# OpenCV快速入门——图像预处理（必看）

### OpenCV预处理

• 对图像特征提取前的预处理
• 1.灰度化
• 2.滤波处理
• 3.轮廓检测
• 4.透视变换
• 5.二值化
• 6.形态学
• 7.图像绘制&添加文字

### 对图像特征提取前的预处理

#### 5.二值化

``````import numpy as np
import cv2

def order_points(pts):
# 一共4个坐标点
rect = np.zeros((4, 2), dtype="float32")

# 按顺序找到对应坐标0123分别是 左上，右上，右下，左下
# 计算左上，右下
s = pts.sum(axis=1)
rect[0] = pts[np.argmin(s)]
rect[2] = pts[np.argmax(s)]

# 计算右上和左下
diff = np.diff(pts, axis=1)
rect[1] = pts[np.argmin(diff)]
rect[3] = pts[np.argmax(diff)]
return rect

def four_point_transform(image, pts):
# 获取输入坐标点
rect = order_points(pts)
(tl, tr, br, bl) = rect

# 计算输入的w和h值
widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
maxWidth = max(int(widthA), int(widthB))

heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
maxHeight = max(int(heightA), int(heightB))

# 变换后对应坐标位置
dst = np.array([
[0, 0],
[maxWidth - 1, 0],
[maxWidth - 1, maxHeight - 1],
[0, maxHeight - 1]], dtype="float32")

# 计算变换矩阵
M = cv2.getPerspectiveTransform(rect, dst)
warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))

# 返回变换后结果
return warped

def resize(image, width=None, height=None, inter=cv2.INTER_AREA):
dim = None
(h, w) = image.shape[:2]
if width is None and height is None:
return image
if width is None:
r = height / float(h)
dim = (int(w * r), height)
else:
r = width / float(w)
dim = (width, int(h * r))
resized = cv2.resize(image, dim, interpolation=inter)
return resized

# 读取输入
#坐标也会相同变化
ratio = image.shape[0] / 500.0
orig = image.copy()

image = resize(orig, height=500)

# 预处理
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(gray, 75, 200)

# 展示预处理结果
print("STEP 1: 边缘检测")
cv2.imshow("Image", image)
cv2.imshow("Edged", edged)
cv2.waitKey(0)
cv2.destroyAllWindows()

# 轮廓检测
cnts = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[1]
cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:5]

# 遍历轮廓
for c in cnts:
# 计算轮廓近似
peri = cv2.arcLength(c, True)
# C表示输入的点集
# epsilon表示从原始轮廓到近似轮廓的最大距离，它是一个准确度参数
# True表示封闭的
approx = cv2.approxPolyDP(c, 0.02 * peri, True)

# 4个点的时候就拿出来
if len(approx) == 4:
screenCnt = approx
break

# 展示结果
print("STEP 2: 获取轮廓")
cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2)
cv2.imshow("Outline", image)
cv2.waitKey(0)
cv2.destroyAllWindows()

# 透视变换
warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio)

# 二值处理
warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
ref = cv2.threshold(warped, 100, 255, cv2.THRESH_BINARY)[1]
cv2.imwrite('scan.jpg', ref)
# 展示结果
print("STEP 3: 变换")
cv2.imshow("Original", resize(orig, height = 650))
cv2.imshow("Scanned", resize(ref, height = 650))
cv2.waitKey(0)
cv2.destroyAllWindows()
``````

#### 6.形态学

``````
# kernel ==> element 算子
element = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
element2 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
element3 = cv2.getStructuringElement(cv2.MORPH_CROSS, (5, 5))

# 腐蚀、膨胀
erosion = cv2.erode(img, kernel, iterations=1)
dilation = cv2.dialte(img, kernel)

# 通用形态学操作
kernel = np.ones((5, 5), np.uint8)
dst = cv2.morphologyEx(src, cv2.MORPH_operation, kernel, iteration)

opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
closing = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel, 2)

# 形态学梯度 = dilation - erosion，用于提取物体轮廓

# 顶帽 = src - open，用来分离比邻近点亮一些的斑块
tophat = cv2.morphologyEx(img, cv2.MORPH_TOPHAT, kernel)

# 黑帽 = src - close，用来分离比邻近点暗一些的斑块
blackhat= cv2.morphologyEx(img, cv2.MORPH_BLACKHAT, kernel)

``````

#### 7.图像绘制&添加文字

``````cv2.line(img, (0, 0), (511, 511), (255, 0, 0), 3)            # pt1, pt2, color, thickness
cv2.rectangle(img, (384, 0), (511, 511), (255, 0, 0), 3)     # pt1, pt2, color, thickness
cv2.circle(img, (447, 63), 63, (0, 0, 255), -1)              # center, radius, color, shift

pts = np.array([[10, 5], [20, 30], [70, 20], [50, 10]], np.int32).reshape((-1, 2))
cv2.polylines(img, [pts], True, (0, 255, 255))               # pts, is_closed, color

font = cv2.FONT_HERSHEY_SIMPLEX        # text, start_pt, font, fontscale, color, thickness, linetype
cv2.putText(img, 'hello', (10, 500), font, 4, (255, 255, 0), 2, cv2.LINE_AA)
``````