130 lines
2.9 KiB
Python
Executable File
130 lines
2.9 KiB
Python
Executable File
#! /usr/bin/env python3
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# -*- coding: utf-8 -*-
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import cv2, codecs, os, argparse
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import numpy as np
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from typing import Sequence
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def crop_image(img):
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height = img.shape[0]
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width = img.shape[1]
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v_sum = np.sum(img, axis=0)
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h_sum = np.sum(img, axis=1)
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left = 0
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right = width - 1
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top = 0
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bottom = height - 1
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# 从左往右扫描，遇到非零像素点就以此为字体的左边界
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for i in range(width):
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if v_sum[i] > 0:
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left = i
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break
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# 从右往左扫描
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for i in range(width - 1, -1, -1):
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if v_sum[i] > 0:
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right = i
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break
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# 从上往下扫描
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for i in range(height):
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if h_sum[i] > 0:
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top = i
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break
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# 从下往上扫描
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for i in range(height - 1, -1, -1):
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if h_sum[i] > 0:
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bottom = i
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break
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return img[top: bottom + 1, left: right + 1]
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def image_to_sample(img):
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arr = np.asarray(img)
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graysum = [0 for i in range(36)]
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total = 0
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for i in range(arr.shape[0]):
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for j in range(arr.shape[1]):
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if arr[i, j] > 0:
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graysum[(i // 5) * 6 + j // 5] += 1
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total += 1
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for i in range(len(graysum)):
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graysum[i] = graysum[i] / total
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return np.mat(graysum)
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def normalize_image_size(img, width, height):
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img = cv2.resize(img, (width, height))
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return img
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# 线性分类器类
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class Classifier:
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def __init__(self, w, w0: float, id0, id1):
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self.__w = w
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self.__w0 = w0
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self.id0 = id0
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self.id1 = id1
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def do(self, x):
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assert len(self.__w) == len(x)
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return self.id0 if float(x * self.__w.T + self.__w0) > 0 else self.id1
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def load_train(filename):
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fp = open(filename, "r")
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arr = []
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for line in fp:
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elems = line.split(':')
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char_pair = elems[0].split(',')
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w0 = float(elems[1])
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w_star = np.mat([[float(num) for num in elems[2].split(',')]])
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arr.append(Classifier(w_star, w0, char_pair[0], char_pair[1]))
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return arr
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def get_label_dict(filename):
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f = codecs.open(filename, 'r', encoding='utf-8')
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i = 0
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dic = {}
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for char in f:
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dic[i] = str(char).rstrip('\n')
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i = i + 1
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return dic
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def recognise(classfier_list , img):
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ret, img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY_INV)
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img = crop_image(img)
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img = normalize_image_size(img, 30, 30)
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sample = image_to_sample(img)
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ret, img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY);
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scoreboard = {}
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for item in classifiers:
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cls = item.do(sample)
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if cls not in scoreboard:
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scoreboard[cls] = 0
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scoreboard[cls] += 1
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max_score = 0
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max_id = ""
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for k, v in scoreboard.items():
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if v > max_score:
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max_score = v
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max_id = k
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return max_id, max_score
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("-t", required=True)
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parser.add_argument("-l", required=True)
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parser.add_argument("image_dir")
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args = parser.parse_args()
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classifiers = load_train(args.t)
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label = get_label_dict(args.l)
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path = args.image_dir
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for file in sorted(os.listdir(path)):
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file = os.path.join(path, file)
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img = cv2.imread(file, cv2.IMREAD_GRAYSCALE)
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id, scord = recognise(classifiers, img)
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print("%s --> '%s'" % (file, label[int(id)]))
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