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infer_img_atten.py
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infer_img_atten.py
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#!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@File : infer.py
@Time : 2021/08/09 17:16:57
@Author : weijiawu
@Version : 1.0
@Contact : weijiawu@zju.edu.cn
@License : (C)Copyright 2021-2022, Zhejiang University
@Desc : 改进PANPP-视频OCR
'''
import torchvision.transforms as transforms
from PIL import Image
from models import build_model
from mmcv import Config
from glob import glob
import torch
import numpy as np
import cv2
from mmcv import Config
from dataset.dataset_tool import get_vocabulary
from models.utils import fuse_module
from os import path as osp
from tracker.video_tools import visulization as vis
# here put the import lib
from tqdm import tqdm
import os
import time
from scipy.spatial.distance import cdist
class PANppE2E(object):
def __init__(self, checkpoint_path, config_path, ctc=False):
self.voc, self.char2id, self.id2char = get_vocabulary('LOWERCASE', use_ctc=ctc)
self.device = torch.device('cuda')
self.model = self.build_model(config_path, checkpoint_path)
if self.cfg.test_cfg.is_half:
self.model.half()
self.model = self.model.to(self.device)
self.img_size = self.cfg.data.test.short_size
self.align_mode = self.cfg.data.test.align_mode
assert self.align_mode in ("short", "long", "force", "online")
print('align_mode', self.align_mode, self.img_size)
def build_model(self, config_path, checkpoint_path):
self.cfg = Config.fromfile(config_path)
for d in [self.cfg, self.cfg.data.test]:
d.update(dict(
report_speed=True
))
if hasattr(self.cfg.model, 'recognition_head'):
self.cfg.model.recognition_head.update(dict(
voc=self.voc,
char2id=self.char2id,
id2char=self.id2char,
))
if hasattr(self.cfg.model, 'description_head'):
self.cfg.model.description_head.update(dict(
voc=self.voc
))
model = build_model(self.cfg.model)
model = model.cuda()
checkpoint = torch.load(checkpoint_path)
d = dict()
for key, value in checkpoint['state_dict'].items():
if "s_desc" in key or "s_rec" in key or "s_det" in key:
print(key, value)
continue
tmp = key[7:]
d[tmp] = value
model.load_state_dict(d, strict=False)
model = fuse_module(model)
model.eval()
# num_param = sum(param.numel() if param in for param in model.parameters())
rec_parameter = 0
for name, param in model.named_parameters():
if name.split('.')[0] == 'rec_head':
rec_parameter+=param.numel()
# print(name)
# print(param.numel())
print("rec head parameter:",rec_parameter)
return model
def preprocess(self, img_path):
data = {}
frame_id = osp.basename(img_path).split('.')[0]
img = cv2.imread(img_path)
img = img[:, :, [2, 1, 0]]
img_meta = dict(
org_img_size=np.array([img.shape[:2]]))
if self.align_mode == 'short':
# img, valid_size = self.scale_aligned_long_padding(img, self.img_size)
img, valid_size = self.scale_aligned_short(img, self.img_size)
elif self.align_mode == 'long':
img, valid_size = self.scale_aligned_long_padding(img, self.img_size)
elif self.align_mode == 'force':
img, valid_size = cv2.resize(img, dsize=(self.img_size, self.img_size)), (self.img_size, self.img_size)
elif self.align_mode == 'online':
img, valid_size = self.scale_aligned_force_padding(img)
# img = cv2.resize(img, dsize=(736, 1280))
# print(img.shape, valid_size)
img_meta.update(dict(
img_size=np.array([img.shape[:2]])
))
img_meta.update(dict(
valid_size=np.array([valid_size])
))
img = Image.fromarray(img)
img = img.convert('RGB')
img = transforms.ToTensor()(img)
img = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])(img)
# img = img.cuda().unsqueeze(0)
if self.cfg.test_cfg.is_half:
img = img.type(torch.HalfTensor).cuda().unsqueeze(0)
else:
img = img.cuda().unsqueeze(0)
data = dict(
imgs=img,
img_metas=img_meta,
)
data.update(dict(
cfg=self.cfg
))
return data
def predict(self, img_path):
start = time.time()
data = self.preprocess(img_path)
# print("preprocess:",time.time()-start)
start = time.time()
with torch.no_grad():
outputs = self.model(**data)
# print("model:",time.time()-start)
start = time.time()
struct_outputs = self.post_process(outputs)
# print("post_process:",time.time()-start)
start = time.time()
return struct_outputs,outputs
def post_process(self, outputs):
res = {}
num_dets = len(outputs['bboxes'])
# valid_indexs = [i for i in range(num_dets)] # if float(outputs['word_scores'][i]) > 0.4]
# print(outputs['word_scores'])
valid_indexs = [i for i in range(num_dets) if float(outputs['word_scores'][i]) > self.cfg.test_cfg.min_rec_score]
res['dets'] = np.zeros((len(valid_indexs), 9))
res['contents'] = [""] * len(valid_indexs)
res['word_scores'] = [""] * len(valid_indexs)
res['id_features'] = np.zeros((len(valid_indexs), 128))
res['out_rec'] = np.zeros((len(valid_indexs), 39,32))
# res['report_speed'] = float(outputs['rec_time'])
for i, valid_index in enumerate(valid_indexs):
res['dets'][i][:8] = outputs['bboxes'][valid_index]
res['dets'][i][8] = outputs['scores'][valid_index]
res['contents'][i] = outputs['words'][valid_index]
res['word_scores'][i] = float(outputs['word_scores'][valid_index])
res['id_features'][i] = outputs['multi_info_feature'][valid_index]
res['out_rec'][i] = outputs['out_rec'][valid_index]
return res
@staticmethod
def scale_aligned_short(img, short_size=736):
h, w = img.shape[0:2]
scale = short_size * 1.0 / min(h, w)
h = int(h * scale + 0.5)
w = int(w * scale + 0.5)
if h % 32 != 0:
h = h + (32 - h % 32)
if w % 32 != 0:
w = w + (32 - w % 32)
img = cv2.resize(img, dsize=(w, h))
return img, img.shape[:2]
@staticmethod
def scale_aligned_long_padding(img, long_size=736):
h, w = img.shape[0:2]
scale = long_size * 1.0 / max(h, w)
h = int(h * scale + 0.5)
w = int(w * scale + 0.5)
if h % 32 != 0:
h = h + (32 - h % 32)
if w % 32 != 0:
w = w + (32 - w % 32)
img = cv2.resize(img, dsize=(w, h))
valid_shape = (h, w)
img = cv2.copyMakeBorder(img, 0, long_size-h, 0,long_size-w, borderType=cv2.BORDER_CONSTANT, value=(0,)) # top, bottom, left, right
# print('valid_shape', h, w)
# print('out_shape', img.shape)
# cv2.imwrite('./debug.jpg', img)
return img, valid_shape
@staticmethod
def scale_aligned_force_padding(img, short_size=736, long_size=1280):
h, w = img.shape[0:2]
scale = short_size * 1.0 / w
h = int(h * scale + 0.5)
if h > long_size:
h = long_size
w = short_size # int(w * scale + 0.5)
if h % 32 != 0:
h = h + (32 - h % 32)
# if w % 32 != 0:
# w = w + (32 - w % 32)
img = cv2.resize(img, dsize=(w, h))
valid_shape = (h, w)
img = cv2.copyMakeBorder(img, 0, long_size-h, 0, 0, borderType=cv2.BORDER_CONSTANT, value=(0,)) # top, bottom, left, right
return img, valid_shape
if __name__ == '__main__':
config_path = './config/pan_pp/pan_pp_r18_atten_ic15_detrec.py'
checkpoint_path = './outputs/pan_pp_r18_atten_ic15_desc/12_0_0_0_0_checkpoint.pth.tar'
img_path = '/share/wuweijia/Data/ICDAR2013_video/test/frames/'
model = PANppE2E(checkpoint_path, config_path, ctc=False)
show = False
import time
start = time.time()
image_len = 0
head = 0
for cls in tqdm(os.listdir(img_path)):
cls_path = os.path.join(img_path,cls)
image_len += len(os.listdir(cls_path))
# test_list = ["598.jpg","600.jpg"]
for image_ in tqdm(os.listdir(cls_path)):
if "jpg" in image_:
image_path_ = os.path.join(cls_path,image_)
res,outputs = model.predict(image_path_)
res_path = "./eval/Evaluation_Detection_ICDAR2013_video/icdar15_evaluate/res/" + "{}.json{}.txt".format(cls,image_.replace(".jpg",""))
vis.write_detections(res['dets'], res['contents'], res['word_scores'],res_path)
# if image_ == "598.jpg":
# feature1 = res['id_features']
# else:
# feature2 = res['id_features']
# cosine = cdist(np.array(feature1), np.array(feature2), 'cosine')
# print("cosine：",cosine)
# print(res["out_rec"][0].sum(axis=0))
if show and res['dets'].any():
plt_img,hotmap = vis.plot_detections(cv2.imread(image_path_), res['dets'], res['contents'], res['word_scores'], res['out_rec'])
cv2.imwrite('./outputs/demo/show/{}'.format(image_), plt_img)
cv2.imwrite('./outputs/demo/show/hot{}'.format(image_), hotmap)
# break
print("time cost:",time.time() - start)
print("image number:",image_len)
# print("head cost:",head)
# plt_img = vis.plot_detections(cv2.imread(img_path), res['dets'], res['contents'], res['word_scores'])
# cv2.imwrite('./debug.jpg', plt_img)
# del res['id_features']
# print(res)