Trying to run face detection example on oak-1 max

Sure!

The following program is from my side

from depthai_sdk import OakCamera
import cv2, numpy as np
from queue import Queue
from typing import Tuple

class Face:

    def __init__(self) -> None:
        pass

    def get_bbox(self, packet):
        for i in packet.detections:
            pt1, pt2 = i.bbox.denormalize(packet.frame.shape)
            print(f"img shape from me: {packet.frame.shape}")
            print(f"denormalized points from me: {pt1,pt2}")
            print(f"Points from me: {i.bbox}")
            print("________________________________________")
        # cv2.imshow("frame", packet.frame)

    def start(self):
        with OakCamera() as oak:
            color = oak.create_camera('color')
            nn = oak.create_nn('face-detection-retail-0004', color)
            oak.visualize([nn.out.main], fps=True)
            oak.callback(nn, callback=self.get_bbox)
            oak.start(blocking=True)

f = Face()
f.start()

I am currently printing the image shape, as well as the denormalized and normalized points in the callback function

I noticed the coordinates returned back were inaccurate when I tried to draw the bounding box myself in the callback function.

However, the bounding box from the visualizer look correct so I printed the same information from depthai_sdk/visualize/visualization_helper.py in the draw_stylized_bbox function.

def draw_stylized_bbox(img: np.ndarray, obj: VisBoundingBox) -> None:
    """
    Draw a stylized bounding box. The style is either passed as an argument or defined in the config.

    Args:
        img: Image to draw on.
        obj: Bounding box to draw.
    """
    pt1, pt2 = obj.bbox.denormalize(img.shape)

    print(f"image shape from visualizer helper: {img.shape}")
    print(f"denormalized points from visualizer helper {pt1, pt2}")
    print(f"Points from visualizer helper: {obj.bbox}")

    box_w = pt2[0] - pt1[0]
    box_h = pt2[1] - pt1[1]

    line_width = int(box_w * obj.config.detection.line_width) // 2
    line_height = int(box_h * obj.config.detection.line_height) // 2
    roundness = int(obj.config.detection.box_roundness)
    bbox_style = obj.bbox_style or obj.config.detection.bbox_style
    alpha = obj.config.detection.fill_transparency

    if bbox_style == BboxStyle.RECTANGLE:
        draw_bbox(img, pt1, pt2,
                  obj.color, obj.thickness, 0,
                  line_width=0, line_height=0, alpha=alpha)
    elif bbox_style == BboxStyle.CORNERS:
        draw_bbox(img, pt1, pt2,
                  obj.color, obj.thickness, 0,
                  line_width=line_width, line_height=line_height, alpha=alpha)
    elif bbox_style == BboxStyle.ROUNDED_RECTANGLE:
        draw_bbox(img, pt1, pt2,
                  obj.color, obj.thickness, roundness,
                  line_width=0, line_height=0, alpha=alpha)
    elif bbox_style == BboxStyle.ROUNDED_CORNERS:
        draw_bbox(img, pt1, pt2,
                  obj.color, obj.thickness, roundness,
                  line_width=line_width, line_height=line_height, alpha=alpha)

The following is the output:

As can be seen, the x coordinates are identical but the y coordinates are different. Depending on where the face is tracked, the discrepancy in the y values can be significant.