| """ |
| Copyright (c) 2019-present NAVER Corp. |
| MIT License |
| """ |
|
|
| |
| import numpy as np |
| from skimage import io |
| import cv2 |
|
|
| def loadImage(img_file): |
| img = io.imread(img_file) |
| if img.shape[0] == 2: img = img[0] |
| if len(img.shape) == 2 : img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) |
| if img.shape[2] == 4: img = img[:,:,:3] |
| img = np.array(img) |
|
|
| return img |
|
|
| def normalizeMeanVariance(in_img, mean=(0.485, 0.456, 0.406), variance=(0.229, 0.224, 0.225)): |
| |
| img = in_img.copy().astype(np.float32) |
|
|
| img -= np.array([mean[0] * 255.0, mean[1] * 255.0, mean[2] * 255.0], dtype=np.float32) |
| img /= np.array([variance[0] * 255.0, variance[1] * 255.0, variance[2] * 255.0], dtype=np.float32) |
| return img |
|
|
| def denormalizeMeanVariance(in_img, mean=(0.485, 0.456, 0.406), variance=(0.229, 0.224, 0.225)): |
| |
| img = in_img.copy() |
| img *= variance |
| img += mean |
| img *= 255.0 |
| img = np.clip(img, 0, 255).astype(np.uint8) |
| return img |
|
|
| def resize_aspect_ratio(img, square_size, interpolation, mag_ratio=1): |
| height, width, channel = img.shape |
|
|
| |
| target_size = mag_ratio * max(height, width) |
|
|
| |
| if target_size > square_size: |
| target_size = square_size |
| |
| ratio = target_size / max(height, width) |
|
|
| target_h, target_w = int(height * ratio), int(width * ratio) |
| proc = cv2.resize(img, (target_w, target_h), interpolation = interpolation) |
|
|
|
|
| |
| target_h32, target_w32 = target_h, target_w |
| if target_h % 32 != 0: |
| target_h32 = target_h + (32 - target_h % 32) |
| if target_w % 32 != 0: |
| target_w32 = target_w + (32 - target_w % 32) |
| resized = np.zeros((target_h32, target_w32, channel), dtype=np.float32) |
| resized[0:target_h, 0:target_w, :] = proc |
| target_h, target_w = target_h32, target_w32 |
|
|
| size_heatmap = (int(target_w/2), int(target_h/2)) |
|
|
| return resized, ratio, size_heatmap |
|
|
| def cvt2HeatmapImg(img): |
| img = (np.clip(img, 0, 1) * 255).astype(np.uint8) |
| img = cv2.applyColorMap(img, cv2.COLORMAP_JET) |
| return img |
|
|
