newstart

整理/CrossTestTable.py

@@ -0,0 +1,55 @@
+import numpy as np 
+import json
+import cv2
+import shapely
+import os
+import argparse
+import csv
+
+with open("CrossTest.csv", 'w', newline='') as csvfileOut:
+    detects = ['yolo-Result-best-rulebase', 'unet-Result-best-rulebase',  'marge']
+    regres = ['RARETYPE1', 'RARERGB','RAREBINARY',  'dataColor.RARERGB', 
+              'CRNNTYPE1','CRNNRGB','CRNNBINARY',   'dataColor.CRNNRGB', 
+              'RosettaTYPE1','RosettaRGB','RosettaBINARY',  'dataColor.RosettaRGB',
+              'STARNetTYPE1', 'STARNetRGB','STARNetBINARY',  'dataColor.STARNetRGB',
+              'dataColor.NoneResNetBiLSTMCTCRGB',
+              "voteRGB", "vote2RGB"]
+    table = np.zeros((18, len(detects), len(regres)))
+    for filename in os.listdir("./CrossTest/"):
+        name, ext = os.path.splitext(filename)
+
+        detect, regre, color = name.split("_")
+        regre = regre+color
+        print(filename, detect, regre)
+        with open(os.path.join('./CrossTest/', filename), newline='') as csvfile:
+            rows = csv.reader(csvfile, delimiter=':')
+            for i, row in enumerate(rows):
+                row = row[0].split(",")
+                if detect not in detects:
+                    # print(detect, "not in ")
+                    continue
+                if regre not in regres:
+                    # print(regre, "not in ")
+                    continue
+                table[i, detects.index(detect), regres.index(regre)] = float(row[-1])
+    print("table", table.shape)
+    tableMean = np.mean(table, axis=0)
+    # print(detects)
+    # print(regres)
+    # print(table)
+
+    writer = csv.writer(csvfileOut)
+    writer.writerow([""]+detects)
+    for i in range(len(tableMean[0])):
+        print(row)
+        row = tableMean[:,i]
+        writer.writerow([regres[i]]+row.tolist())
+
+    with open("CrossTestbest.csv", 'w', newline='') as csvfileOut2:
+        writer2 = csv.writer(csvfileOut2)
+        tableMaxIdx = np.argmax(table.reshape((18, len(detects)*len(regres))), axis=1)
+        tableMaxIdx = np.unravel_index(tableMaxIdx, (len(detects), len(regres)))
+        print(tableMaxIdx)
+        for i in range(len(tableMaxIdx[0])):
+            print(i, detects[tableMaxIdx[0][i]].split("-")[0], regres[tableMaxIdx[1][i]], table[i,tableMaxIdx[0][i],tableMaxIdx[1][i]])
+            writer2.writerow(["FPK_"+str(i+1).zfill(2)+".jpg", detects[tableMaxIdx[0][i]].split("-")[0], regres[tableMaxIdx[1][i]], table[i,tableMaxIdx[0][i],tableMaxIdx[1][i]]])

整理/EAST/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2019 SakuraRiven
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

整理/EAST/README.md

@@ -0,0 +1,75 @@
+## Description
+This is a PyTorch Re-Implementation of [EAST: An Efficient and Accurate Scene Text Detector](http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhou_EAST_An_Efficient_CVPR_2017_paper.pdf).
+
+* Only RBOX part is implemented.
+* Using dice loss instead of class-balanced cross-entropy loss. Some codes refer to [argman/EAST](https://github.com/argman/EAST) and [songdejia/EAST](https://github.com/songdejia/EAST)
+* The pre-trained model provided achieves __82.79__ F-score on ICDAR 2015 Challenge 4 using only the 1000 images. see [here](http://rrc.cvc.uab.es/?ch=4&com=evaluation&view=method_info&task=1&m=52405) for the detailed results.
+
+| Model | Loss | Recall | Precision | F-score | 
+| - | - | - | - | - |
+| Original | CE | 72.75 | 80.46 | 76.41 |
+| Re-Implement | Dice | 81.27 | 84.36 | 82.79 |
+
+## Prerequisites
+Only tested on
+* Anaconda3
+* Python 3.7.1
+* PyTorch 1.0.1
+* Shapely 1.6.4
+* opencv-python 4.0.0.21
+* lanms 1.0.2
+
+When running the script, if some module is not installed you will see a notification and installation instructions. __if you failed to install lanms, please update gcc and binutils__. The update under conda environment is:
+
+    conda install -c omgarcia gcc-6
+    conda install -c conda-forge binutils
+
+The original lanms code has a bug in ```normalize_poly``` that the ref vertices are not fixed when looping the p's ordering to calculate the minimum distance. We fixed this bug in [LANMS](https://github.com/SakuraRiven/LANMS) so that anyone could compile the correct lanms. However, this repo still uses the original lanms.
+
+## Installation
+### 1. Clone the repo
+
+```
+git clone https://github.com/SakuraRiven/EAST.git
+cd EAST
+```
+
+### 2. Data & Pre-Trained Model
+* Download Train and Test Data: [ICDAR 2015 Challenge 4](http://rrc.cvc.uab.es/?ch=4&com=downloads). Cut the data into four parts: train_img, train_gt, test_img, test_gt.
+
+* Download pre-trained VGG16 from PyTorch: [VGG16](https://drive.google.com/open?id=1HgDuFGd2q77Z6DcUlDEfBZgxeJv4tald) and our trained EAST model: [EAST](https://drive.google.com/open?id=1AFABkJgr5VtxWnmBU3XcfLJvpZkC2TAg). Make a new folder ```pths``` and put the download pths into ```pths```
+
+```
+mkdir pths
+mv east_vgg16.pth vgg16_bn-6c64b313.pth pths/
+```
+
+Here is an example:
+```
+.
+├── EAST
+│   ├── evaluate
+│   └── pths
+└── ICDAR_2015
+    ├── test_gt
+    ├── test_img
+    ├── train_gt
+    └── train_img
+```
+## Train
+Modify the parameters in ```train.py``` and run:
+```
+CUDA_VISIBLE_DEVICES=0,1 python train.py
+```
+## Detect
+Modify the parameters in ```detect.py``` and run:
+```
+CUDA_VISIBLE_DEVICES=0 python detect.py
+```
+## Evaluate
+* The evaluation scripts are from [ICDAR Offline evaluation](http://rrc.cvc.uab.es/?ch=4&com=mymethods&task=1) and have been modified to run successfully with Python 3.7.1.
+* Change the ```evaluate/gt.zip``` if you test on other datasets.
+* Modify the parameters in ```eval.py``` and run:
+```
+CUDA_VISIBLE_DEVICES=0 python eval.py
+```