replace random_affine() with random_perspective()

train.py

@@ -32,7 +32,7 @@ hyp = {'optimizer': 'SGD',  # ['adam', 'SGD', None] if none, default is SGD
        'hsv_s': 0.7,  # image HSV-Saturation augmentation (fraction)
        'hsv_v': 0.4,  # image HSV-Value augmentation (fraction)
        'degrees': 0.0,  # image rotation (+/- deg)
-       'translate': 0.0,  # image translation (+/- fraction)
+       'translate': 0.5,  # image translation (+/- fraction)
        'scale': 0.5,  # image scale (+/- gain)
        'shear': 0.0}  # image shear (+/- deg)
 

utils/datasets.py

@@ -485,9 +485,9 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
 
             # MixUp https://arxiv.org/pdf/1710.09412.pdf
             # if random.random() < 0.5:
-            #     img2, labels2 = load_mosaic(self, random.randint(0, len(self.labels) - 1))
+            #    img2, labels2 = load_mosaic(self, random.randint(0, len(self.labels) - 1))
             #     r = np.random.beta(0.3, 0.3)  # mixup ratio, alpha=beta=0.3
-            #     img = (img * r + img2 * (1 - r)).astype(np.uint8)
+            #    img = (img * r + img2 * (1 - r)).astype(np.uint8)
             #     labels = np.concatenate((labels, labels2), 0)
 
         else:
@@ -513,11 +513,11 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
         if self.augment:
             # Augment imagespace
             if not self.mosaic:
-                img, labels = random_affine(img, labels,
-                                            degrees=hyp['degrees'],
-                                            translate=hyp['translate'],
-                                            scale=hyp['scale'],
-                                            shear=hyp['shear'])
+                img, labels = random_perspective(img, labels,
+                                                 degrees=hyp['degrees'],
+                                                 translate=hyp['translate'],
+                                                 scale=hyp['scale'],
+                                                 shear=hyp['shear'])
 
             # Augment colorspace
             augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v'])
@@ -610,7 +610,7 @@ def load_mosaic(self, index):
 
     labels4 = []
     s = self.img_size
-    yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border]  # mosaic center x, y
+    yc, xc = s, s  # mosaic center x, y
     indices = [index] + [random.randint(0, len(self.labels) - 1) for _ in range(3)]  # 3 additional image indices
     for i, index in enumerate(indices):
         # Load image
@@ -656,12 +656,12 @@ def load_mosaic(self, index):
 
     # Augment
     # img4 = img4[s // 2: int(s * 1.5), s // 2:int(s * 1.5)]  # center crop (WARNING, requires box pruning)
-    img4, labels4 = random_affine(img4, labels4,
-                                  degrees=self.hyp['degrees'],
-                                  translate=self.hyp['translate'],
-                                  scale=self.hyp['scale'],
-                                  shear=self.hyp['shear'],
-                                  border=self.mosaic_border)  # border to remove
+    img4, labels4 = random_perspective(img4, labels4,
+                                       degrees=self.hyp['degrees'],
+                                       translate=self.hyp['translate'],
+                                       scale=self.hyp['scale'],
+                                       shear=self.hyp['shear'],
+                                       border=self.mosaic_border)  # border to remove
 
     return img4, labels4
 
@@ -716,36 +716,54 @@ def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scale
     return img, ratio, (dw, dh)
 
 
-def random_affine(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10, border=(0, 0)):
+def random_perspective(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0, border=(0, 0)):
     # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
-    # https://medium.com/uruvideo/dataset-augmentation-with-random-homographies-a8f4b44830d4
     # targets = [cls, xyxy]
 
     height = img.shape[0] + border[0] * 2  # shape(h,w,c)
     width = img.shape[1] + border[1] * 2
 
+    # Center
+    C = np.eye(3)
+    C[0, 2] = -img.shape[1] / 2  # x translation (pixels)
+    C[1, 2] = -img.shape[0] / 2  # y translation (pixels)
+
+    # Perspective
+    P = np.eye(3)
+    P[2, 0] = random.uniform(-perspective, perspective)  # x perspective (about y)
+    P[2, 1] = random.uniform(-perspective, perspective)  # y perspective (about x)
+
     # Rotation and Scale
     R = np.eye(3)
     a = random.uniform(-degrees, degrees)
     # a += random.choice([-180, -90, 0, 90])  # add 90deg rotations to small rotations
     s = random.uniform(1 - scale, 1 + scale)
     # s = 2 ** random.uniform(-scale, scale)
-    R[:2] = cv2.getRotationMatrix2D(angle=a, center=(img.shape[1] / 2, img.shape[0] / 2), scale=s)
-
-    # Translation
-    T = np.eye(3)
-    T[0, 2] = random.uniform(-translate, translate) * img.shape[1] + border[1]  # x translation (pixels)
-    T[1, 2] = random.uniform(-translate, translate) * img.shape[0] + border[0]  # y translation (pixels)
+    R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
 
     # Shear
     S = np.eye(3)
     S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180)  # x shear (deg)
     S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180)  # y shear (deg)
 
+    # Translation
+    T = np.eye(3)
+    T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width  # x translation (pixels)
+    T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height  # y translation (pixels)
+
     # Combined rotation matrix
-    M = S @ T @ R  # ORDER IS IMPORTANT HERE!!
+    M = T @ S @ R @ P @ C  # order of operations (right to left) is IMPORTANT
     if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any():  # image changed
-        img = cv2.warpAffine(img, M[:2], dsize=(width, height), flags=cv2.INTER_LINEAR, borderValue=(114, 114, 114))
+        if perspective:
+            img = cv2.warpPerspective(img, M, dsize=(width, height), borderValue=(114, 114, 114))
+        else:  # affine
+            img = cv2.warpAffine(img, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
+
+    # Visualize
+    # import matplotlib.pyplot as plt
+    # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
+    # ax[0].imshow(img[:, :, ::-1])  # base
+    # ax[1].imshow(img2[:, :, ::-1])  # warped
 
     # Transform label coordinates
     n = len(targets)
@@ -753,7 +771,11 @@ def random_affine(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10,
         # warp points
         xy = np.ones((n * 4, 3))
         xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2)  # x1y1, x2y2, x1y2, x2y1
-        xy = (xy @ M.T)[:, :2].reshape(n, 8)
+        xy = xy @ M.T  # transform
+        if perspective:
+            xy = (xy[:, :2] / xy[:, 2:3]).reshape(n, 8)  # rescale
+        else:  # affine
+            xy = xy[:, :2].reshape(n, 8)
 
         # create new boxes
         x = xy[:, [0, 2, 4, 6]]