131 lines
4.0 KiB
Python
131 lines
4.0 KiB
Python
# -*- coding: utf-8 -*-
|
||
"""
|
||
Created on Fri Aug 9 10:36:45 2024
|
||
|
||
@author: ym
|
||
"""
|
||
import os
|
||
import cv2
|
||
import numpy as np
|
||
import torch
|
||
import sys
|
||
from scipy.spatial.distance import cdist
|
||
sys.path.append(r"D:\DetectTracking")
|
||
from tracking.trackers.reid.reid_interface import ReIDInterface
|
||
from tracking.trackers.reid.config import config as ReIDConfig
|
||
ReIDEncoder = ReIDInterface(ReIDConfig)
|
||
|
||
|
||
def inference_image(images):
|
||
batch_patches = []
|
||
patches = []
|
||
for d, img1 in enumerate(images):
|
||
|
||
|
||
img = img1[:, :, ::-1].copy() # the model expects RGB inputs
|
||
patch = ReIDEncoder.transform(img)
|
||
|
||
# patch = patch.to(device=self.device).half()
|
||
if str(ReIDEncoder.device) != "cpu":
|
||
patch = patch.to(device=ReIDEncoder.device).half()
|
||
else:
|
||
patch = patch.to(device=ReIDEncoder.device)
|
||
|
||
patches.append(patch)
|
||
if (d + 1) % ReIDEncoder.batch_size == 0:
|
||
patches = torch.stack(patches, dim=0)
|
||
batch_patches.append(patches)
|
||
patches = []
|
||
|
||
if len(patches):
|
||
patches = torch.stack(patches, dim=0)
|
||
batch_patches.append(patches)
|
||
|
||
features = np.zeros((0, ReIDEncoder.embedding_size))
|
||
for patches in batch_patches:
|
||
pred = ReIDEncoder.model(patches)
|
||
pred[torch.isinf(pred)] = 1.0
|
||
feat = pred.cpu().data.numpy()
|
||
features = np.vstack((features, feat))
|
||
|
||
return features
|
||
|
||
|
||
def similarity_compare(root_dir):
|
||
'''
|
||
root_dir：包含 "subimgs"字段的文件夹中图像为 subimg子图
|
||
功能：相邻帧子图间相似度比较
|
||
'''
|
||
|
||
all_files = []
|
||
extensions = ['.png', '.jpg']
|
||
for dirpath, dirnames, filenames in os.walk(root_dir):
|
||
filepaths = []
|
||
for filename in filenames:
|
||
if os.path.basename(dirpath).find('subimgs') < 0:
|
||
continue
|
||
file, ext = os.path.splitext(filename)
|
||
if ext in extensions:
|
||
imgpath = os.path.join(dirpath, filename)
|
||
filepaths.append(imgpath)
|
||
nf = len(filepaths)
|
||
if nf==0:
|
||
continue
|
||
|
||
fnma = os.path.basename(filepaths[0]).split('.')[0]
|
||
imga = cv2.imread(filepaths[0])
|
||
ha, wa = imga.shape[:2]
|
||
|
||
for i in range(1, nf):
|
||
fnmb = os.path.basename(filepaths[i]).split('.')[0]
|
||
|
||
imgb = cv2.imread(filepaths[i])
|
||
hb, wb = imgb.shape[:2]
|
||
|
||
|
||
feats = inference_image(((imga, imgb)))
|
||
|
||
similar = 1 - np.maximum(0.0, cdist(feats, feats, metric='cosine'))
|
||
|
||
|
||
h, w = max((ha, hb)), max((wa, wb))
|
||
img = np.zeros(((h, 2*w, 3)), np.uint8)
|
||
img[0:ha, 0:wa], img[0:hb, w:(w+wb)] = imga, imgb
|
||
|
||
linewidth = max(round(((h+2*w))/2 * 0.001), 2)
|
||
cv2.putText(img,
|
||
text=f'{similar[0,1]:.2f}', # Text string to be drawn
|
||
org=(max(w-20, 10), h-10), # Bottom-left corner of the text string
|
||
fontFace=0, # Font type
|
||
fontScale=linewidth/3, # Font scale factor
|
||
color=(0, 0, 255), # Text color
|
||
thickness=linewidth, # Thickness of the lines used to draw a text
|
||
lineType=cv2.LINE_AA, # Line type
|
||
)
|
||
spath = os.path.join(dirpath, 's'+fnma+'-vs-'+fnmb+'.png')
|
||
cv2.imwrite(spath, img)
|
||
|
||
|
||
fnma = os.path.basename(filepaths[i]).split('.')[0]
|
||
imga = imgb.copy()
|
||
ha, wa = imga.shape[:2]
|
||
|
||
|
||
|
||
return
|
||
|
||
|
||
def main():
|
||
root_dir = r"D:\contrast\dataset\result\20240723-112242_6923790709882"
|
||
|
||
try:
|
||
similarity_compare(root_dir)
|
||
except Exception as e:
|
||
print(f'Error: {e}')
|
||
|
||
|
||
|
||
|
||
if __name__ == '__main__':
|
||
main()
|
||
|