import cv2
import numpy as np

# 定義旋轉(zhuǎn)中心和角度
center = (width/2, height/2)  # 通常以圖像中心為旋轉(zhuǎn)點(diǎn)
angle = 45  # 旋轉(zhuǎn)角度
scale = 1.0  # 縮放比例

# 獲取旋轉(zhuǎn)矩陣
rotation_matrix = cv2.getRotationMatrix2D(center, angle, scale)

# 讀取圖像
image = cv2.imread('input.jpg')

# 獲取圖像尺寸
height, width = image.shape[:2]

# 應(yīng)用旋轉(zhuǎn)
rotated_image = cv2.warpAffine(image, rotation_matrix, (width, height))

# 顯示結(jié)果
cv2.imshow('Rotated Image', rotated_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

# 順時(shí)針旋轉(zhuǎn)90度
rotated_90 = cv2.rotate(image, cv2.ROTATE_90_CLOCKWISE)

# 逆時(shí)針旋轉(zhuǎn)90度
rotated_90_counter = cv2.rotate(image, cv2.ROTATE_90_COUNTERCLOCKWISE)

# 旋轉(zhuǎn)180度
rotated_180 = cv2.rotate(image, cv2.ROTATE_180)

def rotate_image(image, angle):
    # 獲取圖像尺寸
    (h, w) = image.shape[:2]
    
    # 計(jì)算旋轉(zhuǎn)中心
    center = (w // 2, h // 2)
    
    # 獲取旋轉(zhuǎn)矩陣
    M = cv2.getRotationMatrix2D(center, angle, 1.0)
    
    # 計(jì)算旋轉(zhuǎn)后圖像的邊界
    cos = np.abs(M[0, 0])
    sin = np.abs(M[0, 1])
    new_w = int((h * sin) + (w * cos))
    new_h = int((h * cos) + (w * sin))
    
    # 調(diào)整旋轉(zhuǎn)矩陣以考慮平移
    M[0, 2] += (new_w / 2) - center[0]
    M[1, 2] += (new_h / 2) - center[1]
    
    # 執(zhí)行旋轉(zhuǎn)
    rotated = cv2.warpAffine(image, M, (new_w, new_h), 
                            borderMode=cv2.BORDER_CONSTANT, 
                            borderValue=(255, 255, 255))
    
    return rotated

# 不同邊界填充方式
rotated_replicate = cv2.warpAffine(image, M, (w, h), 
                                 borderMode=cv2.BORDER_REPLICATE)
rotated_reflect = cv2.warpAffine(image, M, (w, h), 
                               borderMode=cv2.BORDER_REFLECT)
rotated_wrap = cv2.warpAffine(image, M, (w, h), 
                            borderMode=cv2.BORDER_WRAP)

def rotate_by_two_points(image, pt1, pt2, desired_angle=0):
    # 計(jì)算兩點(diǎn)之間的角度
    dx = pt2[0] - pt1[0]
    dy = pt2[1] - pt1[1]
    current_angle = np.degrees(np.arctan2(dy, dx))
    
    # 計(jì)算需要旋轉(zhuǎn)的角度
    rotation_angle = desired_angle - current_angle
    
    # 計(jì)算旋轉(zhuǎn)中心（兩點(diǎn)中點(diǎn)）
    center = ((pt1[0] + pt2[0]) / 2, (pt1[1] + pt2[1]) / 2)
    
    # 獲取旋轉(zhuǎn)矩陣
    M = cv2.getRotationMatrix2D(center, rotation_angle, 1.0)
    
    # 執(zhí)行旋轉(zhuǎn)
    rotated = cv2.warpAffine(image, M, (image.shape[1], image.shape[0]))
    
    return rotated

def correct_document_skew(image):
    # 轉(zhuǎn)換為灰度圖
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    
    # 邊緣檢測(cè)
    edges = cv2.Canny(gray, 50, 150, apertureSize=3)
    
    # 霍夫線變換檢測(cè)直線
    lines = cv2.HoughLinesP(edges, 1, np.pi/180, 100, 
                           minLineLength=100, maxLineGap=10)
    
    # 計(jì)算平均角度
    angles = []
    for line in lines:
        x1, y1, x2, y2 = line[0]
        angle = np.degrees(np.arctan2(y2-y1, x2-x1))
        angles.append(angle)
    
    median_angle = np.median(angles)
    
    # 旋轉(zhuǎn)圖像校正
    (h, w) = image.shape[:2]
    center = (w // 2, h // 2)
    M = cv2.getRotationMatrix2D(center, median_angle, 1.0)
    corrected = cv2.warpAffine(image, M, (w, h), 
                              flags=cv2.INTER_CUBIC,
                              borderMode=cv2.BORDER_REPLICATE)
    
    return corrected

def augment_data(image):
    # 隨機(jī)旋轉(zhuǎn)角度 (-15到15度之間)
    angle = np.random.uniform(-15, 15)
    
    # 隨機(jī)縮放 (0.8到1.2之間)
    scale = np.random.uniform(0.8, 1.2)
    
    # 獲取旋轉(zhuǎn)矩陣
    h, w = image.shape[:2]
    center = (w/2, h/2)
    M = cv2.getRotationMatrix2D(center, angle, scale)
    
    # 應(yīng)用變換
    augmented = cv2.warpAffine(image, M, (w, h), 
                               flags=cv2.INTER_LINEAR,
                               borderMode=cv2.BORDER_REFLECT)
    
    return augmented