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875天前#!/usr/bin/env python |
# encoding: utf-8 |
''' |
python实现光照特效 光线增强 可调整光位置和强度 |
''' |
import cv2, math |
import numpy as np |
class picture: |
def __init__(self): |
self.path = 'assets/picture.jpeg' |
def hello(self): |
return self |
def run(self): |
# 读取原始图像 |
img = cv2.imread(self.path) |
# 获取图像行和列 |
rows, cols = img.shape[:2] |
# 设置中心点 |
centerX = rows / 2 |
centerY = cols / 2 |
print(centerX, centerY) |
radius = min(centerX, centerY) |
print(radius) |
# 设置光照强度 |
strength = 30 |
# 新建目标图像 |
dst = np.zeros((rows, cols, 3), dtype="uint8") |
# 图像光照特效 |
for i in range(rows): |
for j in range(cols): |
# 计算当前点到光照中心的距离(平面坐标系中两点之间的距离) |
distance = math.pow((centerY - j), 2) + math.pow((centerX - i), 2) |
# 获取原始图像 |
B = img[i, j][0] |
G = img[i, j][1] |
R = img[i, j][2] |
if (distance < radius * radius): |
# 按照距离大小计算增强的光照值 |
result = (int)(strength * (1.0 - math.sqrt(distance) / radius)) |
B = img[i, j][0] + result |
G = img[i, j][1] + result |
R = img[i, j][2] + result |
# 判断边界 防止越界 |
B = min(255, max(0, B)) |
G = min(255, max(0, G)) |
R = min(255, max(0, R)) |
dst[i, j] = np.uint8((B, G, R)) |
else: |
dst[i, j] = np.uint8((B, G, R)) |
# 显示图像 |
cv2.imshow('src', img) |
cv2.imshow('dst', dst) |
cv2.waitKey() |
cv2.destroyAllWindows() |
if __name__ == '__main__': |
picture().hello().run() |
