From ea04f23ddca9aa0d4047a989a9b7606127a445be Mon Sep 17 00:00:00 2001
From: pos97em56 <10225101485@ecnu.stu.edu.cn>
Date: Tue, 2 Jul 2024 23:03:02 +0800
Subject: [PATCH] Delete 'advanced/license_plate_recog/image_operations.py'

---
 .../license_plate_recog/image_operations.py   | 160 ------------------
 1 file changed, 160 deletions(-)
 delete mode 100644 advanced/license_plate_recog/image_operations.py

diff --git a/advanced/license_plate_recog/image_operations.py b/advanced/license_plate_recog/image_operations.py
deleted file mode 100644
index 9844067..0000000
--- a/advanced/license_plate_recog/image_operations.py
+++ /dev/null
@@ -1,160 +0,0 @@
-import cv2
-import os
-import numpy as np
-import pytesseract
-
-
-def locate_license_plate(image_path):
-    if not os.path.isfile(image_path):
-        print(f"Error: File '{image_path}' does not exist.")
-        return None
-
-    """
-    根据输入的图像路径，定位蓝色车牌并返回裁剪出的车牌区域
-    """
-    # 读取图像
-    car = cv2.imread(image_path, 1)
-
-    if car is None:
-        print(f"Error: Unable to read image '{image_path}'")
-        return None
-
-    # 定义蓝色所对应的色彩空间范围
-    lower_blue = np.array([100, 110, 110])
-    upper_blue = np.array([130, 255, 255])
-
-    # 将图像转换到HSV颜色空间
-    hsv = cv2.cvtColor(car, cv2.COLOR_BGR2HSV)
-
-    # 获取蓝色区域的掩模
-    mask_blue = cv2.inRange(hsv, lower_blue, upper_blue)
-
-    # 将掩模转换为灰度图像
-    mask = mask_blue
-
-    # 形态学处理，开运算和闭运算
-    matrix = np.ones((20, 20), np.uint8)
-    mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, matrix)
-    mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, matrix)
-
-    # 二值化
-    ret, mask = cv2.threshold(mask, 0, 255, cv2.THRESH_BINARY)
-
-    # 查找轮廓
-    contours, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-
-    # 寻找最大轮廓并定位车牌
-    max_area = 0
-    best_contour = None
-    for contour in contours:
-        area = cv2.contourArea(contour)
-        if area > max_area:
-            max_area = area
-            best_contour = contour
-
-    # 获取定位车牌的外接矩形框
-    rect = cv2.minAreaRect(best_contour)
-    box = cv2.boxPoints(rect)
-    box = np.int32(box)
-
-    # 获取旋转矩阵
-    angle = rect[2]
-    flag = 0
-    print(angle)
-    if angle < -45:
-        angle += 90
-        flag = 1
-    if angle > 45:
-        angle -= 90
-        flag = 1
-        print(angle)
-    center = (rect[0][0], rect[0][1])
-    size = (int(rect[1][0]), int(rect[1][1]))
-    M = cv2.getRotationMatrix2D(center, angle, 1.0)
-
-    # 旋转图像
-    height, width = car.shape[:2]
-    rotated = cv2.warpAffine(car, M, (width, height))
-
-    # 获取旋转后矩形框的坐标
-    if flag == 0:
-        box = cv2.boxPoints(((center[0], center[1]), (size[0], size[1]), 0.0))
-        box = np.int32(box)
-
-    # 裁剪车牌区域
-    xs = [box[0, 0], box[1, 0], box[2, 0], box[3, 0]]
-    ys = [box[0, 1], box[1, 1], box[2, 1], box[3, 1]]
-    x1, x2 = min(xs), max(xs)
-    y1, y2 = min(ys), max(ys)
-    ROI_plate = rotated[y1:y2, x1:x2]
-
-    return ROI_plate
-
-
-def preprocess_image_for_ocr(image):
-    """
-    预处理图像以提高 OCR 识别率
-    """
-    # 去除左右上下各3个像素的边框
-    height, width = image.shape[:2]
-    image = image[3:height - 3, 3:width - 3]
-
-    # 调整图像大小到 165x40
-    image = cv2.resize(image, (165, 40), interpolation=cv2.INTER_AREA)
-
-    # 转换为灰度图像
-    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-
-    # 二值化
-    _, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
-
-    # 去噪
-    denoised = cv2.medianBlur(binary, 3)
-
-    return denoised
-
-
-def find_split_line(image):
-    """
-    在宽度15-30像素之间查找分割线
-    """
-    height, width = image.shape
-    column_sums = np.sum(image[3:height - 3, 15:30], axis=0)  # 忽略顶部和底部的像素
-
-    zero_columns = np.where(column_sums == 0)[0]  # 找到像素和为0的列
-    if len(zero_columns) == 0:
-        # 如果没有找到和为0的列，返回默认分割线为22
-        split_line = 22
-    else:
-        # 计算零列的中值作为分割线
-        split_line = int(np.median(zero_columns)) + 15  # 加上偏移量15
-    print(split_line)
-    return split_line
-
-
-def recognize_characters(image):
-    """
-    使用OCR技术识别车牌上的字符
-    """
-    preprocessed_image = preprocess_image_for_ocr(image)
-
-    # 找到分割线
-    split_line = find_split_line(preprocessed_image)
-
-    # 切割图像为两部分
-    part1 = preprocessed_image[:, :split_line]  # 宽度0-split_line部分
-    part2 = preprocessed_image[:, split_line:]  # 剩余部分
-
-    # 调用 Tesseract OCR 识别宽度0-24部分
-    custom_config_chi_sim = r'--oem 3 --psm 6 -l chi_sim'
-    result_chi_sim = pytesseract.image_to_string(part1, config=custom_config_chi_sim)
-
-    # 调用 Tesseract OCR 识别剩余部分
-    custom_config_default = r'--oem 3 --psm 6'
-    result_default = pytesseract.image_to_string(part2, config=custom_config_default)
-
-    # 合并两个部分的识别结果
-    recognized_characters = result_chi_sim.strip() + result_default.strip()
-
-    return recognized_characters
-