python如何将图片摆正？

Python提供了多种库和方法来实现这一功能。以下是几种常用的图片摆正方法：

1. 基于OpenCV的图像校正

OpenCV是最常用的图像处理库，提供了多种图像校正方法：

1.1 基于边缘检测和霍夫变换的校正

import cv2
import numpy as np
import math

def deskew_image(image_path, output_path=None):
    # 读取图像
    image = cv2.imread(image_path)

    # 转换为灰度图
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # 边缘检测
    edges = cv2.Canny(gray, 50, 150, apertureSize=3)

    # 霍夫线变换
    lines = cv2.HoughLines(edges, 1, np.pi/180, 200)

    if lines is not None:
        # 计算倾斜角度
        angles = []
        for line in lines:
            rho, theta = line[0]
            # 只考虑接近水平或垂直的线
            if (theta < np.pi/4 or theta > 3*np.pi/4):
                angles.append(theta)

        if angles:
            # 计算平均角度
            median_angle = np.median(angles)

            # 将角度转换为度数
            angle = median_angle * 180 / np.pi

            # 调整角度（确保角度在-45到45度之间）
            if angle > 45:
                angle = 90 - angle
            elif angle < -45:
                angle = -90 - angle

            # 获取图像中心
            (h, w) = image.shape[:2]
            center = (w // 2, h // 2)

            # 旋转图像
            M = cv2.getRotationMatrix2D(center, angle, 1.0)
            rotated = cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)

            # 保存结果
            if output_path:
                cv2.imwrite(output_path, rotated)

            return rotated

    # 如果没有检测到线条，返回原图
    if output_path:
        cv2.imwrite(output_path, image)

    return image

# 使用示例
deskew_image("tilted_image.jpg", "corrected_image.jpg")

1.2 基于文本行的校正（适用于文档图像）

import cv2
import numpy as np

def correct_skew(image_path, output_path=None, delta=1, limit=5):
    # 读取图像
    image = cv2.imread(image_path)

    # 转换为灰度图
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # 二值化处理
    thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

    # 计算各个角度的投影，找到文本行最整齐的角度
    scores = []
    angles = np.arange(-limit, limit + delta, delta)

    for angle in angles:
        # 旋转图像
        (h, w) = thresh.shape[:2]
        center = (w // 2, h // 2)
        M = cv2.getRotationMatrix2D(center, angle, 1.0)
        rotated = cv2.warpAffine(thresh, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)

        # 计算水平投影
        hist = cv2.reduce(rotated, 1, cv2.REDUCE_SUM, dtype=cv2.CV_32S)

        # 计算投影的方差（方差越大，说明文本行越整齐）
        score = np.var(hist)
        scores.append(score)

    # 找到得分最高的角度
    best_angle = angles[np.argmax(scores)]

    # 旋转原始图像
    (h, w) = image.shape[:2]
    center = (w // 2, h // 2)
    M = cv2.getRotationMatrix2D(center, best_angle, 1.0)
    rotated...

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