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