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王庆刚
2024-11-04 18:06:52 +08:00
parent dfb2272a15
commit 5ecc1285d4
41 changed files with 2552 additions and 440 deletions
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contrast/one2n_contrast.py
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@ -16,60 +16,13 @@ import shutil
import numpy as np
import matplotlib.pyplot as plt
import cv2
from pathlib import Path
import sys
sys.path.append(r"D:\DetectTracking")
from tracking.utils.plotting import Annotator, colors
from tracking.utils.read_data import extract_data, read_deletedBarcode_file, read_tracking_output
from tracking.utils.read_data import extract_data, read_deletedBarcode_file, read_tracking_output, read_returnGoods_file
from tracking.utils.plotting import draw_tracking_boxes
def showHist(err, correct):
err = np.array(err)
correct = np.array(correct)
fig, axs = plt.subplots(2, 1)
axs[0].hist(err, bins=50, edgecolor='black')
axs[0].set_xlim([0, 1])
axs[0].set_title('err')
axs[1].hist(correct, bins=50, edgecolor='black')
axs[1].set_xlim([0, 1])
axs[1].set_title('correct')
# plt.show()
return plt
def show_recall_prec(recall, prec, ths):
# x = np.linspace(start=-0, stop=1, num=11, endpoint=True).tolist()
fig = plt.figure(figsize=(10, 6))
plt.plot(ths, recall, color='red', label='recall')
plt.plot(ths, prec, color='blue', label='PrecisePos')
plt.legend()
plt.xlabel(f'threshold')
# plt.ylabel('Similarity')
plt.grid(True, linestyle='--', alpha=0.5)
# plt.savefig('accuracy_recall_grid.png')
# plt.show()
# plt.close()
return plt
def compute_recall_precision(err_similarity, correct_similarity):
ths = np.linspace(0, 1, 51)
recall, prec = [], []
for th in ths:
TP = len([num for num in correct_similarity if num >= th])
FP = len([num for num in err_similarity if num >= th])
if (TP+FP) == 0:
prec.append(1)
recall.append(0)
else:
prec.append(TP / (TP + FP))
recall.append(TP / (len(err_similarity) + len(correct_similarity)))
return recall, prec, ths
from contrast.utils.tools import showHist, show_recall_prec, compute_recall_precision
# =============================================================================
@ -129,7 +82,7 @@ def read_tracking_imgs(imgspath):
return imgs_0, imgs_1
# =============================================================================
# def draw_tracking_boxes(imgs, tracks):
# '''tracks: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
@ -287,27 +240,9 @@ def save_tracking_imgpairs(pair, basepath, savepath):
cv2.imwrite(imgpath, img)
# def performance_evaluate(all_list, isshow=False):
# corrpairs, correct_barcode_list, correct_similarity, errpairs, err_barcode_list, err_similarity = [], [], [], [], [], []
# for s_list in all_list:
# seqdir = s_list['SeqDir'].strip()
# delete = s_list['Deleted'].strip()
# barcodes = [s.strip() for s in s_list['barcode']]
# similarity = [float(s.strip()) for s in s_list['similarity']]
# if delete in barcodes[:1]:
# corrpairs.append((seqdir, delete))
# correct_barcode_list.append(delete)
# correct_similarity.append(similarity[0])
# else:
# errpairs.append((seqdir, delete, barcodes[0]))
# err_barcode_list.append(delete)
# err_similarity.append(similarity[0])
def performance_evaluate(all_list, isshow=False):
corrpairs, correct_barcode_list, correct_similarity, errpairs, err_barcode_list, err_similarity = [], [], [], [], [], []
def one2n_old(all_list):
corrpairs, errpairs, correct_similarity, err_similarity = [], [], [], []
for s_list in all_list:
seqdir = s_list['SeqDir'].strip()
delete = s_list['Deleted'].strip()
@ -332,70 +267,136 @@ def performance_evaluate(all_list, isshow=False):
matched_barcode = barcodes[index]
if matched_barcode == delete:
corrpairs.append((seqdir, delete))
correct_barcode_list.append(delete)
correct_similarity.append(max(similarity))
else:
errpairs.append((seqdir, delete, matched_barcode))
err_barcode_list.append(delete)
err_similarity.append(max(similarity))
'''3. 计算比对性能 '''
if isshow:
recall, prec, ths = compute_recall_precision(err_similarity, correct_similarity)
show_recall_prec(recall, prec, ths)
showHist(err_similarity, correct_similarity)
return errpairs, corrpairs, err_similarity, correct_similarity
return corrpairs, errpairs, correct_similarity, err_similarity
def contrast_analysis(del_barcode_file, basepath, savepath, saveimgs=False):
'''
del_barcode_file: 测试数据文件，利用该文件进行算法性能分析
def one2n_new(all_list):
corrpairs, correct_similarity, errpairs, err_similarity = [], [], [], []
for s_list in all_list:
seqdir = s_list['SeqDir'].strip()
delete = s_list['Deleted'].strip()
barcodes = [s.strip() for s in s_list['barcode']]
events = [s.strip() for s in s_list['event']]
types = [s.strip() for s in s_list['type']]
## =================== 读入相似度值
similarity_comp, similarity_front = [], []
for simil in s_list['similarity']:
ss = [float(s.strip()) for s in simil.split(',')]
similarity_comp.append(ss[0])
if len(ss)==3:
similarity_front.append(ss[2])
if len(similarity_front):
similarity = [s for s in similarity_front]
else:
similarity = [s for s in similarity_comp]
index = similarity.index(max(similarity))
matched_barcode = barcodes[index]
if matched_barcode == delete:
corrpairs.append((seqdir, events[index]))
correct_similarity.append(max(similarity))
else:
idx = [i for i, name in enumerate(events) if name.split('_')[-1] == delete]
idxmax, simimax = -1, -1
# idxmax, simimax = k, similarity[k] for k in idx if similarity[k] > simimax
for k in idx:
if similarity[k] > simimax:
idxmax = k
simimax = similarity[k]
errpairs.append((seqdir, events[idxmax], events[index]))
err_similarity.append(max(similarity))
return errpairs, corrpairs, err_similarity, correct_similarity
# def contrast_analysis(del_barcode_file, basepath, savepath, saveimgs=False):
def get_relative_paths(del_barcode_file, basepath, savepath, saveimgs=False):
'''
del_barcode_file:
deletedBarcode.txt 格式的 1:n 数据结果文件
returnGoods.txt格式数据文件不需要调用该函数，one2n_old() 函数返回的 errpairs
中元素为三元元组（取出，放入, 错误匹配）
'''
relative_paths = []
'''1. 读取 deletedBarcode 文件 '''
all_list = read_deletedBarcode_file(del_barcode_file)
'''2. 算法性能评估，并输出 (取出，删除, 错误匹配) 对 '''
errpairs, corrpairs, _, _ = performance_evaluate(all_list)
'''3. 获取 (取出，删除, 错误匹配) 对应路径，保存相应轨迹图像'''
relative_paths = []
errpairs, corrpairs, _, _ = one2n_old(all_list)
'''3. 构造事件组合(取出，放入并删除, 错误匹配) 对应路径 '''
for errpair in errpairs:
GetoutPath, InputPath, ErrorPath = get_contrast_paths(errpair, basepath)
relative_paths.append((GetoutPath, InputPath, ErrorPath))
'''3. 获取 (取出，放入并删除, 错误匹配) 对应路径，保存相应轨迹图像'''
if saveimgs:
save_tracking_imgpairs(errpair, basepath, savepath)
return relative_paths
def contrast_loop(fpath):
def one2n_test():
fpath = r'\\192.168.1.28\share\测试_202406\deletedBarcode\other'
fpath = r'\\192.168.1.28\share\测试_202406\1030\images'
savepath = r'\\192.168.1.28\share\测试_202406\deletedBarcode\illustration'
# savepath = r'D:\contrast\dataset\1_to_n\illustration'
if not os.path.exists(savepath):
os.mkdir(savepath)
if os.path.isfile(fpath):
fpath, filename = os.path.split(fpath)
if os.path.isdir(fpath):
filepaths = [os.path.join(fpath, f) for f in os.listdir(fpath)
if f.find('.txt')>0
and (f.find('deletedBarcode')>=0 or f.find('returnGoods')>=0)]
elif os.path.isfile(fpath):
filepaths = [fpath]
else:
return
FileFormat = {}
BarLists, blists = {}, []
for filename in os.listdir(fpath):
file = os.path.splitext(filename)[0][15:]
filepath = os.path.join(fpath, filename)
blist = read_deletedBarcode_file(filepath)
for pth in filepaths:
file = str(Path(pth).stem)
if file.find('deletedBarcode')>=0:
FileFormat[file] = 'deletedBarcode'
blist = read_deletedBarcode_file(pth)
elif file.find('returnGoods')>=0:
FileFormat[file] = 'returnGoods'
blist = read_returnGoods_file(pth)
else:
return
BarLists.update({file: blist})
blists.extend(blist)
BarLists.update({file: blist})
BarLists.update({"Total": blists})
for file, blist in BarLists.items():
errpairs, corrpairs, err_similarity, correct_similarity = performance_evaluate(blist)
for file, blist in BarLists.items():
if FileFormat[file] == 'deletedBarcode':
_, _, err_similarity, correct_similarity = one2n_old(blist)
elif FileFormat[file] == 'returnGoods':
_, _, err_similarity, correct_similarity = one2n_new(blist)
else:
_, _, err_similarity, correct_similarity = one2n_old(blist)
recall, prec, ths = compute_recall_precision(err_similarity, correct_similarity)
@ -411,25 +412,33 @@ def contrast_loop(fpath):
# plt.close()
def main():
fpath = r'\\192.168.1.28\share\测试_202406\deletedBarcode\other'
contrast_loop(fpath)
def main1():
def test_getreltpath():
'''
适用于：deletedBarcode.txt，不适用于：returnGoods.txt
'''
del_barcode_file = r'\\192.168.1.28\share\测试_202406\709\deletedBarcode.txt'
basepath = r'\\192.168.1.28\share\测试_202406\709'
savepath = r'D:\contrast\dataset\result'
# del_barcode_file = r'\\192.168.1.28\share\测试_202406\1030\images\returnGoods.txt'
# basepath = r'\\192.168.1.28\share\测试_202406\1030\images'
savepath = r'D:\contrast\dataset\result'
saveimgs = True
try:
relative_path = contrast_analysis(del_barcode_file, basepath, savepath)
relative_path = get_relative_paths(del_barcode_file, basepath, savepath, saveimgs)
except Exception as e:
print(f'Error Type: {e}')
if __name__ == '__main__':
main()
# main1()
one2n_test()
# test_getreltpath()