接上篇文章， 我做了透视矫正的功能，且实现裁剪，但是效果不是很理想，欢迎小伙伴来评论
import cv2
import numpy as np
def process_image(image, is_original=False):
# 1. 复制图层并进行高斯模糊
blurred = cv2.GaussianBlur(image, (201, 201), 0).astype(float)
# 2. 实现“划分”模式
epsilon = 1e-7
divided = image / (blurred + epsilon)
# 将结果缩放到0-255范围并转换为8位无符号整数
divided = np.clip(divided * 255, 0, 255).astype(np.uint8)
merged = divided.astype(float) # 转换为浮点数以避免操作中的整数截断
# 3. 实现正片叠底模式
multiply = (divided * merged) / 255
return np.clip(multiply, 0, 255).astype(np.uint8)
def scan_effect(image_path):
# 读取原始图像
original = cv2.imread(image_path)
gray = cv2.cvtColor(original, cv2.COLOR_BGR2GRAY)
# 边缘检测
edged = cv2.Canny(gray, 50, 150)
# 膨胀操作，增强轮廓
dilated = cv2.dilate(edged, None, iterations=2)
# 找到轮廓
contours, _ = cv2.findContours(dilated, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
contours = sorted(contours, key=cv2.contourArea, reverse=True)[:10]
# 遍历轮廓，找到大概是文档的四边形
screen_contour = None
for contour in contours:
peri = cv2.arcLength(contour, True)
approx = cv2.approxPolyDP(contour, 0.01 * peri, True)
if len(approx) == 4:
screen_contour = approx
break
if screen_contour is not None:
# 在原始图像上绘制轮廓
# cv2.drawContours(original, [screen_contour], -1, (0, 255, 0), 2)
# 透视变换
def order_points(pts):
if len(pts.shape) == 3:
pts = pts.reshape(4, 2)
rect = np.zeros((4, 2), dtype="float32")
center = np.mean(pts, axis=0)
for point in pts:
if point[0] < center[0] and point[1] < center[1]:
rect[0] = point # 左上
elif point[0] > center[0] and point[1] < center[1]:
rect[1] = point # 右上
elif point[0] > center[0] and point[1] > center[1]:
rect[2] = point # 右下
else:
rect[3] = point # 左下
return rect
def four_point_transform(image, pts):
rect = order_points(pts)
(tl, tr, br, bl) = rect
widthA = np.linalg.norm(br - bl)
widthB = np.linalg.norm(tr - tl)
maxWidth = max(int(widthA), int(widthB))
heightA = np.linalg.norm(tr - br)
heightB = np.linalg.norm(tl - bl)
maxHeight = max(int(heightA), int(heightB))
dst = np.array([[0, 0], [maxWidth - 1, 0], [maxWidth - 1, maxHeight - 1], [0, maxHeight - 1]], dtype="float32")
M = cv2.getPerspectiveTransform(rect, dst)
return cv2.warpPerspective(image, M, (maxWidth, maxHeight))
warped = four_point_transform(original, screen_contour.reshape(4, 2))
multiply = process_image(warped)
else:
multiply = process_image(original, is_original=True)
# 显示和保存最终结果
cv2.imshow("Result", multiply)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.imwrite(r'C:\Users\40650\Desktop\20241009171353-1.jpg', multiply)
scan_effect(r'C:\Users\40650\Desktop\20241009171353.jpg')