version 0.0.2 release

README.md

@@ -34,6 +34,8 @@ Currently, the following formats are supported:
 
 ## Installation
 
+### Installation from source code
+
 ```bash
 git clone https://github.com/codePerfectPlus/dataset-convertor/
 ```
@@ -48,6 +50,12 @@ source venv/bin/activate
 pip install requirements.txt
 ```
 
+### Installation from PyPI
+
+```bash
+pip install dataset-convertor
+```
+
 ## Usage
 
 convert annotations from one format to another format.
@@ -60,14 +68,24 @@ dataset formatting example:
     - data/yolo5/JPEGImages/*.jpg
     - data/yolo5/labels/*.txt
 
+### Pascal VOC(xml) to yolo(txt)
+
 ```bash
-# example command for pascal-voc(xml) to yolo(txt)
-python convert.py --input-folder ./data/pascal_voc \
-                  --output-folder ./output/yolo5 \
-                  --input-format  voc \
-                  --output-format yolo
+from convert import Convertor
+
+con = Convertor(input_folder='/home/user/data/pascal_voc', output_folder='/home/user/data/yolo5')
+con.voc2yolo()
+```
+
+### from yolo(txt) to Pascal VOC(xml)
+
+```bash
+from convert import Convertor
+con = Convertor(input_folder='/home/user/data/yolo5', output_folder='/home/user/data/pascal_voc')
+con.yolo2voc()
 ```
 
+
 ## Contributing
 
 create an issue/PR if any format is missing.Open-source contribution is welcome.check the [contributing guide](/CONTRIBUTING.md) for details.

convert/__init__.py

@@ -0,0 +1 @@
+from convert.utils import Convertor

convert/utils.py

@@ -0,0 +1,223 @@
+import os
+import shutil
+from glob import glob
+from PIL import Image
+from pathlib import Path
+from datetime import datetime
+from xml.etree import ElementTree as ET
+import logging
+
+logging.basicConfig(format='%(levelname)s - %(asctime)s - %(name)s - %(message)s',
+					datefmt='%d/%m/%Y %I:%M:%S %p',
+					level=logging.INFO,
+					handlers=[
+        				logging.FileHandler('debug.log'),
+        				logging.StreamHandler()])
+
+class Convertor:
+    '''
+    Convertor class is a class for converting images and annotations
+    from one format to another.
+        
+    Usage:
+        # voc to yolo
+        convertor = Convertor('data/VOC2007', 'outout/VOC2007_yolo', 'voc', 'yolo')
+        convertor.voc2yolo()
+
+        # voc to coco
+        convertor = Convertor('data/VOC2007', 'outout/VOC2007_coco', 'voc', 'coco')
+        convertor.voc2coco()
+
+        # yolo to voc
+        convertor = Convertor('data/yolo', 'outout/VOC2007', 'yolo', 'voc')
+        convertor.yolo2voc()
+
+
+    Parameters
+    ----------
+    input_folder : str
+        Path to the input folder.
+    output_folder : str
+        Path to the output folder.
+    input_format : str
+        Input format of the images and annotations.
+    output_format : str
+        Output format of the images and annotations.
+
+    Methods
+    -------
+    voc2yolo()
+        Convert Pascal VOC format to Yolo5 format.
+
+    yolo2voc()
+        Convert Yolo5 format to Pascal VOC format.
+
+    yolo2coco()
+        Convert Yolo5 format to COCO format.
+    
+    coco2yolo()
+        Convert COCO format to Yolo5 format.
+
+    voc2coco()
+        Convert Pascal VOC format to COCO format.
+    
+    coco2voc()
+        Convert COCO format to Pascal VOC format.
+
+    pascal2tfrecord()
+        Convert Pascal VOC format to TFRecord format.
+    
+    coco2tfrecord()
+        Convert COCO format to TFRecord format.
+
+    yolo2tfrecord()
+        Convert Yolo5 format to TFRecord format.
+
+
+    # Pascal Voc - XML
+    # Pascal VOC format is a XML file format for images and annotations.
+
+    # Yolo5 - TXT
+    # Yolo5 format is a text file format for images and annotations.
+
+    # COCO - JSON
+    # COCO format is a JSON file format for images and annotations.
+
+    # TFRecord - TFRecord
+    # TFRecord format is a TFRecord file format for images and annotations.
+
+    '''
+    def __init__(self, input_folder, outout_folder):
+        self.input_image_folder = os.path.join(self.input_folder, 'JPEGImages')
+        self.input_annotation_folder = os.path.join(self.input_folder, 'Annotations')
+        self.output_image_folder = os.path.join(self.output_folder, 'JPEGImages')
+        self.output_annotation_folder = os.path.join(self.output_folder, 'Annotations')
+
+
+    def voc2yolo(self):
+        ''' Pascal Voc format to yolo txt format conversion '''
+
+        if not os.path.exists(self.output_annotation_folder):
+            os.makedirs(self.output_annotation_folder)
+
+        for xml_file in glob(os.path.join(self.input_annotation_folder, '*.xml')):
+
+            if not os.path.exists(self.output_image_folder):
+                os.makedirs(self.output_image_folder)
+
+            shutil.copy(os.path.join(self.input_image_folder, file_name), os.path.join(self.output_image_folder, file_name))
+
+            tree = ET.parse(xml_file)
+            root = tree.getroot()
+
+            file_name = root.find('filename').text
+            width = int(root.find('size').find('width').text)
+            height = int(root.find('size').find('height').text)
+
+            for obj in root.iter('object'):
+                xmin = int(obj.find('bndbox').find('xmin').text)
+                ymin = int(obj.find('bndbox').find('ymin').text)
+                xmax = int(obj.find('bndbox').find('xmax').text)
+                ymax = int(obj.find('bndbox').find('ymax').text)
+                label = obj.find('name').text
+
+                # conver xmin ymin xmax ymax to x y w h
+                x = (xmin + xmax) / width
+                y = (ymin + ymax) / height
+                w = (xmax - xmin) / width
+                h = (ymax - ymin) / height
+
+                # FIXME: conver label to id
+                with open(os.path.join(self.output_annotation_folder, file_name[:-4] + '.txt'), 'a') as f:
+                    f.write(label + ' ' + str(x) + ' ' + str(y) + ' ' + str(w) + ' ' + str(h) + '\n')
+
+
+    def yolo2voc(self):
+        ''' yolo txt format to pascal voc xml format '''
+
+        # create output Annotations folder if not exists
+        if not os.path.exists(self.output_annotation_folder):
+                os.makedirs(self.output_annotation_folder)
+
+        for txt_file in glob(os.path.join(self.input_annotation_folder, '*.txt')):
+            image_file_name = os.path.basename(txt_file)[:-4] + '.jpg'
+
+            # create output image file directory if not exists
+            if not os.path.exists(self.output_image_folder):
+                os.makedirs(self.output_image_folder)
+
+            # copy image file to output image file directory
+            shutil.copy(os.path.join(self.input_image_folder, image_file_name),
+                        os.path.join(self.output_image_folder, image_file_name))
+
+            # read txt file
+            with open(txt_file, 'r') as f:
+                lines = f.readlines()
+                for line in lines:
+                    line = line.strip()
+                    label, x, y, w, h = line.split(' ')
+                    img = Image.open(os.path.join(self.input_image_folder, image_file_name))
+
+                    img_width, img_height = img.size
+
+                    xmin = int(float(x) * img_width)
+                    ymin = int(float(y) * img_height)
+                    xmax = int(float(x) * img_width + float(w) * img_width)
+                    ymax = int(float(y) * img_height + float(h) * img_height)
+
+                    # create xml file
+                    # REVIEW: need to REVIEW the xml file formation
+                    root = ET.Element('annotation')
+                    ET.SubElement(root, 'folder').text = 'VOC'
+                    ET.SubElement(root, 'filename').text = image_file_name
+                    ET.SubElement(root, 'source').text = 'https://github.com/codePerfectPlus/dataset-convertor'
+                    ET.SubElement(root, 'database').text = 'VOC Format'
+                    ET.SubElement(root, 'date').text = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+                    root2 = ET.SubElement(root, 'size')
+                    ET.SubElement(root2, 'width').text = str(img_width)
+                    ET.SubElement(root2, 'height').text = str(img_height)
+                    ET.SubElement(root2, 'depth').text = '3'
+                    root3 = ET.SubElement(root, 'object')
+                    ET.SubElement(root3, 'name').text = label
+                    ET.SubElement(root3, 'pose').text = 'Unspecified'
+                    ET.SubElement(root3, 'truncated').text = '0'
+                    ET.SubElement(root3, 'difficult').text = '0'
+                    root4 = ET.SubElement(root3, 'bndbox')
+                    ET.SubElement(root4, 'xmin').text = str(xmin)
+                    ET.SubElement(root4, 'ymin').text = str(ymin)
+                    ET.SubElement(root4, 'xmax').text = str(xmax)
+                    ET.SubElement(root4, 'ymax').text = str(ymax)
+
+                    # write xml file
+                    tree = ET.ElementTree(root)
+                    tree.write(os.path.join(self.output_annotation_folder, image_file_name[:-4] + '.xml'))
+
+
+    def yolo2coco(self):
+        ''' yolo txt format to coco json format '''
+        pass
+
+    def coco2yolo(self):
+        ''' coco json format to yolo txt format '''
+        pass
+
+    def voc2coco(self):
+        ''' voc xml format to coco json format '''
+        pass
+
+    def coco2voc(self):
+        ''' coco json format to voc xml format '''
+        pass
+
+    def coco2tfrecord(self):
+        ''' coco json format to tfrecord format '''
+        pass
+
+    def yolo2tfrecord(self):
+        ''' yolo txt format to tfrecord format '''
+        pass
+
+    def voc2tfrecord(self):
+        ''' voc xml format to tfrecord format '''
+        pass
+

setup.py

@@ -0,0 +1,37 @@
+import setuptools
+
+with open("README.md", "r") as fh:
+    long_description = fh.read()
+
+setuptools.setup(
+    #Here is the module name.
+    name="dataset-convertor",
+
+    #version of the module
+    version="0.0.2",
+
+    #Name of Author
+    author="CodePerfectPlus",
+
+    #your Email address
+    author_email="deepak008@live.com",
+
+    #Small Description about module
+    description="Convert Dataset from one format to another format",
+
+    long_description=long_description,
+
+    #Specifying that we are using markdown file for description
+    long_description_content_type="text/markdown",
+
+    #Any link to reach this module, if you have any webpage or github profile
+    url="https://github.com/codePerfectPlus/dataset-convertor",
+    packages=setuptools.find_packages(),
+
+    #classifiers like program is suitable for python3, just leave as it is.
+    classifiers=[
+        "Programming Language :: Python :: 3",
+        "License :: OSI Approved :: MIT License",
+        "Operating System :: OS Independent",
+    ],
+)