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Runtime error
| import numpy as np | |
| from .warp_mls import WarpMLS | |
| def tia_distort(src, segment=4): | |
| img_h, img_w = src.shape[:2] | |
| cut = img_w // segment | |
| thresh = cut // 3 | |
| src_pts = list() | |
| dst_pts = list() | |
| src_pts.append([0, 0]) | |
| src_pts.append([img_w, 0]) | |
| src_pts.append([img_w, img_h]) | |
| src_pts.append([0, img_h]) | |
| dst_pts.append([np.random.randint(thresh), np.random.randint(thresh)]) | |
| dst_pts.append([img_w - np.random.randint(thresh), np.random.randint(thresh)]) | |
| dst_pts.append( | |
| [img_w - np.random.randint(thresh), img_h - np.random.randint(thresh)] | |
| ) | |
| dst_pts.append([np.random.randint(thresh), img_h - np.random.randint(thresh)]) | |
| half_thresh = thresh * 0.5 | |
| for cut_idx in np.arange(1, segment, 1): | |
| src_pts.append([cut * cut_idx, 0]) | |
| src_pts.append([cut * cut_idx, img_h]) | |
| dst_pts.append( | |
| [ | |
| cut * cut_idx + np.random.randint(thresh) - half_thresh, | |
| np.random.randint(thresh) - half_thresh, | |
| ] | |
| ) | |
| dst_pts.append( | |
| [ | |
| cut * cut_idx + np.random.randint(thresh) - half_thresh, | |
| img_h + np.random.randint(thresh) - half_thresh, | |
| ] | |
| ) | |
| trans = WarpMLS(src, src_pts, dst_pts, img_w, img_h) | |
| dst = trans.generate() | |
| return dst | |
| def tia_stretch(src, segment=4): | |
| img_h, img_w = src.shape[:2] | |
| cut = img_w // segment | |
| thresh = cut * 4 // 5 | |
| src_pts = list() | |
| dst_pts = list() | |
| src_pts.append([0, 0]) | |
| src_pts.append([img_w, 0]) | |
| src_pts.append([img_w, img_h]) | |
| src_pts.append([0, img_h]) | |
| dst_pts.append([0, 0]) | |
| dst_pts.append([img_w, 0]) | |
| dst_pts.append([img_w, img_h]) | |
| dst_pts.append([0, img_h]) | |
| half_thresh = thresh * 0.5 | |
| for cut_idx in np.arange(1, segment, 1): | |
| move = np.random.randint(thresh) - half_thresh | |
| src_pts.append([cut * cut_idx, 0]) | |
| src_pts.append([cut * cut_idx, img_h]) | |
| dst_pts.append([cut * cut_idx + move, 0]) | |
| dst_pts.append([cut * cut_idx + move, img_h]) | |
| trans = WarpMLS(src, src_pts, dst_pts, img_w, img_h) | |
| dst = trans.generate() | |
| return dst | |
| def tia_perspective(src): | |
| img_h, img_w = src.shape[:2] | |
| thresh = img_h // 2 | |
| src_pts = list() | |
| dst_pts = list() | |
| src_pts.append([0, 0]) | |
| src_pts.append([img_w, 0]) | |
| src_pts.append([img_w, img_h]) | |
| src_pts.append([0, img_h]) | |
| dst_pts.append([0, np.random.randint(thresh)]) | |
| dst_pts.append([img_w, np.random.randint(thresh)]) | |
| dst_pts.append([img_w, img_h - np.random.randint(thresh)]) | |
| dst_pts.append([0, img_h - np.random.randint(thresh)]) | |
| trans = WarpMLS(src, src_pts, dst_pts, img_w, img_h) | |
| dst = trans.generate() | |
| return dst | |