# Path: object_detection/vod_converter/converter.py
# class Ingestor:
#     def validate(self, path):
#         """
#         Validate that a path contains files / directories expected for a given data format.
# 
#         This is where you can provide feedback to the end user if they are attempting to convert from
#         your format but have passed you path to a directory that is missing the expected files or directory
#         structure.
# 
#         :param path: Where the data is stored
#         :return: (sucess, error message), e.g (False, "error message") if anything is awry, (True, None) otherwise.
#         """
#         return True, None
# 
#     def ingest(self, path):
#         """
#         Read in data from the filesytem.
#         :param path: '/path/to/data/'
#         :return: an array of dicts conforming to `IMAGE_DETECTION_SCHEMA`
#         """
#         pass
# 
# class Egestor:
# 
#     def expected_labels(self):
#         """
#         Return a dict with a key for each label generally expected by this dataset format and
#         any aliases that should be converted.
# 
#         In the example below the expected labels are 'car' and 'pedestrian' and, for example, both
#         'Car' and 'auto' should be converted to 'car'.
# 
#         :return: {'car': ['Car', 'auto'], 'pedestrian': ['Person']}
#         """
#         raise NotImplementedError()
# 
#     def egest(self, image_detections, root):
#         """
#         Output data to the filesystem.
# 
#         Note: image_detections will already have any conversions specified via `expected_labels` applied
#         by the time they are passed to this method.
# 
#         :param image_detections: an array of dicts conforming to `IMAGE_DETECTION_SCHEMA`
#         :param root: '/path/to/output/data/'
#         """
#         raise NotImplementedError()


import csv
import errno
import os
import shutil
from PIL import Image
from object_detection.vod_converter.converter import Ingestor, Egestor
            for row in f_csv:
                x1, y1, x2, y2 = map(float, row[4:8])
                label, truncated, occluded, alpha= row[:4]
                detections.append({
                    'label': label,
                    'occluded': occluded,
                    'truncated': truncated,
                    'alpha': alpha,
                    'left': x1,
                    'right': x2,
                    'top': y1,
                    'bottom': y2,
                })
        return detections


def _image_dimensions(path):
    with Image.open(path) as image:
        return image.width, image.height

def makedirs(path):
    try:
        os.makedirs(path)
    except OSError as e:
        if e.errno != errno.EEXIST:
            raise

DEFAULT_TRUNCATED = 0.0 # 0% truncated
DEFAULT_OCCLUDED = 0    # fully visible


# Path: object_detection/vod_converter/converter.py
# class Ingestor:
#     def validate(self, path):
#         """
#         Validate that a path contains files / directories expected for a given data format.
# 
#         This is where you can provide feedback to the end user if they are attempting to convert from
#         your format but have passed you path to a directory that is missing the expected files or directory
#         structure.
# 
#         :param path: Where the data is stored
#         :return: (sucess, error message), e.g (False, "error message") if anything is awry, (True, None) otherwise.
#         """
#         return True, None
# 
#     def ingest(self, path):
#         """
#         Read in data from the filesytem.
#         :param path: '/path/to/data/'
#         :return: an array of dicts conforming to `IMAGE_DETECTION_SCHEMA`
#         """
#         pass
# 
# class Egestor:
# 
#     def expected_labels(self):
#         """
#         Return a dict with a key for each label generally expected by this dataset format and
#         any aliases that should be converted.
# 
#         In the example below the expected labels are 'car' and 'pedestrian' and, for example, both
#         'Car' and 'auto' should be converted to 'car'.
# 
#         :return: {'car': ['Car', 'auto'], 'pedestrian': ['Person']}
#         """
#         raise NotImplementedError()
# 
#     def egest(self, image_detections, root):
#         """
#         Output data to the filesystem.
# 
#         Note: image_detections will already have any conversions specified via `expected_labels` applied
#         by the time they are passed to this method.
# 
#         :param image_detections: an array of dicts conforming to `IMAGE_DETECTION_SCHEMA`
#         :param root: '/path/to/output/data/'
#         """
#         raise NotImplementedError()


# Path: object_detection/vod_converter/kitti.py
import csv
import errno
import os
import shutil
from PIL import Image
from object_detection.vod_converter.converter import Ingestor, Egestor

            for row in f_csv:
                x1, y1, x2, y2 = map(float, row[4:8])
                label, truncated, occluded, alpha= row[:4]
                detections.append({
                    'label': label,
                    'occluded': occluded,
                    'truncated': truncated,
                    'alpha': alpha,
                    'left': x1,
                    'right': x2,
                    'top': y1,
                    'bottom': y2,
                })
        return detections


def _image_dimensions(path):
    with Image.open(path) as image:
        return image.width, image.height

def makedirs(path):
    try:
        os.makedirs(path)
    except OSError as e:
        if e.errno != errno.EEXIST:
            raise

DEFAULT_TRUNCATED = 0.0 # 0% truncated
DEFAULT_OCCLUDED = 0    # fully visible


class KITTIEgestor(Egestor):