app.py

import cv2
import inspect
import numpy as np
import albumentations as A
import gradio as gr
from typing import get_type_hints
from PIL import Image, ImageDraw
import base64
import io
from PIL import Image
from functools import wraps
from copy import deepcopy


DEFAULT_TRANSFORM = "CoarseDropout"

DEFAULT_IMAGE = "images/doctor.webp"
DEFAULT_IMAGE_HEIGHT = 400
DEFAULT_IMAGE_WIDTH = 600
DEFAULT_BOXES = [[265, 121, 326, 177], [192, 169, 401, 395]]
DEFAULT_KEYPOINTS = [
    [(x_min + x_max) // 2, (y_min + y_max) // 2]
    for x_min, y_min, x_max, y_max in DEFAULT_BOXES
]
CORENERS = [[[x_min, y_min], [x_max, y_max], [x_min, y_max], [x_max, y_min]] for x_min, y_min, x_max, y_max in DEFAULT_BOXES]
for bbox_corners in CORENERS:
    DEFAULT_KEYPOINTS += bbox_corners

BASE64_DEFAULT_MASKS = [
    {
        "label": "Coverall",
        # light green color
        "color": (144, 238, 144),
        "mask": "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",
    },
    {
        "label": "Mask",
        # light blue color
        "color": (173, 216, 230),
        "mask": "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",
    },
]

# Get all the transforms from the albumentations library
transforms_map = {
    name: cls
    for name, cls in vars(A).items()
    if inspect.isclass(cls) and issubclass(cls, (A.DualTransform, A.ImageOnlyTransform))
}
transforms_map.pop("DualTransform", None)
transforms_map.pop("ImageOnlyTransform", None)
transforms_keys = list(sorted(transforms_map.keys()))

# Decode the masks
for mask in BASE64_DEFAULT_MASKS:
    mask["mask"] = np.array(Image.open(io.BytesIO(base64.b64decode(mask["mask"]))).convert("L"))


def run_with_retry(compose):
    @wraps(compose)
    def wrapper(*args, **kwargs):
        processors = deepcopy(compose.processors)
        for _ in range(4):
            try:
                result = compose(*args, **kwargs)
                break
            except NotImplementedError as e:
                print(f"Caught NotImplementedError: {e}")
                if "bbox" in str(e):
                    kwargs.pop("bboxes", None)
                    kwargs.pop("category_id", None)
                    compose.processors.pop("bboxes")
                if "keypoint" in str(e):
                    kwargs.pop("keypoints", None)
                    compose.processors.pop("keypoints")
                if "mask" in str(e):
                    kwargs.pop("mask", None)
            except Exception as e:
                compose.processors = processors
                raise e
        compose.processors = processors
        return result
    return wrapper


def draw_boxes(image, boxes, color=(255, 0, 0), thickness=2) -> np.ndarray:
    """Draw boxes with PIL."""
    pil_image = Image.fromarray(image)
    draw = ImageDraw.Draw(pil_image)
    for box in boxes:
        x_min, y_min, x_max, y_max = box
        draw.rectangle([x_min, y_min, x_max, y_max], outline=color, width=thickness)
    return np.array(pil_image)


def draw_keypoints(image, keypoints, color=(255, 0, 0), radius=2):
    """Draw keypoints with PIL."""
    pil_image = Image.fromarray(image)
    draw = ImageDraw.Draw(pil_image)
    for keypoint in keypoints:
        x, y = keypoint
        draw.ellipse([x - radius, y - radius, x + radius, y + radius], fill=color)
    return np.array(pil_image)


def get_rgb_mask(masks):
    """Get the RGB mask from the binary mask."""
    rgb_mask = np.zeros((DEFAULT_IMAGE_HEIGHT, DEFAULT_IMAGE_WIDTH, 3), dtype=np.uint8)
    for data in masks:
        mask = data["mask"]
        rgb_mask[mask > 0] = np.array(data["color"])
    return rgb_mask


def draw_mask(image, mask):
    """Draw the mask on the image."""
    image_with_mask = cv2.addWeighted(image, 0.5, mask, 0.5, 0)
    return image_with_mask


def draw_not_implemented_image(image):
    """Draw the image with a text. In the middle."""
    pil_image = Image.fromarray(image)
    draw = ImageDraw.Draw(pil_image)
    # align in the centerm, and make bigger font
    text = "NOT IMPLEMETED FOR THIS TYPE OF ANNOTATIONS"
    length = draw.textlength(text)
    draw.text(
        (DEFAULT_IMAGE_WIDTH // 2 - length // 2, DEFAULT_IMAGE_HEIGHT // 2),
        text,
        fill=(255, 0, 0),
        align="center",
    )
    return np.array(pil_image)


def get_formatted_signature(function_or_class, indentation=4):

    signature = inspect.signature(function_or_class)
    type_hints = get_type_hints(function_or_class)

    args = []
    for param in signature.parameters.values():
        if param.name == "p":
            str_param = "p=1.0,"
        elif param.default == inspect.Parameter.empty:
            str_param = f"{param.name}=,"
        else:
            if isinstance(param.default, str):
                str_param = f'{param.name}="{param.default}",'
            else:
                str_param = f"{param.name}={param.default},"

        annotation = type_hints.get(param.name, param.annotation)
        if isinstance(param.annotation, type):
            str_param += f"  # {param.annotation.__name__}"
        else:
            str_annotation = str(annotation).replace("typing.", "")
            str_param += f"  # {str_annotation}"
        str_param = "\n" + " " * indentation + str_param
        args.append(str_param)

    result = "(" + "".join(args) + "\n" + " " * (indentation - 4) + ")"
    return result


def update(image, code):
    try:
        augmentation = eval(code)
        compose = A.Compose(
            [augmentation],
            bbox_params=A.BboxParams(format="pascal_voc", label_fields=["category_id"]),
            keypoint_params=A.KeypointParams(format="xy"),
            additional_targets={"not_implemented_image": "image"}
        )
        compose = run_with_retry(compose)  # to prevent NotImplementedError

        keypoints = DEFAULT_KEYPOINTS
        bboxes = DEFAULT_BOXES
        mask = get_rgb_mask(BASE64_DEFAULT_MASKS)
        augmented = compose(
            image=image,
            not_implemented_image=draw_not_implemented_image(image),
            mask=mask,
            keypoints=keypoints,
            bboxes=bboxes,
            category_id=range(len(bboxes)),
        )
        image = augmented["image"]
        not_implemented_image = augmented["not_implemented_image"]
        mask = augmented.get("mask", None)
        bboxes = augmented.get("bboxes", None)
        keypoints = augmented.get("keypoints", None)

        image_with_mask = draw_mask(image.copy(), mask) if mask is not None else not_implemented_image
        image_with_bboxes = draw_boxes(image.copy(), bboxes) if bboxes is not None else not_implemented_image
        image_with_keypoints = draw_keypoints(image.copy(), keypoints) if keypoints is not None else not_implemented_image

        return [
            (image_with_mask, "Mask"),
            (image_with_bboxes, "Boxes"),
            (image_with_keypoints, "Keypoints"),
        ]
    except Exception as e:
        raise e


def update_image_info(image):
    h, w = image.shape[:2]
    dtype = image.dtype
    max_, min_ = image.max(), image.min()
    return f"Image info:\n\t - shape: {h}x{w}\n\t - dtype: {dtype}\n\t - min/max: {min_}/{max_}"


def get_formatted_transform(transform_number):
    transform_name = transforms_keys[transform_number]
    transform = transforms_map[transform_name]
    return f"A.{transform.__name__}{get_formatted_signature(transform)}"


def get_formatted_transform_docs(transform_number):
    transform_name = transforms_keys[transform_number]
    transform = transforms_map[transform_name]
    return transform.__doc__.strip("\n")


with gr.Blocks() as demo:

    with gr.Row():
        with gr.Column():
            with gr.Group():
                select = gr.Dropdown(
                    label="Select a transformation",
                    choices=transforms_keys,
                    value=DEFAULT_TRANSFORM,
                    type="index",
                    interactive=True,
                )
                with gr.Accordion("Documentation", open=False):
                    docs = gr.TextArea(
                        get_formatted_transform_docs(
                            transforms_keys.index(DEFAULT_TRANSFORM)
                        ),
                        show_label=False,
                        interactive=False,
                    )
                code = gr.Code(
                    language="python",
                    value=get_formatted_transform(transforms_keys.index(DEFAULT_TRANSFORM)),
                    interactive=True,
                    lines=5,
                )
            button = gr.Button("Run")
            #info = gr.Text(interactive=False, label="Image info", value="")
        image = gr.Image(
            value=DEFAULT_IMAGE,
            type="numpy",
            height=500,
            width=300,
            sources=[],
        )
    with gr.Row():
        augmented_image = gr.Gallery(rows=1, columns=3)
        # augmented_image = gr.Image(type="numpy", height=300, width=300)

    #image.upload(fn=update_image_info, inputs=[image], outputs=[info])
    select.change(fn=get_formatted_transform, inputs=[select], outputs=[code])
    button.click(fn=update, inputs=[image, code], outputs=[augmented_image])


if __name__ == "__main__":
    demo.launch()