mini_dpvo/stream.py

import cv2
import numpy as np
from pathlib import Path
from itertools import chain
from multiprocessing import Queue


def image_stream(
    queue: Queue, imagedir: str, calib: str, stride: int, skip: int = 0
) -> None:
    """image generator"""

    calib = np.loadtxt(calib, delimiter=" ")
    fx, fy, cx, cy = calib[:4]

    K = np.eye(3)
    K[0, 0] = fx
    K[0, 2] = cx
    K[1, 1] = fy
    K[1, 2] = cy

    img_exts = ["*.png", "*.jpeg", "*.jpg"]
    image_list = sorted(chain.from_iterable(Path(imagedir).glob(e) for e in img_exts))[
        skip::stride
    ]

    for t, imfile in enumerate(image_list):
        image = cv2.imread(str(imfile))
        if len(calib) > 4:
            image = cv2.undistort(image, K, calib[4:])

        if 0:
            image = cv2.resize(image, None, fx=0.5, fy=0.5)
            intrinsics = np.array([fx / 2, fy / 2, cx / 2, cy / 2])

        else:
            intrinsics = np.array([fx, fy, cx, cy])

        h, w, _ = image.shape
        image = image[: h - h % 16, : w - w % 16]

        queue.put((t, image, intrinsics))

    queue.put((-1, image, intrinsics))


def video_stream(
    queue: Queue, imagedir: str, calib: str, stride: int, skip: int = 0
) -> None:
    """video generator"""

    calib = np.loadtxt(calib, delimiter=" ")
    fx, fy, cx, cy = calib[:4]

    K = np.eye(3)
    K[0, 0] = fx
    K[0, 2] = cx
    K[1, 1] = fy
    K[1, 2] = cy

    cap = cv2.VideoCapture(imagedir)

    t = 0

    for _ in range(skip):
        ret, image = cap.read()

    while True:
        # Capture frame-by-frame
        for _ in range(stride):
            ret, image = cap.read()
            # if frame is read correctly ret is True
            if not ret:
                break

        if not ret:
            break

        if len(calib) > 4:
            image = cv2.undistort(image, K, calib[4:])

        image = cv2.resize(image, None, fx=0.5, fy=0.5, interpolation=cv2.INTER_AREA)
        h, w, _ = image.shape
        image = image[: h - h % 16, : w - w % 16]

        intrinsics = np.array([fx * 0.5, fy * 0.5, cx * 0.5, cy * 0.5])
        queue.put((t, image, intrinsics))

        t += 1

    queue.put((-1, image, intrinsics))
    cap.release()