回传数据解析，兼容v5和v10

contrast/utils/databits.py

@@ -0,0 +1,127 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Apr  1 16:43:04 2025
+
+@author: wqg
+"""
+import os
+import pickle
+import numpy as np
+from scipy.spatial.distance import cdist
+
+
+def int8_to_ft16(arr_uint8, amin, amax):
+    arr_ft16 = (arr_uint8 / 255 * (amax-amin) + amin).astype(np.float16)
+    
+    return arr_ft16
+    
+def ft16_to_uint8(arr_ft16):
+    # pickpath = r"\\192.168.1.28\share\测试_202406\contrast\std_features_ft32vsft16\6902265587712_ft16.pickle"
+    
+    # with open(pickpath, 'rb') as f:  
+    #     edict = pickle.load(f)
+        
+    # arr_ft16 = edict['feats']
+    
+    amin = np.min(arr_ft16)
+    amax = np.max(arr_ft16)
+    arr_ft255 = (arr_ft16 - amin) * 255 / (amax-amin)
+    arr_uint8 = arr_ft255.astype(np.uint8)
+
+    arr_ft16_ = int8_to_ft16(arr_uint8, amin, amax)
+
+    arrDistNorm = np.linalg.norm(arr_ft16_ - arr_ft16) / arr_ft16_.size
+
+    return arr_uint8, arr_ft16_
+
+
+def data_precision_compare(stdfeat, evtfeat, evtMessage, similPath='', save=True):
+    evt, stdbcd, label = evtMessage
+    rltdata, rltdata_ft16, rltdata_ft16_ = [], [], []
+
+    matrix = 1 - cdist(stdfeat, evtfeat, 'cosine')
+    simi_mean = np.mean(matrix)
+    simi_max  = np.max(matrix)
+    stdfeatm = np.mean(stdfeat, axis=0, keepdims=True)
+    evtfeatm = np.mean(evtfeat, axis=0, keepdims=True)
+    simi_mfeat = 1- np.maximum(0.0, cdist(stdfeatm, evtfeatm, 'cosine'))
+    rltdata = [label, stdbcd, evt, simi_mean, simi_max, simi_mfeat[0,0]]
+    
+    
+    ##================================================================= float16
+    stdfeat_ft16 = stdfeat.astype(np.float16)
+    evtfeat_ft16 = evtfeat.astype(np.float16)
+    stdfeat_ft16 /= np.linalg.norm(stdfeat_ft16, axis=1)[:, None]
+    evtfeat_ft16 /= np.linalg.norm(evtfeat_ft16, axis=1)[:, None]
+    
+    
+    matrix_ft16 = 1 - cdist(stdfeat_ft16, evtfeat_ft16, 'cosine')
+    simi_mean_ft16 = np.mean(matrix_ft16)
+    simi_max_ft16  = np.max(matrix_ft16)
+    stdfeatm_ft16 = np.mean(stdfeat_ft16, axis=0, keepdims=True)
+    evtfeatm_ft16 = np.mean(evtfeat_ft16, axis=0, keepdims=True)
+    simi_mfeat_ft16 = 1- np.maximum(0.0, cdist(stdfeatm_ft16, evtfeatm_ft16, 'cosine'))
+    rltdata_ft16 = [label, stdbcd, evt, simi_mean_ft16, simi_max_ft16, simi_mfeat_ft16[0,0]]
+    
+    '''****************** uint8 is ok!!!!!! ******************'''
+    ##=================================================================== uint8
+    # stdfeat_uint8, stdfeat_ft16_ = ft16_to_uint8(stdfeat_ft16)
+    # evtfeat_uint8, evtfeat_ft16_ = ft16_to_uint8(evtfeat_ft16)
+    
+    stdfeat_uint8 = (stdfeat_ft16*128).astype(np.int8)
+    evtfeat_uint8 = (evtfeat_ft16*128).astype(np.int8)
+    stdfeat_ft16_ = stdfeat_uint8.astype(np.float16)/128
+    evtfeat_ft16_ = evtfeat_uint8.astype(np.float16)/128
+
+    absdiff = np.linalg.norm(stdfeat_ft16_ - stdfeat) / stdfeat.size
+    
+    matrix_ft16_ = 1 - cdist(stdfeat_ft16_, evtfeat_ft16_, 'cosine')
+    simi_mean_ft16_ = np.mean(matrix_ft16_)
+    simi_max_ft16_  = np.max(matrix_ft16_)
+    stdfeatm_ft16_ = np.mean(stdfeat_ft16_, axis=0, keepdims=True)
+    evtfeatm_ft16_ = np.mean(evtfeat_ft16_, axis=0, keepdims=True)
+    simi_mfeat_ft16_ = 1- np.maximum(0.0, cdist(stdfeatm_ft16_, evtfeatm_ft16_, 'cosine'))
+    rltdata_ft16_ = [label, stdbcd, evt, simi_mean_ft16_, simi_max_ft16_, simi_mfeat_ft16_[0,0]]
+    
+    if not save:
+        return
+    
+
+    ##========================================================= save as float32
+    rppath = os.path.join(similPath, f'{evt}_ft32.pickle')
+    with open(rppath, 'wb') as f:  
+        pickle.dump(rltdata, f)
+        
+    rtpath = os.path.join(similPath, f'{evt}_ft32.txt')
+    with open(rtpath, 'w', encoding='utf-8') as f:
+        for result in rltdata:
+            part = [f"{x:.3f}" if isinstance(x, float) else str(x) for x in result]
+            line = ', '.join(part)
+            f.write(line + '\n')
+
+
+    ##========================================================= save as float16        
+    rppath_ft16 = os.path.join(similPath, f'{evt}_ft16.pickle')
+    with open(rppath_ft16, 'wb') as f:  
+        pickle.dump(rltdata_ft16, f)
+    
+    rtpath_ft16 = os.path.join(similPath, f'{evt}_ft16.txt')
+    with open(rtpath_ft16, 'w', encoding='utf-8') as f:
+        for result in rltdata_ft16:
+            part = [f"{x:.3f}" if isinstance(x, float) else str(x) for x in result]
+            line = ', '.join(part)
+            f.write(line + '\n')
+    
+
+    ##=========================================================== save as uint8        
+    rppath_uint8 = os.path.join(similPath, f'{evt}_uint8.pickle')
+    with open(rppath_uint8, 'wb') as f:  
+        pickle.dump(rltdata_ft16_, f)
+    
+    rtpath_uint8 = os.path.join(similPath, f'{evt}_uint8.txt')
+    with open(rtpath_uint8, 'w', encoding='utf-8') as f:
+        for result in rltdata_ft16_:
+            part = [f"{x:.3f}" if isinstance(x, float) else str(x) for x in result]
+            line = ', '.join(part)
+            f.write(line + '\n')