def random_affine()

in container-dp/resources/FairMOT/jde.py [0:0]

• 46 lines of code
• 4 McCabe index (conditional complexity)

def random_affine(img, targets=None, degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-2, 2),
                  borderValue=(127.5, 127.5, 127.5)):
    # 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

    border = 0  # width of added border (optional)
    height = img.shape[0]
    width = img.shape[1]

    # Rotation and Scale
    R = np.eye(3)
    a = random.random() * (degrees[1] - degrees[0]) + degrees[0]
    # a += random.choice([-180, -90, 0, 90])  # 90deg rotations added to small rotations
    s = random.random() * (scale[1] - scale[0]) + scale[0]
    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.random() * 2 - 1) * translate[0] * img.shape[0] + border  # x translation (pixels)
    T[1, 2] = (random.random() * 2 - 1) * translate[1] * img.shape[1] + border  # y translation (pixels)

    # Shear
    S = np.eye(3)
    S[0, 1] = math.tan((random.random() * (shear[1] - shear[0]) + shear[0]) * math.pi / 180)  # x shear (deg)
    S[1, 0] = math.tan((random.random() * (shear[1] - shear[0]) + shear[0]) * math.pi / 180)  # y shear (deg)

    M = S @ T @ R  # Combined rotation matrix. ORDER IS IMPORTANT HERE!!
    imw = cv2.warpPerspective(img, M, dsize=(width, height), flags=cv2.INTER_LINEAR,
                              borderValue=borderValue)  # BGR order borderValue

    # Return warped points also
    if targets is not None:
        if len(targets) > 0:
            n = targets.shape[0]
            points = targets[:, 2:6].copy()
            area0 = (points[:, 2] - points[:, 0]) * (points[:, 3] - points[:, 1])

            # warp points
            xy = np.ones((n * 4, 3))
            xy[:, :2] = points[:, [0, 1, 2, 3, 0, 3, 2, 1]].reshape(n * 4, 2)  # x1y1, x2y2, x1y2, x2y1
            xy = (xy @ M.T)[:, :2].reshape(n, 8)

            # create new boxes
            x = xy[:, [0, 2, 4, 6]]
            y = xy[:, [1, 3, 5, 7]]
            xy = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T

            # apply angle-based reduction
            radians = a * math.pi / 180
            reduction = max(abs(math.sin(radians)), abs(math.cos(radians))) ** 0.5
            x = (xy[:, 2] + xy[:, 0]) / 2
            y = (xy[:, 3] + xy[:, 1]) / 2
            w = (xy[:, 2] - xy[:, 0]) * reduction
            h = (xy[:, 3] - xy[:, 1]) * reduction
            xy = np.concatenate((x - w / 2, y - h / 2, x + w / 2, y + h / 2)).reshape(4, n).T

            # reject warped points outside of image
            #np.clip(xy[:, 0], 0, width, out=xy[:, 0])
            #np.clip(xy[:, 2], 0, width, out=xy[:, 2])
            #np.clip(xy[:, 1], 0, height, out=xy[:, 1])
            #np.clip(xy[:, 3], 0, height, out=xy[:, 3])
            w = xy[:, 2] - xy[:, 0]
            h = xy[:, 3] - xy[:, 1]
            area = w * h
            ar = np.maximum(w / (h + 1e-16), h / (w + 1e-16))
            i = (w > 4) & (h > 4) & (area / (area0 + 1e-16) > 0.1) & (ar < 10)

            targets = targets[i]
            targets[:, 2:6] = xy[i]

        return imw, targets, M
    else:
        return imw