def draw_box(
out_img: np.ndarray,
box: Box,
labels: Iterable[Tuple[str, Point]],
color: Color = Color.red(),
line_thickness: int = 2,
) -> np.ndarray:
cv.rectangle(
img=out_img,
pt1=box.top_left,
pt2=box.bottom_right,
color=color.to_bgr(),
thickness=line_thickness,
)
for text, translation in labels:
text_loc: Point = translate_point(
Point(box.top_left_x, box.bottom_right_y), translation)
cv.putText(
img=out_img,
text=text,
org=text_loc,
fontFace=cv.FONT_HERSHEY_SIMPLEX,
fontScale=0.5,
color=Color.orange().to_bgr(),
thickness=2,
)
return out_img
def detect(
self,
img: np.ndarray,
out_img: np.ndarray = None,
color: Color = Color.yellow(),
line_thickness: int = 2,
) -> np.ndarray:
"""
Detect self.template in img and draw a box around it.
:param img: The image to detect the template in
:param out_img: The image to draw on (should be at least as large as img)
:param color: The color of the bounding box
:param line_thickness: The thickness of he bounding box line
:return: out_img
"""
# TODO: Make this scale invariant.
# see https://www.pyimagesearch.com/2015/01/26/multi-scale-template-matching-using-python-opencv/
if out_img is None:
out_img = img.copy()
else:
assert all(
img_dim <= out_img_dim
for img_dim, out_img_dim in zip(img.shape, out_img.shape))
similarity_map: np.ndarray = self._compute_similarity_map(img)
match_xy_indices: Iterable[Tuple[
int, int]] = self._get_match_xy_indices(similarity_map)
# For each match, draw the bounding box
for x, y in match_xy_indices:
top_left = x, y
bottom_right = x + self.temp_w, y + self.temp_h
cv.rectangle(
img=out_img,
pt1=top_left,
pt2=bottom_right,
color=color.to_bgr(),
thickness=line_thickness,
)
return out_img
