def eval(model, config, device, thre, long_size=1280):
model.eval()
img_path = os.path.join(config.testroot, 'test_img')
save_path = os.path.join(config.workspace, 'output_eval')
# if os.path.exists(save_path):
# shutil.rmtree(save_path, ignore_errors=True)
if not os.path.exists(save_path):
os.makedirs(save_path)
long_size = 2240
# 预测所有测试图片
img_paths = [os.path.join(img_path, x) for x in os.listdir(img_path)]
for img_path in tqdm(img_paths, desc='test models'):
img_name = os.path.basename(img_path).split('.')[0]
save_name = os.path.join(save_path, 'res_' + img_name + '.txt')
assert os.path.exists(img_path), 'file is not exists'
img = cv2.imread(img_path)
h, w = img.shape[:2]
scale = long_size / max(h, w)
img = cv2.resize(img, None, fx=scale, fy=scale)
# 将图片由(w,h)变为(1,img_channel,h,w)
tensor = transforms.ToTensor()(img)
tensor = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])(tensor)
tensor = tensor.unsqueeze_(0)
tensor = tensor.to(device)
with torch.no_grad():
preds = model(tensor)
preds, boxes_list = pse_decode(preds[0], config.scale)
scale = (preds.shape[1] * 1.0 / w, preds.shape[0] * 1.0 / h)
if len(boxes_list):
boxes_list = boxes_list / scale
np.savetxt(save_name,
boxes_list.reshape(-1, 8),
delimiter=',',
fmt='%d')
# 开始计算 recall precision f1
methodHmean, methodPrecision, methodRecall = getresult(
save_path, config.gt_name)
print("precision: {} , recall: {}, f1: {}".format(methodPrecision,
methodRecall,
methodHmean))
def eval(model, save_path, test_path, device):
model.eval()
img_path = os.path.join(test_path, 'val', 'image')
if os.path.exists(save_path):
shutil.rmtree(save_path, ignore_errors=True)
if not os.path.exists(save_path):
os.makedirs(save_path)
long_size = 2240
# 预测所有测试图片
img_paths = [os.path.join(img_path, x) for x in os.listdir(img_path)]
for img_path in tqdm(img_paths, desc='test models'):
img_name = os.path.basename(img_path).split('.')[0]
save_name = os.path.join(save_path, 'res_' + img_name + '.txt')
assert os.path.exists(img_path), 'file is not exists'
img = cv2.imread(img_path)
org_img = img.copy()
h, w = img.shape[:2]
#if max(h, w) > long_size:
# scale = long_size / max(h, w)
# img = cv2.resize(img, None, fx=scale, fy=scale)
img = scale_image(img)
# 将图片由(w,h)变为(1,img_channel,h,w)
tensor = transforms.ToTensor()(img)
tensor = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])(tensor)
tensor = tensor.unsqueeze_(0)
tensor = tensor.to(device)
with torch.no_grad():
preds = model(tensor)
preds, boxes_list = pse_decode(preds[0], config.scale, org_img,
config.is_test)
scale = (preds.shape[1] * 1.0 / w, preds.shape[0] * 1.0 / h)
if len(boxes_list):
boxes_list = boxes_list / scale
np.savetxt(save_name,
boxes_list.reshape(-1, 8),
delimiter=',',
fmt='%d')
# 开始计算 recall precision f1
f_score_new = getresult(save_path, config.gt_name)
return f_score_new
def eval(model, workspace, test_path, is_test, device):
model.eval()
# torch.cuda.empty_cache() # speed up evaluating after training finished
if is_test:
img_path = os.path.join(test_path, 'test')
save_path = os.path.join(workspace, 'output_test')
else:
img_path = os.path.join(test_path, 'val', 'image')
save_path = os.path.join(workspace, 'output')
gt_path = os.path.join(test_path, 'gt/Test/')
if os.path.exists(save_path):
shutil.rmtree(save_path, ignore_errors=True)
if not os.path.exists(save_path):
os.makedirs(save_path)
vis_icd17 = os.path.join(workspace, 'vis_icd17')
if os.path.exists(vis_icd17):
shutil.rmtree(vis_icd17, ignore_errors=True)
if not os.path.exists(vis_icd17):
os.makedirs(vis_icd17)
short_size = 1600
# 预测所有测试图片
img_paths = [os.path.join(img_path, x) for x in os.listdir(img_path)]
gt_paths = [
os.path.join(gt_path, 'poly_gt_' + x.split('.')[0] + '.mat')
for x in os.listdir(img_path)
]
for idx, img_path_one in enumerate(tqdm(img_paths, desc='test models')):
img_name = os.path.basename(img_path_one).split('.')[0]
if is_test:
save_name = os.path.join(
save_path, 'res_' + img_name.split('ts_')[-1] + '.txt')
else:
save_name = os.path.join(save_path, 'res_' + img_name + '.txt')
assert os.path.exists(img_path_one), 'file is not exists'
img = readImg(img_path_one)
org_img = img.copy()
h, w = img.shape[:2]
img = scale_image(img, short_size)
tensor = transforms.ToTensor()(img)
tensor = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])(tensor)
tensor = tensor.unsqueeze_(0)
tensor = tensor.to(device)
with torch.no_grad():
preds = model(tensor)
preds, boxes_list = pse_decode(preds[0], config.scale, org_img,
is_test)
# scale = (preds.shape[1] * 1.0 / w, preds.shape[0] * 1.0 / h)
# if len(boxes_list):
# boxes_list = boxes_list / scale
if is_test:
np.savetxt(save_name,
boxes_list.reshape(-1, 9),
delimiter=',',
fmt='%d')
else:
np.savetxt(save_name,
boxes_list.reshape(-1, 8),
delimiter=',',
fmt='%d')
# recall precision f1
if not is_test:
f_score_new = getresult(save_path, config.gt_name)
return f_score_new