回传数据解析，兼容v5和v10

contrast/select_subimgs.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Mon Dec 23 13:58:13 2024
+
+writting for selectting std subimgs to Wuhuaqi
+
+@author: ym
+"""
+
+import os
+import time
+# import torch
+import pickle
+# import json
+import numpy as np
+from PIL import Image
+import matplotlib.pyplot as plt
+from scipy.spatial.distance import cdist
+from feat_extract.config import config as conf
+# from model import resnet18 as resnet18
+from feat_extract.inference import FeatsInterface #, inference_image
+
+
+IMG_FORMAT = ['.bmp', '.jpg', '.jpeg', '.png']
+
+
+
+def gen_features(imgpath):
+    
+    Encoder = FeatsInterface(conf) 
+    imgs, imgnames = [], []
+    for filename in os.listdir(imgpath):
+        file, ext = os.path.splitext(filename)
+        if ext not in IMG_FORMAT: continue
+        
+        fpath = os.path.join(imgpath, filename)
+        img = Image.open(fpath)
+        
+        imgs.append(img)
+        
+        filelist = file.split("_")
+        newname = "_".join([filelist[0],filelist[1], filelist[2], filelist[-3], filelist[-2], filelist[-1]])
+        # imgnames.append(newname)
+        imgnames.append(file)
+        
+    features = Encoder.inference(imgs)
+    features /= np.linalg.norm(features, axis=1)[:, None]
+    
+    return features, imgnames
+
+def top_p_percent_indices(matrix, p):
+    """
+    Finds the indices of the top p% largest elements in a 2D matrix.
+    Args:
+        matrix (np.ndarray): A 2D NumPy array.
+        p: int, 0-100
+    Returns:
+        List[Tuple[int, int]]: A list of indices (row, column) for the top 10% largest elements.
+    """
+    # Flatten the matrix
+    flat_matrix = matrix.flatten()
+    
+    # Calculate the threshold for the top 10%
+    num_elements = len(flat_matrix)
+    threshold_index = int(num_elements * 0.01*p)  # Top 10%
+    threshold_index = max(1, threshold_index)  # Ensure at least one element is considered
+    threshold_value = np.partition(flat_matrix, -threshold_index)[-threshold_index]
+    
+    # Create a mask for elements >= threshold
+    mask = matrix >= threshold_value
+    
+    # Get the indices of elements that satisfy the mask
+    indices = np.argwhere(mask)
+    
+    return list(map(tuple, indices))
+
+
+def main():
+    imgpath = r"\\192.168.1.28\share\数据\已完成数据\展厅数据\v1.0\比对数据\整理\zhantingBase\6923555210479"
+    
+    feats, imgnames = gen_features(imgpath)
+    n = len(feats)
+    
+    matrix = 1 - cdist(feats, feats, 'cosine')
+  
+    nmatrix = np.array([[matrix[i][j] for j in range(n) if i != j] for i in range(n)])
+    
+    top_p_large_index = top_p_percent_indices(nmatrix, 1)
+    top_p_small_index = top_p_percent_indices(-1*nmatrix, 1)
+
+    simi_mean = np.mean(nmatrix, axis=1)
+    max_simi = np.max(nmatrix)
+    max_index = np.where(nmatrix==max_simi)
+    min_simi = np.min(nmatrix)
+    min_index = np.where(nmatrix==min_simi)
+    
+    fig, ax = plt.subplots()
+    
+    simils = [matrix[i][j] for j in range(n) for i in range(n) if j>i]
+    ax.hist(simils, bins=60, range=(-0.2, 1), edgecolor='black')
+    ax.set_xlim([-0.2, 1])
+    ax.set_title("Similarity")
+    
+    
+    print("done!")
+    
+    
+    
+    
+    
+    
+
+    
+
+    
+if __name__ == '__main__':
+    main()