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def randomCropLetterboxPil(img): output_w, output_h = (1408, 768) jitter = 0.3 fill_color = 127 orig_w, orig_h = img.size img_np = np.array(img) channels = img_np.shape[2] if len(img_np.shape) > 2 else 1 dw = int(jitter * orig_w) dh = int(jitter * orig_h) new_ar = float(orig_w + random.randint(-dw, dw)) / (orig_h + random.randint(-dh, dh)) scale = random.random() * (2 - 0.25) + 0.25 if new_ar < 1: nh = int(scale * orig_h) nw = int(nh * new_ar) else: nw = int(scale * orig_w) nh = int(nw / new_ar) if output_w > nw: dx = random.randint(0, output_w - nw) else: dx = random.randint(output_w - nw, 0) if output_h > nh: dy = random.randint(0, output_h - nh) else: dy = random.randint(output_h - nh, 0) nxmin = max(0, -dx) nymin = max(0, -dy) nxmax = min(nw, -dx + output_w - 1) nymax = min(nh, -dy + output_h - 1) sx, sy = float(orig_w) / nw, float(orig_h) / nh orig_xmin = int(nxmin * sx) orig_ymin = int(nymin * sy) orig_xmax = int(nxmax * sx) orig_ymax = int(nymax * sy) orig_crop = img.crop((orig_xmin, orig_ymin, orig_xmax, orig_ymax)) orig_crop_resize = orig_crop.resize((nxmax - nxmin, nymax - nymin)) output_img = Image.new(img.mode, (output_w, output_h), color=(fill_color,) * channels) output_img.paste(orig_crop_resize, (0, 0)) return output_img def randomCropLetterboxCv(img): output_w, output_h = (1408, 768) jitter = 0.3 fill_color = 127 # orig_w, orig_h = img.size orig_w, orig_h = img.shape[:2] channels = img.shape[2] if len(img.shape) > 2 else 1 dw = int(jitter * orig_w) dh = int(jitter * orig_h) new_ar = float(orig_w + random.randint(-dw, dw)) / (orig_h + random.randint(-dh, dh)) scale = random.random() * (2 - 0.25) + 0.25 if new_ar < 1: nh = int(scale * orig_h) nw = int(nh * new_ar) else: nw = int(scale * orig_w) nh = int(nw / new_ar) if output_w > nw: dx = random.randint(0, output_w - nw) else: dx = random.randint(output_w - nw, 0) if output_h > nh: dy = random.randint(0, output_h - nh) else: dy = random.randint(output_h - nh, 0) nxmin = max(0, -dx) nymin = max(0, -dy) nxmax = min(nw, -dx + output_w - 1) nymax = min(nh, -dy + output_h - 1) sx, sy = float(orig_w) / nw, float(orig_h) / nh orig_xmin = int(nxmin * sx) orig_ymin = int(nymin * sy) orig_xmax = int(nxmax * sx) orig_ymax = int(nymax * sy) # orig_crop = img.crop((orig_xmin, orig_ymin, orig_xmax, orig_ymax)) orig_crop = img[orig_xmin:orig_xmax, orig_ymin:orig_ymax, :] orig_crop_resize = cv2.resize(orig_crop, (nxmax - nxmin, nymax - nymin), interpolation=cv2.INTER_CUBIC) output_img = np.ones((output_h, output_w, channels), dtype=np.uint8) * fill_color y_lim = int(min(output_img.shape[0], orig_crop_resize.shape[0])) x_lim = int(min(output_img.shape[1], orig_crop_resize.shape[1])) output_img[:y_lim, :x_lim, :] = orig_crop_resize[:y_lim, :x_lim, :] # orig_crop_resize = orig_crop.resize((nxmax - nxmin, nymax - nymin)) # output_img = Image.new(img.mode, (output_w, output_h), color=(fill_color,) * channels) # output_img.paste(orig_crop_resize, (0, 0)) return output_img
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