README.md

ImageDatasetViz

Observe dataset of images and targets in few shots

Descriptions

Idea is to create tools to store images, targets from a dataset as a few large images to observe the dataset in few shots.

Installation

with pip

 pip install image-dataset-viz

from sources

python setup.py install

or

pip install git+https://github.com/vfdev-5/ImageDatasetViz.git

Usage

Render a single datapoint

First, we can just take a look on a single data point rendering. Let's assume that we have img as, for example, PIL.Image and target as acceptable target type (str or list of points or PIL.Image mask, etc), thus we can generate a single image with target.

from image_dataset_viz import render_datapoint

# if target is a simple label
res = render_datapoint(img, "test label", text_color=(0, 255, 0), text_size=10)
plt.imshow(res)

# if target is a mask image (PIL.Image)
res = render_datapoint(img, target, blend_alpha=0.5)
plt.imshow(res)

# if target is a bounding box, e.g. np.array([[10, 10], [55, 10], [55, 77], [10, 77]])
res = render_datapoint(img, target, geom_color=(255, 0, 0))
plt.imshow(res)

Example output on Leaf Segmentation dataset from CVPPP2017

Export complete dataset

For example, we have a dataset of image files and annotations files (polygons with labels):

img_files = [
    '/path/to/image_1.ext',
    '/path/to/image_2.ext',
    ...
    '/path/to/image_1000.ext',
]
target_files = [
    '/path/to/target_1.ext2',
    '/path/to/target_2.ext2',
    ...
    '/path/to/target_1000.ext2',
]

We can produce a single image composed of 20x50 small samples with targets to better visualize the whole dataset. Let's assume that we do need a particular processing to open the images in RGB 8bits format:

from PIL import Image

def read_img_fn(img_filepath):
    return Image.open(img_filepath).convert('RGB')

and let's say the annotations are just lines with points and a label, e.g. 12 23 34 45 56 67 car

from pathlib import Path
import numpy as np

def read_target_fn(target_filepath):
    with Path(target_filepath).open('r') as handle:
        points_labels = []
        while True:
            line = handle.readline()
            if len(line) == 0:
                break
            splt = line[:-1].split(' ')  # Split into points and labels
            label = splt[-1]
            points = np.array(splt[:-1]).reshape(-1, 2)
            points_labels.append((points, label))
    return points_labels

Now we can export the dataset

de = DatasetExporter(read_img_fn=read_img_fn, read_target_fn=read_target_fn,
                     img_id_fn=lambda fp: Path(fp).stem, n_cols=20)
de.export(img_files, target_files, output_folder="dataset_viz")

and thus we should obtain a single png image with composed of 20x50 small samples.

Examples

• CIFAR10
• VEDAI

Other basic examples

Image and Mask/BBox/Label

import numpy as np
from image_dataset_viz import render_datapoint, bbox_to_points

img = ((0, 0, 255) * np.ones((256, 256, 3))).astype(np.uint8)
bbox = (
    (bbox_to_points((10, 12, 145, 156)), "A"),
    (bbox_to_points((109, 120, 215, 236)), "B"),
)

mask = 0 * np.ones((256, 256, 3), dtype=np.uint8)
mask[34:145, 56:123, :] = 255

res = render_datapoint(img, (mask, "mask", bbox), blend_alpha=0.5)

Image and Multi-Colored BBoxes

import numpy as np
from image_dataset_viz import render_datapoint, bbox_to_points


img = ((0, 0, 255) * np.ones((256, 256, 3))).astype(np.uint8)

mask = 0 * np.ones((256, 256, 3), dtype=np.uint8)
mask[34:145, 56:123, :] = 255

targets = (
    (mask, {"blend_alpha": 0.6}),
    (
        (bbox_to_points((10, 12, 145, 156)), "A"),
        (bbox_to_points((109, 120, 215, 236)), "B"),
        {"geom_color": (255, 255, 0)}
    ),
    (bbox_to_points((129, 140, 175, 186)), "C"),
)

res = render_datapoint(img, targets, blend_alpha=0.5)