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区分度分布
| 5 : 0.4680251736111111 | |
| 10 : 0.7441905381944445 | |
| 15 : 0.8501150173611111 | |
| 20 : 0.8981987847222223 | |
| 25 : 0.9249197048611111 | |
| 30 : 0.9417599826388889 | |
| 35 : 0.9525086805555556 | |
| 40 : 0.9603667534722222 | |
| 45 : 0.9662131076388889 | |
| 50 : 0.9709288194444444 | |
| 55 : 0.9748697916666667 | |
| 60 : 0.9779774305555555 | |
| 65 : 0.9807183159722223 | |
| 70 : 0.9829730902777778 | |
| 75 : 0.9847005208333334 | |
| 80 : 0.9863433159722222 | |
| 85 : 0.9875368923611111 | |
| 90 : 0.9887651909722223 | |
| 95 : 0.9897873263888889 |
区分度和相似度结合
相似度 > 50
相似度 > 50, 区分度 > 30
相似度 > 50, 区分度 > 20
代码示例
| # utf-8 | |
| # python3.8 | |
| import cv2 as cv | |
| import numpy as np | |
| def get_unit_similarity(a_4, b_4, k): | |
| # a_4 = sorted(a_4) | |
| # b_4 = sorted(b_4) | |
| result = 0 | |
| for i in range(4): | |
| result = result + (abs(int(a_4[i]) - int(b_4[i]))) ** k | |
| result = result ** (1/k) | |
| return int(result) | |
| def input_flag(flag, x, y, similarity): | |
| for i in range(4): | |
| if flag[x][y][i] == 0: | |
| flag[x][y][i] = similarity | |
| break | |
| return flag | |
| def get_channels_similarity(channel, k, rank): | |
| row, col = channel.shape | |
| flag = np.zeros((row - 1, col - 1, 4)) | |
| result = np.zeros((row - 1, col - 1)) | |
| for i in range(row - 2): | |
| for j in range(col - 2): | |
| array = [channel[i][j], channel[i][j + 1], channel[i + 1][j + 1], channel[i + 1][j]] | |
| r_array = [channel[i][j + 1], channel[i + 1][j + 1], channel[i][j + 2], channel[i + 1][j + 2]] | |
| d_array = [channel[i + 1][j + 1], channel[i + 1][j], channel[i + 2][j + 1], channel[i + 2][j]] | |
| r_similarity = get_unit_similarity(array, r_array, k) | |
| d_similarity = get_unit_similarity(array, d_array, k) | |
| flag = input_flag(flag, i, j, r_similarity) | |
| flag = input_flag(flag, i, j, d_similarity) | |
| flag = input_flag(flag, i+1, j, r_similarity) | |
| flag = input_flag(flag, i, j+1, d_similarity) | |
| # result[i][j] = (r_similarity + d_similarity) / 2 | |
| for i in range(row-2): | |
| for j in range(col-2): | |
| flag4 = sorted(flag[i][j]) | |
| result[i][j] = flag4[4-rank] | |
| return result | |
| def get_qufendu(array): | |
| return int(max(array)) - int(min(array)) | |
| def get_channels_qufendu(channel): | |
| row, col = channel.shape | |
| result = np.zeros((row - 1, col - 1)) | |
| for i in range(row-1): | |
| for j in range(col-1): | |
| array = [channel[i][j], channel[i][j + 1], channel[i + 1][j + 1], channel[i + 1][j]] | |
| qufendu = get_qufendu(array) | |
| result[i][j] = qufendu | |
| return result | |
| def mark(channel, similarity, qufendu): | |
| row, col = similarity.shape | |
| for i in range(row): | |
| for j in range(col): | |
| if similarity[i][j] > 50 and qufendu[i][j] > 20: | |
| channel[i][j] = 0 | |
| else: | |
| channel[i][j] = 255 | |
| return channel | |
| def mark_similarity(img, k, rank): | |
| p = cv.imread(img, 1) | |
| b, g, r = cv.split(p) | |
| b_similarity = get_channels_similarity(b, k, rank) | |
| b_qufendu = get_channels_qufendu(b) | |
| b = mark(b, b_similarity, b_qufendu) | |
| g_similarity = get_channels_similarity(g, k, rank) | |
| g_qufendu = get_channels_qufendu(g) | |
| g = mark(g, g_similarity, g_qufendu) | |
| r_similarity = get_channels_similarity(r, k, rank) | |
| r_qufendu = get_channels_qufendu(r) | |
| r = mark(r, r_similarity, r_qufendu) | |
| p = cv.merge((b, g, r)) | |
| cv.imwrite(img[0:-4] + '_mark.png', p) | |
| if __name__ == '__main__': | |
| img_addr = '8068.jpg' | |
| mark_similarity(img_addr, 1, 1) |
正文完
