annotations()
 # train.train_dataset()
 # train.resume_training('/azaion/dev/ai-training/runs/detect/train12/weights/last.pt')