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ctw1500_corner_test.py
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import numpy as np
import torch
import argparse
import torch.nn as nn
import torch.nn.functional as F
import shutil
from torch.autograd import Variable
from torch.utils import data
from pypse import pypse
from dataset import CTW1500Testset_Bound
from models import resnet50
from models.loss import dice_loss
from myutils import Logger
from myutils import AverageMeter
from myutils import RunningScore
from myutils import ohem_single, ohem_batch
from myutils import adjust_learning_rate_StepLR
import os
import sys
import time
import collections
import pyclipper
import Polygon as plg
import cv2
from tqdm import tqdm
def generate_img_result(image, result_filename, root_path):
if not os.path.exists(root_path):
os.makedirs(root_path)
result_filepath = os.path.join(root_path, 'result_%s.jpg'%(result_filename))
cv2.imwrite(result_filepath, image)
def test(args):
testset = CTW1500Testset_Bound()
testloader = torch.utils.data.DataLoader(dataset=testset,
batch_size=1,
shuffle=False,
num_workers=1,
drop_last=True)
if args.backbone == 'res50':
model = resnet50(pretrained=True, num_classes=6)
for param in model.parameters():
param.requires_grad = False
model = model.cuda()
if args.resume is not None:
if os.path.exists(args.resume):
print('Load from', args.resume)
checkpoint = torch.load(args.resume)
# 这里为什么不直接用model.load_state_dict(checkpoint['state_dict'])
# 是因为训练时使用多卡训练，模型中各个参数的名字前面有个前缀，需要去除该前缀
d = collections.OrderedDict()
for key, value in checkpoint['state_dict'].items():
tmp = key[7:]
d[tmp] = value
model.load_state_dict(d)
else:
print('No such checkpoint file at', args.resume)
model.eval()
for idx, (img, original_img) in tqdm(enumerate(testloader)):
img = Variable(img.cuda())
original_img = original_img.numpy().astype('uint8')[0]
original_img = original_img.copy()
outputs = model(img)
score = torch.sigmoid(outputs[:, 0, :, :])
outputs = (torch.sign(outputs - 1.0) + 1) / 2
output_text = outputs[:, 0, :, :]
kernel = outputs[:, 1, :, :]
# top = outputs[:, 2, :, :]
# bot = outputs[:, 3, :, :]
top_left = outputs[:, 2, :, :]
top_right = outputs[:, 3, :, :]
bot_right = outputs[:, 4, :, :]
bot_left = outputs[:, 5, :, :]
output_kernel = outputs[:, 1, :, :] * output_text
# output_top = outputs[:, 2, :, :] * output_text
# output_bot = outputs[:, 3, :, :] * output_text
output_top_left = top_left * output_text
output_top_right = top_right * output_text
output_bot_right = bot_right * output_text
output_bot_left = bot_left * output_text
score = score.data.cpu().numpy()[0].astype(np.float32)
output_text = output_text.data.cpu().numpy()[0].astype(np.uint8)
output_kernel = output_kernel.data.cpu().numpy().astype(np.uint8)
# output_top = output_top.data.cpu().numpy().astype(np.uint8)
# output_bot = output_bot.data.cpu().numpy().astype(np.uint8)
output_top_left = output_top_left.data.cpu().numpy().astype(np.uint8)
output_top_right = output_top_right.data.cpu().numpy().astype(np.uint8)
output_bot_right = output_bot_right.data.cpu().numpy().astype(np.uint8)
output_bot_left = output_bot_left.data.cpu().numpy().astype(np.uint8)
kernel = kernel.data.cpu().numpy()[0].astype(np.uint8)
# top = top.data.cpu().numpy().astype(np.uint8)
# bot = bot.data.cpu().numpy().astype(np.uint8)
# pred = pypse(output_kernel, 10)
pred_top = pypse(output_top_left, 0, connectivity=8)
# scale = (original_img.shape[1] / pred.shape[1], original_img.shape[0] / pred.shape[0])
# bboxes = []
# num_label = np.max(pred) + 1
# for i in range(1, num_label):
# points_loc = np.array(np.where(pred == i)).transpose((1, 0))
# # points = points[:,::-1]
# if points_loc.shape[0] < 300:
# continue
# score_i = np.mean(score[pred == i])
# if score_i < 0.93:
# continue
#
# binary = np.zeros(pred.shape, dtype='uint8')
# binary[pred == i] = 1
#
# contours, _ = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# contour = contours[0]
# contour = contour * scale
# if contour.shape[0] <= 2:
# continue
# contour = contour.astype('int32')
# bboxes.append(contour.reshape(-1))
scale = (original_img.shape[1] / pred_top.shape[1], original_img.shape[0] / pred_top.shape[0])
# pred_top = np.reshape(pred_top, (pred_top.shape[0], pred_top.shape[1], 1))
# pred_top = cv2.resize(pred_top, dsize=(original_img.shape[0], original_img.shape[1]))
# pred_top = pred_top[:, :, 0]
# original_img[pred_top > 0.5, :] = (0, 0, 255)
bboxes = []
num_label = np.max(pred_top) + 1
for i in range(1, num_label):
points_loc = np.array(np.where(pred_top == i)).transpose((1, 0))
# points = points[:,::-1]
# if points_loc.shape[0] < 1:
# continue
# score_i = np.mean(score[pred_top == i])
# if score_i < 0.93:
# continue
binary = np.zeros(pred_top.shape, dtype='uint8')
binary[pred_top == i] = 1
contours, _ = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contour = contours[0]
contour = contour * scale
if contour.shape[0] <= 2:
continue
contour = contour.astype('int32')
bboxes.append(contour.reshape(-1))
torch.cuda.synchronize()
for bbox in bboxes:
cv2.drawContours(original_img, [bbox.reshape(int(bbox.shape[0] / 2), 2)], -1, (0, 0, 255), 1)
image_name = testset.img_paths[idx].split('/')[-1].split('.')[0]
# generate_txt_result_ctw(bboxes, image_name, 'outputs/result_ctw_txt_wh_new')
generate_img_result(original_img, image_name, 'outputs/result_ctw_img_my_top_left')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--backbone', nargs='?', type=str, default='res50')
# parser.add_argument('--resume', nargs='?', type=str, default='/home/data1/zhm/ctw_bound_full_new_checkpoint_0322_200e.pth.tar')
parser.add_argument('--resume', nargs='?', type=str, default='/home/data1/zhm/ctw_corner_checkpoint_0325_100e.pth.tar')
args = parser.parse_args()
test(args)