updates

utils/datasets.py

@@ -357,10 +357,15 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
                             f = '%s%sclassifier%s%g_%g_%s' % (p.parent.parent, os.sep, os.sep, x[0], j, p.name)
                             if not os.path.exists(Path(f).parent):
                                 os.makedirs(Path(f).parent)  # make new output folder
-                            box = xywh2xyxy(x[1:].reshape(-1, 4) * np.array([1, 1, 1.5, 1.5])).ravel()
+
-                            b = np.clip(box, 0, 1)  # clip boxes outside of image
+                            b = x[1:] * np.array([w, h, w, h])  # box
-                            ret_val = cv2.imwrite(f, img[int(b[1] * h):int(b[3] * h), int(b[0] * w):int(b[2] * w)])
+                            b[2:] = b[2:].max()  # rectangle to square
-                            assert ret_val, 'Failure extracting classifier boxes'
+                            b[2:] = b[2:] * 1.3 + 30  # pad
+                            b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(np.int)
+
+                            b[[0, 2]] = np.clip(b[[0, 2]], 0, w)  # clip boxes outside of image
+                            b[[1, 3]] = np.clip(b[[1, 3]], 0, h)
+                            assert cv2.imwrite(f, img[b[1]:b[3], b[0]:b[2]]), 'Failure extracting classifier boxes'
                 else:
                     ne += 1  # file empty
 

utils/utils.py

@@ -624,6 +624,29 @@ def select_best_evolve(path='evolve*.txt'):  # from utils.utils import *; select
         print(file, x[fitness(x).argmax()])
 
 
+def crop_images_random(path='../images/', scale=0.50):  # from utils.utils import *; crop_images_random()
+    # crops images into random squares up to scale fraction
+    # WARNING: overwrites images!
+    for file in tqdm(sorted(glob.glob('%s/*.*' % path))):
+        img = cv2.imread(file)  # BGR
+        if img is not None:
+            h, w = img.shape[:2]
+
+            # create random mask
+            a = 30  # minimum size (pixels)
+            mask_h = random.randint(a, int(max(a, h * scale)))  # mask height
+            mask_w = mask_h  # mask width
+
+            # box
+            xmin = max(0, random.randint(0, w) - mask_w // 2)
+            ymin = max(0, random.randint(0, h) - mask_h // 2)
+            xmax = min(w, xmin + mask_w)
+            ymax = min(h, ymin + mask_h)
+
+            # apply random color mask
+            cv2.imwrite(file, img[ymin:ymax, xmin:xmax])
+
+
 def coco_single_class_labels(path='../coco/labels/train2014/', label_class=43):
     # Makes single-class coco datasets. from utils.utils import *; coco_single_class_labels()
     if os.path.exists('new/'):