From 830c3a05713d46d00cb15acc74eb20490ef8d082 Mon Sep 17 00:00:00 2001
From: hasslesstech <hasslesstech@tutanota.com>
Date: Wed, 3 Dec 2025 12:20:19 +0200
Subject: [PATCH] aug batchable

---
 aug.py | 38 +++++++++++++++-----------------------
 1 file changed, 15 insertions(+), 23 deletions(-)

diff --git a/aug.py b/aug.py
index 3cc4571..444c0fd 100644
--- a/aug.py
+++ b/aug.py
@@ -1,11 +1,7 @@
 import albumentations as a
 import numpy as np
-#from ultralytics import YOLO
 from os import listdir as ls
 import cv2 as cv
-#from pybboxes import BoundingBox as bb
-
-#import torch
 
 def b_c(b, s = (1920, 1080)):
     return [
@@ -24,6 +20,8 @@ def sq(img, bbox):
 base_path = '../videos/total'
 tagt_path = '../videos/total-exp'
 
+p = 1
+
 t = a.Compose([
     a.BBoxSafeRandomCrop(),
     a.HorizontalFlip(p=0.5),
@@ -36,29 +34,23 @@ for i in list(fs)[:1]:
     img = cv.cvtColor(cv.imread(f"{base_path}/{i}.jpg"), cv.COLOR_BGR2RGB)
     box = [tuple(map(float, j.split()[1:])) for j in open(f"{base_path}/{i}.txt").read().split("\n") if j]
     
-    #for j in open(f"{base_path}/{i}.txt").read().split("\n"):
-    #    box.append(tuple(map(float, j.split()[1:])))
-
-    print(box)
-
     lbl = np.array(['ch'] * len(box))
     box = np.array(box)
 
-    #sq(img, box)
+    for k in range(10):
+        r = t(image = img, bboxes = box, class_labels = lbl)
 
-    r = t(image = img, bboxes = box, class_labels = lbl)
+        out_img = r['image']
+        out_box = r['bboxes']
+        out_lbl = r['class_labels']
 
-    out_img = r['image']
-    out_box = r['bboxes']
-    out_lbl = r['class_labels']
+        #print(f"open({tagt_path}/{p}-{i}-{k}.xml)")
+        #print("\n".join([" ".join(list(map(str, ['0'] + list(y)))) for y in out_box]) + "\n")
+        open(f"{tagt_path}/{p}-{i}-{k}.xml", 'w').write("\n".join([" ".join(list(map(str, ['0'] + list(y)))) for y in out_box]) + "\n")
+        cv.imwrite(f"{tagt_path}/{p}-{i}-{k}.jpg", cv.cvtColor(out_img, cv.COLOR_RGB2BGR))
 
-    sq(out_img, out_box)
+    #sq(out_img, out_box)
     
-    from matplotlib import pyplot as plt
-    plt.imshow(out_img)
-    plt.show()
-
-#y = YOLO("m.yaml")
-#y.info()
-
-#o = y.model(torch.randn(1, 3, 640, 640))
+    #from matplotlib import pyplot as plt
+    #plt.imshow(out_img)
+    #plt.show()