PARSING_CHEATSHEET.md

Parsing Cheatsheet

The goal is to create a resource that will help both beginners and advanced users to easily create their parsers by providing some frequently used code snippets and best practices

Useful example

Custom Parser

Use template generator

The IceVision template generator helps you to generate all the methods that you need to implement based on the parsers mixins. The first step is to create a class that inherits from these smaller building blocks called mixins:

!!! warning "Mixins" This is just an example, choose the mixins that are relevant to your use case.

class WheatParser(parsers.FasterRCNN, parsers.FilepathMixin, parsers.SizeMixin):
    pass

We use a method called generate_template that will print out all the necessary methods we have to implement.

WheatParser.generate_template()

Output:

def __iter__(self) -> Any:
def height(self, o) -> int:
def width(self, o) -> int:
def filepath(self, o) -> Union[str, Path]:
def bboxes(self, o) -> List[BBox]:
def labels(self, o) -> List[int]:
def record_id(self, o) -> Hashable:

If, for example, all the images are .jpg and located in the data_dir folder, the image_paths attribute will be set as follow:

def __init__(self, data_dir):
        self.image_paths = get_files(data_dir, extensions=[".jpg"])

Files code snippets

Let's suppose we have the follwoing fname variable:

fname = Path("PennFudanPed/PNGImages/FudanPed00002.png")

Some useful methods are listed below:

fname	PennFudanPed/PNGImages/FudanPed00002.png
fname.exists()	True
fname.with_suffix('.txt')	PennFudanPed/PNGImages/FudanPed00002.txt
fname.stem	FudanPed00002

Parsing code snippets

Read a CSV file using pandas

import pandas as pd
df = pd.read_csv("path/to/csv/file")
df.head() # or df.sample()

Example of parsing bboxes attributes defined as an array, and stored in a string:

bbox = "[834.0, 222.0, 56.0, 36.0]"`
xywh = np.fromstring(bbox[1:-1], sep=",")
print(xywh)

Output: array([834., 222., 56., 36.])

Example of parsing bboxes attributes defined as a string with a blank separator

label = "2 0.527267 0.702972 0.945466 0.467218"
xywh = np.fromstring(label, sep=" ")[1:]
print(xywh)

Output: array([0.527267, 0.702972, 0.945466, 0.467218])

Masks

Let's assume we have the following dictionnary entries to parse in order to create the corresponding masks. Check out the full dictionnary annotations.json

"annotations": [
    {
      "segmentation": [
        [457.3, 258.92, 458.38, 276.22, 467.03, 289.19, 473.51, 305.41, 483.24, 334.59,...]
      ],
      "area": 43522.80595,
      "iscrowd": 0,
      "image_id": 343934,
      "bbox": [
        175.14,
        175.68,
        321.08,
        240
      ],
      "category_id": 4,
      "id": 150977
    },
    {
      "segmentation": [
        [507.9, 413.08, ...]
    ...
    }
    ...
]

We can implement the abstract method masks(), defined in the abstract class MasksMixin that is inhereted by the MaskRCNN class (see parsers documentation), as follow:

class COCOAnnotationParser(MaskRCNN, COCOBBoxParser):
    def masks(self, o) -> List[MaskArray]:
        seg = o["segmentation"]
        if o["iscrowd"]:
            return [RLE.from_coco(seg["counts"])]
        else:
            return [Polygon(seg)]