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evaluation_on_widerface.py
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evaluation_on_widerface.py
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#!/usr/bin/ python3
# -*- coding: utf-8 -*-
# @Time : 2019-10-17
# @Author : vealocia
# @FileName: evaluation_on_widerface.py
import math
import os
import sys
import numpy as np
import cv2
import torch
from nn import *
from utils.torch_utils import select_device
from utils.utils import non_max_suppression
import cv2
import numpy as np
import glob
from hyp import hyp
point_num = hyp['point_num']
device = select_device('0')
net_type = "mbv3_large_75"
long_side = 640 # -1 mean origin shape
assert net_type in ['mbv3_small_1','mbv3_small_75','mbv3_large_1','mbv3_large_75',
"mbv3_large_75_light", "mbv3_large_1_light",'mbv3_small_75_light','mbv3_small_1_light',
]
if net_type.startswith("mbv3_small_1"):
backone = mobilenetv3_small()
elif net_type.startswith("mbv3_small_75"):
backone = mobilenetv3_small( width_mult=0.75)
elif net_type.startswith("mbv3_large_1"):
backone = mobilenetv3_large()
elif net_type.startswith("mbv3_large_75"):
backone = mobilenetv3_large( width_mult=0.75)
if 'light' in net_type:
net = DarknetWithShh(backone, hyp, light_head=True).to(device)
else:
net = DarknetWithShh(backone, hyp).to(device)
weights = "./weights/{}_final.pt".format(net_type)
net.load_state_dict(torch.load(weights, map_location=device)['model'])
net.eval()
val_image_root = "/mnt/data1/yanghuiyu/dlmodel/Fd/RetinaFace/data/retinaface/val/images/" # path to widerface valuation image root
val_result_txt_save_root = "./widerface_evaluate/widerface_evaluation/" # result directory
# val_result_img_save_root = "./result_imgs/" # result directory
counter = 0
for parent, dir_names, file_names in os.walk(val_image_root):
for file_name in file_names:
if not file_name.lower().endswith('jpg'):
continue
orig_image = cv2.imread(os.path.join(parent, file_name))
ori_h, ori_w, _ = orig_image.shape
LONG_SIDE = long_side
if long_side == -1:
max_size = max(ori_w, ori_h)
LONG_SIDE = max(32, max_size - max_size % 32)
if ori_h > ori_w:
scale_h = LONG_SIDE / ori_h
tar_w = int(ori_w * scale_h)
tar_w = tar_w - tar_w % 32
tar_w = max(32, tar_w)
tar_h = LONG_SIDE
else:
scale_w = LONG_SIDE / ori_w
tar_h = int(ori_h * scale_w)
tar_h = tar_h - tar_h % 32
tar_h = max(32, tar_h)
tar_w = LONG_SIDE
scale_w = tar_w * 1.0 / ori_w
scale_h = tar_h * 1.0 / ori_h
image = cv2.resize(orig_image, (tar_w, tar_h))
image = image[..., ::-1]
image = image.astype(np.float32)
image = image / 255.0
image = np.transpose(image, [2, 0, 1])
image = np.expand_dims(image, axis=0)
image = torch.from_numpy(image)
image = image.to(device)
pred = net(image)[0]
pred = non_max_suppression(pred, 0.01, 0.35,
multi_label=False, classes=0, agnostic=False, land=True, point_num=hyp['point_num'])
boxes = []
if pred[0] is not None:
det = pred[0].cpu().detach().numpy()
orig_image = orig_image.astype(np.uint8)
det[:, :4] = det[:, :4] / np.array([scale_w, scale_h] * 2)
det[:, 5:15] = det[:, 5:15] / np.array([scale_w, scale_h] * 5)
for detection in det:
boxes.append(detection[:5].tolist())
event_name = parent.split('/')[-1]
if not os.path.exists(os.path.join(val_result_txt_save_root, event_name)):
os.makedirs(os.path.join(val_result_txt_save_root, event_name))
fout = open(os.path.join(val_result_txt_save_root, event_name, file_name.split('.')[0] + '.txt'), 'w')
# if not os.path.exists(os.path.join(val_result_img_save_root, event_name)):
# os.makedirs(os.path.join(val_result_img_save_root, event_name))
# image = cvDrawBoxes(boxes, im, ratio_w, ratio_h)
# cv2.imwrite(os.path.join(val_result_img_save_root, event_name, file_name.split('.')[0] + '.jpg'), image)
fout.write(file_name.split('.')[0] + '\n')
fout.write(str(len(boxes)) + '\n')
for i in range(len(boxes)):
bbox = boxes[i]
fout.write('%d %d %d %d %.03f' % (math.floor(bbox[0]), math.floor(bbox[1]), math.ceil(bbox[2] - bbox[0]), math.ceil(bbox[3] - bbox[1]), bbox[4] if bbox[4] <= 1 else 1) + '\n')
fout.close()
counter += 1
print('[%d] %s is processed. detect: %d' % (counter, file_name,len(boxes)))