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infer_icd15.py
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infer_icd15.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 : [email protected]
@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 time
# import mxnet as mx
import os
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) #CHINESE LOWERCASE
print("voc:",len(self.voc))
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()
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_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
))
# PCA
# gt_instance = np.zeros(img.shape[-2:], dtype='uint8')
# unique_labels_ = []
# box = []
# IDs = []
# for i,data1 in enumerate(ann):
# ID = data1["ID"]
# id_content = str(data1["transcription"])
# # if "#" in id_content:
# IDs.append(ID)
# h, w = img_meta["org_img_size"][0]
# bbox = np.array([float(aa) for aa in data1["points"]])
# box.append(np.array(bbox))
# bbox = np.array(bbox) / ([w * 1.0, h * 1.0] * 4)
# h, w = img.shape[-2:]
# bbox = bbox * ([w * 1.0, h * 1.0] * 4)
# bbox = np.reshape(bbox,(4,2)).astype('int32')
# cv2.drawContours(gt_instance, [bbox], -1, i + 1, -1)
# unique_labels_.append(i + 1)
# max_instance = np.max(gt_instance)
# gt_bboxes = [[0,0,0,0]]
# for i in range(1, max_instance + 1):
# ind = gt_instance == i
# if np.sum(ind) == 0:
# continue
# points = np.array(np.where(ind)).transpose((1, 0))
# tl = np.min(points, axis=0)
# br = np.max(points, axis=0) + 1
# gt_bboxes.append((tl[0], tl[1], br[0], br[1]))
# gt_instance = torch.from_numpy(gt_instance).long().cuda().unsqueeze(0)
# unique_labels_ = torch.from_numpy(np.array(unique_labels_)).long().cuda().unsqueeze(0)
# data.update(dict(
# gt_instances=gt_instance,
# identifies = unique_labels_
# ))
# gt_bboxes = torch.from_numpy(np.array(gt_bboxes)).long().cuda().unsqueeze(0)
# box = torch.from_numpy(np.array(box)).long()
# data.update(dict(
# gt_bboxes=gt_bboxes,
# gt_words = box
# ))
return data
def predict(self, img_path):
start = time.time()
# data,IDs = self.preprocess(img_path,ann)
data = self.preprocess(img_path)
start = time.time()
with torch.no_grad():
outputs = self.model(**data)
# for i,ID in enumerate(IDs):
# feature = outputs['multi_info_feature'][i]
# f2 = open('./det_features.txt','a')
# f2.write('{}'.format(ID))
# for yaa in feature:
# f2.write('_{}'.format(yaa))
# f2.write('\n')
# f2.close()
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), 128,32))
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 % 4 != 0:
h = h + (4 - h % 4)
if w % 4 != 0:
w = w + (4 - w % 4)
if h == img.shape[0] and w == img.shape[1]:
return img, img.shape[:2]
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_ic15_detrec.py'
checkpoint_path = './outputs/pan_pp_r18_ic15_detrec/F_Score0.728.pth.tar'
img_path = '/share/wuweijia/Data/ICDAR2013_video/test/frames/'
# img_path = "/home/wangjue_Cloud/wuweijia/Data/ICDAR2013_video/test/frames"
# img_path = '/home/wangjue_Cloud/wuweijia/Data/VideoText/YVT/YVT_test'
model = PANppE2E(checkpoint_path, config_path, ctc=True)
show = False
import time
start = time.time()
image_len = 0
rec_head_cost = 0
backbone_time = 0
neck_time = 0
det_head_time = 0
desc_time = 0
print("ssss")
for cls in tqdm(os.listdir(img_path)):
if ".xml" in cls or ".txt" in cls:
continue
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_)
# image_path_ = image_
print(image_path_)
res,outputs = model.predict(image_path_)
rec_head_cost+= outputs["rec_time"]
backbone_time+= outputs["backbone_time"]
neck_time+= outputs["neck_time"]
det_head_time+= outputs["det_head_time"]
desc_time+= outputs["desc_time"]
res_path = "./eval/Evaluation_Detection_ICDAR2013_video/icdar15_evaluate/res/" + "{}.json{}.txt".format(cls,image_.replace(".jpg",""))
# res_path = "./eval/Evaluation_Detection_YVT_video/icdar15_evaluate/res/" + "{}.json{}.txt".format(cls,int(image_.replace(".jpg","").split("_")[-1][-4:]))
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']
# # print(feature1.shape)
# 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():
# print(res['id_features'][0:128:10])
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("rec_head_cost cost:",rec_head_cost)
print("backbone_time cost:",backbone_time)
print("neck_time cost:",neck_time)
print("det_head_time cost:",det_head_time)
print("desc_time cost:",desc_time)
# print(res)