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index.py
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index.py
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import os
import os
import cv2
import json
run_py = os.path.abspath(__file__)
dirname = os.path.dirname(run_py) #! model_dir
import shutil
import glob
from tqdm import tqdm
import numpy as np
from collections import defaultdict
from wbf import *
print(dirname)
def make_test_json(input_dir):
data_dir = input_dir
image_file_dir = os.path.join(data_dir, 'rgb')
annotations_info = {'images': [], 'annotations': [], 'categories': []}
categories_map = {"car": 1, "truck": 2, "bus":3, "van":4, "freight_car": 5}
for key in categories_map:
categoriy_info = {"id":categories_map[key], "name":key}
annotations_info['categories'].append(categoriy_info)
file_names = sorted([image_file_name.split('.')[0]
for image_file_name in os.listdir(image_file_dir)])
for i, file_name in enumerate(file_names):
# print(i)
image_file_name = file_name + '.jpg'
image_file_path = os.path.join(image_file_dir, image_file_name)
image_info = dict()
image = cv2.cvtColor(cv2.imread(image_file_path), cv2.COLOR_BGR2RGB)
height, width, _ = image.shape
image_info = {'file_name': image_file_name, 'id': i+1,
'height': height, 'width': width}
annotations_info['images'].append(image_info)
end_path = dirname +'/code/test.json'
print('============',end_path)
with open(end_path, 'w') as f:
json.dump(annotations_info, f, indent=4)
def run_wbf(bboxes, confs, labels, iou_thr=0.50, skip_box_thr=0.0001, weights=None):
scores = [conf for conf in confs]
bboxes, scores, labels = weighted_boxes_fusion(
bboxes, scores, labels, weights=weights, iou_thr=iou_thr, skip_box_thr=skip_box_thr, conf_type='avg')
# bboxes, scores, labels, weights=weights, iou_thr=iou_thr, skip_box_thr=skip_box_thr, conf_type='max')
return bboxes, scores, labels
def make_wbf_json(len_jpg):
result_paths = [
f'{dirname}/1.json.bbox.json',
f'{dirname}/2.json.bbox.json',
f'{dirname}/3.json.bbox.json',
]
img_w = 640
iou_thr = 0.8 # 0.492单光和0.502双光融合：0.519，目前最佳; 0.467单光融合后0.518 （iou=0.8, 然后是avg的方式，max只有0.513）
# iou_thr = 0.9 # 0.492单光和0.502双光融合：0.516
# iou_thr = 0.7 # 0.492单光和0.502双光融合：0.494
# score_thr = 0.02 # clw note TODO
score_thr = 0.000001
norm_h, norm_w = 512, 640 # 防止wbf有些框x超出1
# flip_flags = [False, True]
flip_flags = [False, False, False]
save_path = f'{dirname}/4.json.bbox.json'
model_nums = len(result_paths)
results_all_model = []
for result_path in result_paths:
result = json.load(open(result_path))
imgid_to_results_dict = defaultdict(list)
for i in range(1, len_jpg+1): # 测试集共1000张图，id从1到1000
imgid_to_results_dict[i] = []
for item in tqdm(result):
image_id = item['image_id']
imgid_to_results_dict[image_id].append(item)
results_all_model.append(imgid_to_results_dict)
results_wbf_all = []
test_sample_nums = len_jpg
for sample_id in tqdm(range(1, test_sample_nums+1)):
bboxes_all_model = []
confs_all_model = []
labels_all_model = []
for model_idx, results_single_model in enumerate(results_all_model):
results = results_single_model[sample_id]
bboxes_single_model = []
confs_single_model = []
labels_single_model = []
for dt in results:
conf = dt['score']
category_id = dt['category_id']
if conf < score_thr:
continue
if flip_flags[model_idx] == True:
dt['bbox'][0] = img_w - (dt['bbox'][0] + dt['bbox'][2])
bboxes_single_model.append([dt['bbox'][0]/norm_w, dt['bbox'][1]/norm_h, (dt['bbox'][0]+dt['bbox'][2])/norm_w, (dt['bbox'][1]+dt['bbox'][3])/norm_h]) # x1,y1,x2,y2
confs_single_model.append(conf)
labels_single_model.append(category_id)
bboxes_all_model.append(bboxes_single_model)
confs_all_model.append(confs_single_model)
labels_all_model.append(labels_single_model)
bboxes_new, scores_new, labels_new = run_wbf(bboxes_all_model, confs_all_model, labels_all_model, iou_thr=iou_thr, skip_box_thr=score_thr, weights=None)
bboxes_new[:, 0::2] *= norm_w
bboxes_new[:, 1::2] *= norm_h
for bbox_wbf, score_wbf, label_wbf in zip(bboxes_new, scores_new, labels_new):
x1, y1, x2, y2 = bbox_wbf
w = x2 - x1
h = y2 - y1
res_dict = {
'image_id': sample_id,
'bbox': [x1, y1, w, h],
'score': score_wbf,
'category_id': int(label_wbf)
}
results_wbf_all.append(res_dict)
id_set = set()
for res in results_wbf_all:
id_set.add(res['image_id'])
with open(save_path, 'w') as f:
json.dump(results_wbf_all, f)
def invoke(input_dir, output_path):
make_test_json(input_dir)
path1=dirname+'/make_merge_test'
path2 = dirname+'/code'
print("=====================222======================")
os.system(f'cd {path1} && python test.py --data_path {input_dir}')
input_dir2 = os.path.join(input_dir, "rgb-2")
print("=====================333======================")
output_path2=dirname+'/1.json'
output_path3=dirname+'/2.json'
output_path4=dirname+'/3.json'
os.system(f'cd {path2} && python test.py ./configs/co_dino_5scale_vit_large_gaiic2024_two_stream_custom_v3.py ../data/best_model_epoch_9_only_keep_statedict.pth --data_path {input_dir2} --format-only --options "jsonfile_prefix={output_path2}" ')
print("=====================444======================")
os.system(f'cd {path2} && python test.py ./configs/co_dino_5scale_vit_large_gaiic2024_two_stream_custom_v3.py ../data/best_model_best_bbox_mAP_epoch_11_autov2_640_only_keep_statedict.pth --data_path {input_dir2} --format-only --options "jsonfile_prefix={output_path3}" ')
os.system(f'cd {path2} && python test.py ./configs/1.py ../data/best_model_best_bbox_mAP_epoch_10_only_keep_statedict.pth --data_path {input_dir2} --format-only --options "jsonfile_prefix={output_path4}" ')
source_file =dirname +'/4.json.bbox.json'
print("================wbf=======================")
jpg_files = glob.glob(os.path.join(input_dir2, '*.jpg'))
len_jpg = len(jpg_files)
print(f'======================={len_jpg}===================')
make_wbf_json(len_jpg)
os.makedirs(os.path.dirname(output_path), exist_ok=True)
shutil.copyfile(source_file, output_path)
if __name__ == '__main__':
invoke(f'{dirname}/data/contest_data/test', f'{dirname}/data/result/result.json')