71 lines
2.1 KiB
Python
71 lines
2.1 KiB
Python
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"""
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Copyright (c) 2019-present NAVER Corp.
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MIT License
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"""
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# -*- coding: utf-8 -*-
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import numpy as np
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from skimage import io
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import cv2
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def loadImage(img_file):
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img = io.imread(img_file) # RGB order
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if img.shape[0] == 2: img = img[0]
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if len(img.shape) == 2 : img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
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if img.shape[2] == 4: img = img[:,:,:3]
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img = np.array(img)
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return img
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def normalizeMeanVariance(in_img, mean=(0.485, 0.456, 0.406), variance=(0.229, 0.224, 0.225)):
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# should be RGB order
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img = in_img.copy().astype(np.float32)
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img -= np.array([mean[0] * 255.0, mean[1] * 255.0, mean[2] * 255.0], dtype=np.float32)
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img /= np.array([variance[0] * 255.0, variance[1] * 255.0, variance[2] * 255.0], dtype=np.float32)
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return img
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def denormalizeMeanVariance(in_img, mean=(0.485, 0.456, 0.406), variance=(0.229, 0.224, 0.225)):
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# should be RGB order
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img = in_img.copy()
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img *= variance
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img += mean
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img *= 255.0
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img = np.clip(img, 0, 255).astype(np.uint8)
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return img
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def resize_aspect_ratio(img, square_size, interpolation, mag_ratio=1):
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height, width, channel = img.shape
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# magnify image size
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target_size = mag_ratio * max(height, width)
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# set original image size
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if target_size > square_size:
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target_size = square_size
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ratio = target_size / max(height, width)
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target_h, target_w = int(height * ratio), int(width * ratio)
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proc = cv2.resize(img, (target_w, target_h), interpolation = interpolation)
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# make canvas and paste image
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target_h32, target_w32 = target_h, target_w
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if target_h % 32 != 0:
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target_h32 = target_h + (32 - target_h % 32)
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if target_w % 32 != 0:
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target_w32 = target_w + (32 - target_w % 32)
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resized = np.zeros((target_h32, target_w32, channel), dtype=np.float32)
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resized[0:target_h, 0:target_w, :] = proc
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target_h, target_w = target_h32, target_w32
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size_heatmap = (int(target_w/2), int(target_h/2))
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return resized, ratio, size_heatmap
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def cvt2HeatmapImg(img):
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img = (np.clip(img, 0, 1) * 255).astype(np.uint8)
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img = cv2.applyColorMap(img, cv2.COLORMAP_JET)
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return img
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