8c96a80ee2b2ec6e934480dbe01396dbe52b783f,src/SamplePreprocessor.py,,preprocess,#,7

Before Change

        // map image into target image
        M = np.float32([[fx, 0, tx], [0, fy, ty]])
        target = np.ones(imgSize[::-1]) * np.random.uniform(0, 255)
        img = cv2.warpAffine(img, M, dsize=imgSize, dst=target, borderMode=cv2.BORDER_TRANSPARENT)

    // no data augmentation
    else:

After Change

        h, w = img.shape
        f = min(wt / w, ht / h)
        fx = f * np.random.uniform(0.75, 1.25)
        fy = f * np.random.uniform(0.75, 1.25)

        // random position around center
        txc = (wt - w * fx) / 2
        tyc = (ht - h * fy) / 2
        freedom_x = max((wt - fx * w) / 2, 0) + wt / 10
        freedom_y = max((ht - fy * h) / 2, 0) + ht / 10
        tx = txc + np.random.uniform(-freedom_x, freedom_x)
        ty = tyc + np.random.uniform(-freedom_y, freedom_y)

        // map image into target image

Instances