YOLOv8-16bit/ultralytics/tests/data/dataloader/yolosegment.py

import cv2
import numpy as np
import torch
from omegaconf import OmegaConf

from ultralytics.yolo.data import build_dataloader


class Colors:
    # Ultralytics color palette https://ultralytics.com/
    def __init__(self):
        # hex = matplotlib.colors.TABLEAU_COLORS.values()
        hexs = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB',
                '2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7')
        self.palette = [self.hex2rgb(f'#{c}') for c in hexs]
        self.n = len(self.palette)

    def __call__(self, i, bgr=False):
        c = self.palette[int(i) % self.n]
        return (c[2], c[1], c[0]) if bgr else c

    @staticmethod
    def hex2rgb(h):  # rgb order (PIL)
        return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))


colors = Colors()  # create instance for 'from utils.plots import colors'


def plot_one_box(x, img, color=None, label=None, line_thickness=None):
    import random

    # Plots one bounding box on image img
    tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1  # line/font thickness
    color = color or [random.randint(0, 255) for _ in range(3)]
    c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
    cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
    if label:
        tf = max(tl - 1, 1)  # font thickness
        t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
        c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
        cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA)  # filled
        cv2.putText(
            img,
            label,
            (c1[0], c1[1] - 2),
            0,
            tl / 3,
            [225, 255, 255],
            thickness=tf,
            lineType=cv2.LINE_AA,
        )


with open("ultralytics/tests/data/dataloader/hyp_test.yaml") as f:
    hyp = OmegaConf.load(f)

dataloader, dataset = build_dataloader(
    img_path="/d/dataset/COCO/coco128-seg/images",
    img_size=640,
    label_path=None,
    cache=False,
    hyp=hyp,
    augment=False,
    prefix="",
    rect=False,
    batch_size=4,
    stride=32,
    pad=0.5,
    use_segments=True,
    use_keypoints=False,
)

for d in dataloader:
    idx = 1  # show which image inside one batch
    img = d["img"][idx].numpy()
    img = np.ascontiguousarray(img.transpose(1, 2, 0))
    ih, iw = img.shape[:2]
    # print(img.shape)
    bidx = d["batch_idx"]
    cls = d["cls"][bidx == idx].numpy()
    bboxes = d["bboxes"][bidx == idx].numpy()
    masks = d["masks"][idx]
    print(bboxes.shape)
    bboxes[:, [0, 2]] *= iw
    bboxes[:, [1, 3]] *= ih
    nl = len(cls)

    index = torch.arange(nl).view(nl, 1, 1) + 1
    masks = masks.repeat(nl, 1, 1)
    # print(masks.shape, index.shape)
    masks = torch.where(masks == index, 1, 0)
    masks = masks.numpy().astype(np.uint8)
    print(masks.shape)
    # keypoints = d["keypoints"]

    for i, b in enumerate(bboxes):
        x, y, w, h = b
        x1 = x - w / 2
        x2 = x + w / 2
        y1 = y - h / 2
        y2 = y + h / 2
        c = int(cls[i][0])
        # print(x1, y1, x2, y2)
        plot_one_box([int(x1), int(y1), int(x2), int(y2)], img, label=f"{c}", color=colors(c))
        mask = masks[i]
        mask = cv2.resize(mask, (iw, ih))
        mask = mask.astype(bool)
        img[mask] = img[mask] * 0.5 + np.array(colors(c)) * 0.5
    cv2.imshow("p", img)
    if cv2.waitKey(0) == ord("q"):
        break