166 lines
4.5 KiB
Python
166 lines
4.5 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Wed Nov 20 11:17:29 2024
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@author: ym
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"""
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import os
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import cv2
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import pickle
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import numpy as np
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from scipy.spatial.distance import cdist
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import matplotlib.pyplot as plt
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def save_imgpairs(barcode, imgpaths, matrix, savepath, thresh=(0.4, 0.6), ctype="intra"):
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if ctype=="intra":
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rows, cols = np.triu_indices(matrix.shape[0], k=1) # k=1 表示不包括对角线
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mask = matrix[rows, cols] < thresh[1]
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indices = list(zip(rows[mask], cols[mask]))
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else:
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rows, cols = np.where(matrix > thresh[0])
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indices = list(zip(rows, cols))
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if len(indices):
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savepath = os.path.join(savepath, barcode)
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if not os.path.exists(savepath):
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os.makedirs (savepath)
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for idx1, idx2 in indices:
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if len(imgpaths) == 1:
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img1 = cv2.imread(imgpaths[0][idx1])
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img2 = cv2.imread(imgpaths[0][idx2])
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elif len(imgpaths) == 2:
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img1 = cv2.imread(imgpaths[0][idx1])
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img2 = cv2.imread(imgpaths[1][idx2])
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simi = matrix[idx1, idx2]
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H1, W1 = img1.shape[:2]
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H2, W2 = img2.shape[:2]
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H, W = max((H1, H2)), max((W1, W2))
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img = np.ones((H, 2*W, 3), dtype=np.uint8) *np.array([255, 128, 128])
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img[0:H1, 0:W1, :] = img1
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img[0:H2, (2*W-W2):, :] = img2
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text = f"sim: {simi:.2f}"
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org = (10, H-10)
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cv2.putText(img, text, org, fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.75,
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color=(0, 0, 255), thickness=2, lineType=cv2.LINE_AA)
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imgpath = os.path.join(savepath, f"{simi:.2f}_{barcode}_{idx1}_{idx2}.png")
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cv2.imwrite(imgpath, img)
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def feat_analysis(featpath):
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'''
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标准特征集中样本类内、类间相似度分布
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'''
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savepath = r"D:\exhibition\result\stdfeat"
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InterThresh = (0.4, 0.6)
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featDict, features= [], []
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for filename in os.listdir(featpath):
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file, ext = os.path.splitext(filename)
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if ext != ".pickle": continue
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filepath = os.path.join(featpath, filename)
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with open(filepath, 'rb') as f:
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bpDict = pickle.load(f)
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feat = bpDict["feats_ft32"]
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featDict.append(bpDict)
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features.append(feat)
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N = len(features)
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simMatrix = []
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intra_simi = np.empty(0)
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low_simi_index = {}
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for i, feats in enumerate(features):
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matrix = 1 - cdist(feats, feats, 'cosine')
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simMatrix.append(matrix)
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'''提取相似矩阵上三角元素'''
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rows, cols = np.triu_indices(matrix.shape[0], k=1) # k=1 表示不包括对角线
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upper_tri= matrix[rows, cols]
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intra_simi = np.concatenate((intra_simi, upper_tri))
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'''保存相似度小于阈值的图像对'''
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barcode = featDict[i]["barcode"]
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imgpaths = featDict[i]["imgpaths"]
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# save_imgpairs(barcode, [imgpaths], matrix, savepath, InterThresh, "intra")
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print(f"{barcode} have done!")
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Matrix = np.zeros((N, N))
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inter_bcds = []
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inter_simi = np.empty(0)
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for i, feati in enumerate(features):
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bcdi = featDict[i]["barcode"]
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imgpathi = featDict[i]["imgpaths"]
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for j, featj in enumerate(features):
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bcdj = featDict[j]["barcode"]
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imgpathj = featDict[j]["imgpaths"]
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matrix = 1 - cdist(feati, featj, 'cosine')
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inter_bcds.append((i, j, bcdi, bcdj))
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Matrix[i, j] = np.mean(matrix)
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if j>i:
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bcd_ij = bcdi+'_'+bcdj
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# save_imgpairs(bcd_ij, [imgpathi, imgpathj], matrix, savepath, InterThresh, "inter")
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inter_simi = np.concatenate((inter_simi, matrix.ravel()))
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print(f"{bcd_ij} have done!")
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fig, axs = plt.subplots(2, 1)
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axs[0].hist(intra_simi, bins=100, color='blue', edgecolor='black', alpha=0.7)
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axs[0].set_xlim(0, 1)
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axs[0].set_xlabel('Performance')
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axs[0].set_title("inter similarity")
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axs[1].hist(inter_simi, bins=100, color='green', edgecolor='black', alpha=0.7)
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axs[1].set_xlim(0, 1)
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axs[1].set_xlabel('Performance')
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axs[1].set_title("inter similarity")
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print("Done！")
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def main():
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stdpath = r"D:\exhibition\dataset\feats"
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feat_analysis(stdpath)
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if __name__ == '__main__':
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main()
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