"""
Copyright (c) 2019-present NAVER Corp.
MIT License
"""
# -*- coding: utf-8 -*-
import numpy as np
from skimage import io
import cv2
def loadImage(img_file):
img = io.imread(img_file) # RGB order
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)):
# should be RGB order
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)):
# should be RGB order
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
# magnify image size
target_size = mag_ratio * max(height, width)
# set original image size
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)
# make canvas and paste image
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
