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.
