def get_img(inp_res = 512):
"""
Load validation images
"""
if os.path.exists(valid_filepath) is False:
from utils.build_valid import main
main()
dic = pickle.load(open(valid_filepath, 'rb'))
paths, anns, idxes, info = [dic[i] for i in ['path', 'anns', 'idxes', 'info']]
total = len(paths)
tr = tqdm.tqdm( range(0, total), total = total )
for i in tr:
img = cv2.imread(paths[i])[:,:,::-1]
yield anns[i], img
def get_img(inp_res=512):
"""
Load validation images
"""
# load valid file -> validation.pkl or create one from valid_id txt file
if os.path.exists(valid_filepath) is False:
from utils.build_valid import main
main()
# load pkl file and extract each images' paths, anns, idxes and info
dic = pickle.load(open(valid_filepath, 'rb'))
paths, anns, idxes, info = [
dic[i] for i in ['path', 'anns', 'idxes', 'info']
]
total = len(paths)
tr = tqdm.tqdm(range(0, total), total=total)
# this is to generate a dictionary with each image and annotation
for i in tr:
img = cv2.imread(paths[i])[:, :, ::-1]
yield anns[i], img
mode = config['opt'].mode
def runner(imgs):
return func(0, config, 'inference', imgs=torch.Tensor(np.float32(imgs)))['preds']
def do(img):
ans, scores = multiperson(img, runner, mode)
if len(ans) > 0:
ans = ans[:,:,:3]
pred = genDtByPred(ans)
for i, score in zip( pred, scores ):
i['score'] = float(score)
return pred
gts = []
preds = []
idx = 0
for anns, img in get_img(inp_res=-1):
idx += 1
gts.append(anns)
preds.append(do(img))
prefix = os.path.join('exp', config['opt'].exp)
coco_eval(prefix, preds, gts)
if __name__ == '__main__':
main()
