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# -*- coding: utf-8 -*-
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import cv2
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import numpy as np
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from numpy.linalg import norm
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SZ = 20 # 训练图片长宽
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MAX_WIDTH = 1000 # 原始图片最大宽度
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Min_Area = 2000 # 车牌区域允许最大面积
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PROVINCE_START = 1000
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# 来自opencv的sample，用于svm训练
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def deskew(img):
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m = cv2.moments(img)
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if abs(m['mu02']) < 1e-2:
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return img.copy()
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skew = m['mu11'] / m['mu02']
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M = np.float32([[1, skew, -0.5 * SZ * skew], [0, 1, 0]])
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img = cv2.warpAffine(img, M, (SZ, SZ), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)
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return img
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# 来自opencv的sample，用于svm训练
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def preprocess_hog(digits):
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samples = []
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for img in digits:
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gx = cv2.Sobel(img, cv2.CV_32F, 1, 0)
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gy = cv2.Sobel(img, cv2.CV_32F, 0, 1)
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mag, ang = cv2.cartToPolar(gx, gy)
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bin_n = 16
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bin = np.int32(bin_n * ang / (2 * np.pi))
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bin_cells = bin[:10, :10], bin[10:, :10], bin[:10, 10:], bin[10:, 10:]
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mag_cells = mag[:10, :10], mag[10:, :10], mag[:10, 10:], mag[10:, 10:]
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hists = [np.bincount(b.ravel(), m.ravel(), bin_n) for b, m in zip(bin_cells, mag_cells)]
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hist = np.hstack(hists)
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# transform to Hellinger kernel
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eps = 1e-7
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hist /= hist.sum() + eps
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hist = np.sqrt(hist)
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hist /= norm(hist) + eps
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samples.append(hist)
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return np.float32(samples)
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provinces = [
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"zh_cuan", "川",
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"zh_e", "鄂",
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"zh_gan", "赣",
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"zh_gan1", "甘",
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"zh_gui", "贵",
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"zh_gui1", "桂",
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"zh_hei", "黑",
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"zh_hu", "沪",
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"zh_ji", "冀",
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"zh_jin", "津",
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"zh_jing", "京",
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"zh_jl", "吉",
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"zh_liao", "辽",
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"zh_lu", "鲁",
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"zh_meng", "蒙",
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"zh_min", "闽",
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"zh_ning", "宁",
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"zh_qing", "青",
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"zh_qiong", "琼",
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"zh_shan", "陕",
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"zh_su", "苏",
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"zh_sx", "晋",
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"zh_wan", "皖",
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"zh_xiang", "湘",
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"zh_xin", "新",
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"zh_yu", "豫",
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"zh_yu1", "渝",
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"zh_yue", "粤",
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"zh_yun", "云",
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"zh_zang", "藏",
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"zh_zhe", "浙"
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]
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