OpenCV+TensorFlow 人工智能图像处理 (2)

# 1. 加载图片
# 2. 获取图片信息（宽度，高度）
# 3. 调用OpenCV的resize方法，进行图片的缩放
# 4. 检查最终的结果
import cv2

img = cv2.imread('images/image0.jpg', 1)
imgInfo = img.shape
print (imgInfo)
height = imgInfo[0]
width = imgInfo[1]
mode = imgInfo[2] # 颜色的储存方式，由3种颜色(bgr)组成
# 放大， 缩小， 等比缩放 非等比缩放
dstHeight = int(height*0.5) # 目标高度
dstWidth = int(width*0.5)
# 四种resize: 最近邻域插值 双线性插值 像素关系重采样 立方插值
dst = cv2.resize(img, (dstWidth, dstHeight))
cv2.imshow("image", dst)
cv2.waitKey(0)

(547, 730, 3)

-1

# 最近邻域插值
# 1. 获取图片信息
# 2. 创建一个空白模板，与预期目标大小一样
# 3. 计算对应的像素
import cv2
import numpy as np
img = cv2.imread('images/image0.jpg', 1)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
dstHeight = int(height/2)
dstWidth = int(width/2)
dstImage = np.zeros((dstHeight, dstWidth, 3), np.uint8) # 0-255
for i in range(0, dstHeight): # 行
    for j in range(0, dstWidth): # 列
        iNew = int(i*(height*1.0/dstHeight))
        jNew = int(j*(width*1.0/dstWidth))
        dstImage[i, j] = img[iNew, jNew]

cv2.imshow('dstImage', dstImage)
cv2.waitKey(0)

1114089

# 剪切(100, 100) -> (200, 300)
import cv2
img = cv2.imread('images/image0.jpg', 1)
dst = img[100:200, 100:300]
# 将蓝红通道设为0
dst[:, :, [0, 2]] = 0
cv2.imshow("image", dst)
cv2.waitKey(0)

-1

import cv2
import numpy as np
img = cv2.imread('images/image0.jpg', 1)
cv2.imshow('src', img)
imgInfo = img.shape
height, width = imgInfo[0], imgInfo[1]
# 进行矩阵的移位
matShift = np.float32([[1, 0, 100], [0, 1, 200]]) # 2*3
dst = cv2.warpAffine(img, matShift, (height, width)) # 矩阵映射，原图， 移位矩阵， 图片的高度和宽度
cv2.imshow('dst', dst)
cv2.waitKey(0)

1048603

# 算法原理实现图片移位
import cv2
import numpy as np
img = cv2.imread('images/image0.jpg', 1)
cv2.imshow('src', img)
imgInfo = img.shape
dst = np.zeros(img.shape, np.uint8)
height, width = imgInfo[0], imgInfo[1]
for i in range(0, height):
    for j in range(0, width-100):
        dst[i, j+100] = img[i, j]
cv2.imshow('dst', dst)
cv2.waitKey(0)

-1

# 1. 创建一个足够大的画板
# 2. 将一副图像从前向后，从后向前绘制
# 3. 绘制中心分割线
import cv2
import numpy as np
img = cv2.imread('images/image0.jpg', 1)
imgInfo = img.shape
height, width, deep = imgInfo[0], imgInfo[1], imgInfo[2]
newImgInfo = (height*2, width, deep)
dst = np.zeros(newImgInfo, np.uint8)
for i in range(height):
    for j in range(width):
        dst[i, j] = img[i, j]
        # 绘制镜像部分， x轴不变， y = 2*h - y - 1
        dst[height*2-i-1, j] = img[i, j]
for i in range(width):
    dst[height, i] = (0, 0, 255)
cv2.imshow('image', dst)
cv2.waitKey(0)

-1

# 缩放1倍
import cv2
import numpy as np
img = cv2.imread('images/image0.jpg', 1)
imgInfo = img.shape
height, width = imgInfo[0], imgInfo[1]
matScale = np.float32([[0.5, 0, 0], [0, 0.5, 0]])
dst = cv2.warpAffine(img, matScale, (int(width/2), int(height/2)))
cv2.imshow('dst', dst)
cv2.waitKey(0)

-1

import cv2
import numpy as np
img = cv2.imread('images/image0.jpg', 1)
imgInfo = img.shape
height, width = imgInfo[0], imgInfo[1]
# 3点确定一个平面，分别是左上角， 左下角， 右上角
matSrc = np.float32([[0, 0], [0, height], [width, 0]])
matDst = np.float32([[50, 50], [300, height-200], [width-300, 100]])
# 组合
matAffine = cv2.getAffineTransform(matSrc, matDst) # 获取矩阵组合, 1: 描述原矩阵的三点， 2： 目标矩阵的三点
dst = cv2.warpAffine(img, matAffine, (width, height))
cv2.imshow('image', dst)
cv2.waitKey(0)

import cv2
import numpy as np
img = cv2.imread('images/image0.jpg', 1)
imgInfo = img.shape
height, width = imgInfo[0], imgInfo[1]
# 定义旋转矩阵
matRotate = cv2.getRotationMatrix2D((height*0.5, width*0.5), 45, 1) # 得到旋转矩阵， 1 旋转中心店， 2 旋转角度， 3 缩放系数
dst = cv2.warpAffine(img, matRotate, (width, height))
cv2.imshow('image', dst)
cv2.waitKey(0)

-1