YOLOv5 + Albumentations integration (#3882)

requirements.txt

@@ -27,4 +27,5 @@ pandas
 # extras --------------------------------------
 # Cython  # for pycocotools https://github.com/cocodataset/cocoapi/issues/172
 # pycocotools>=2.0  # COCO mAP
+# albumentations>=1.0.0
 thop  # FLOPs computation

utils/augmentations.py

 # YOLOv5 image augmentation functions
 
+import logging
 import random
 
 import cv2
 import math
 import numpy as np
 
-from utils.general import segment2box, resample_segments
+from utils.general import colorstr, segment2box, resample_segments, check_version
 from utils.metrics import bbox_ioa
 
 
+class Albumentations:
+    # YOLOv5 Albumentations class (optional, only used if package is installed)
+    def __init__(self):
+        self.transform = None
+        try:
+            import albumentations as A
+            check_version(A.__version__, '1.0.0')  # version requirement
+
+            self.transform = A.Compose([
+                A.Blur(p=0.1),
+                A.MedianBlur(p=0.1),
+                A.ToGray(p=0.01)],
+                bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels']))
+
+            logging.info(colorstr('albumentations: ') + ', '.join(f'{x}' for x in self.transform.transforms))
+        except ImportError:  # package not installed, skip
+            pass
+        except Exception as e:
+            logging.info(colorstr('albumentations: ') + f'{e}')
+
+    def __call__(self, im, labels, p=1.0):
+        if self.transform and random.random() < p:
+            new = self.transform(image=im, bboxes=labels[:, 1:], class_labels=labels[:, 0])  # transformed
+            im, labels = new['image'], np.array([[c, *b] for c, b in zip(new['class_labels'], new['bboxes'])])
+        return im, labels
+
+
 def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5):
     # HSV color-space augmentation
     if hgain or sgain or vgain:

utils/datasets.py

@@ -22,7 +22,7 @@ from PIL import Image, ExifTags
 from torch.utils.data import Dataset
 from tqdm import tqdm
 
-from utils.augmentations import augment_hsv, copy_paste, letterbox, mixup, random_perspective
+from utils.augmentations import Albumentations, augment_hsv, copy_paste, letterbox, mixup, random_perspective
 from utils.general import check_requirements, check_file, check_dataset, xywh2xyxy, xywhn2xyxy, xyxy2xywhn, \
     xyn2xy, segments2boxes, clean_str
 from utils.torch_utils import torch_distributed_zero_first
@@ -372,6 +372,7 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
         self.mosaic_border = [-img_size // 2, -img_size // 2]
         self.stride = stride
         self.path = path
+        self.albumentations = Albumentations() if augment else None
 
         try:
             f = []  # image files
@@ -540,8 +541,6 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
                 labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1])
 
             if self.augment:
-            # Augment imagespace
-            if not mosaic:
                 img, labels = random_perspective(img, labels,
                                                  degrees=hyp['degrees'],
                                                  translate=hyp['translate'],
@@ -549,32 +548,35 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
                                                  shear=hyp['shear'],
                                                  perspective=hyp['perspective'])
 
-            # Augment colorspace
-            augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v'])
-
-            # Apply cutouts
-            # if random.random() < 0.9:
-            #     labels = cutout(img, labels)
-
-        nL = len(labels)  # number of labels
-        if nL:
+        nl = len(labels)  # number of labels
+        if nl:
             labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0])  # xyxy to xywh normalized
 
         if self.augment:
-            # flip up-down
+            # Albumentations
+            img, labels = self.albumentations(img, labels)
+
+            # HSV color-space
+            augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v'])
+
+            # Flip up-down
             if random.random() < hyp['flipud']:
                 img = np.flipud(img)
-                if nL:
+                if nl:
                     labels[:, 2] = 1 - labels[:, 2]
 
-            # flip left-right
+            # Flip left-right
             if random.random() < hyp['fliplr']:
                 img = np.fliplr(img)
-                if nL:
+                if nl:
                     labels[:, 1] = 1 - labels[:, 1]
 
-        labels_out = torch.zeros((nL, 6))
-        if nL:
+            # Cutouts
+            # if random.random() < 0.9:
+            #     labels = cutout(img, labels)
+
+        labels_out = torch.zeros((nl, 6))
+        if nl:
             labels_out[:, 1:] = torch.from_numpy(labels)
 
         # Convert

utils/general.py

@@ -3,7 +3,6 @@
 import contextlib
 import glob
 import logging
-import math
 import os
 import platform
 import random
@@ -17,6 +16,7 @@ from pathlib import Path
 from subprocess import check_output
 
 import cv2
+import math
 import numpy as np
 import pandas as pd
 import pkg_resources as pkg
@@ -136,13 +136,16 @@ def check_git_status(err_msg=', for updates see https://github.com/ultralytics/y
         print(f'{e}{err_msg}')
 
 
-def check_python(minimum='3.6.2', required=True):
+def check_python(minimum='3.6.2'):
     # Check current python version vs. required python version
-    current = platform.python_version()
-    result = pkg.parse_version(current) >= pkg.parse_version(minimum)
-    if required:
-        assert result, f'Python {minimum} required by YOLOv5, but Python {current} is currently installed'
-    return result
+    check_version(platform.python_version(), minimum, name='Python ')
+
+
+def check_version(current='0.0.0', minimum='0.0.0', name='version ', pinned=False):
+    # Check version vs. required version
+    current, minimum = (pkg.parse_version(x) for x in (current, minimum))
+    result = (current == minimum) if pinned else (current >= minimum)
+    assert result, f'{name}{minimum} required by YOLOv5, but {name}{current} is currently installed'
 
 
 def check_requirements(requirements='requirements.txt', exclude=()):