crosstyan/RapidPoseTriangulation
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Further small improvements.

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This commit is contained in:
Daniel
2024-12-06 18:15:08 +01:00
parent ee8b9bafb3
commit 23108dd594
4 changed files with 179 additions and 170 deletions
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13
scripts/utils_2d_pose_ort.py
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@ -148,7 +148,7 @@ class LetterBox:
resized_img = cv2.resize(
image,
(new_w, new_h),
interpolation=cv2.INTER_LINEAR,
interpolation=cv2.INTER_NEAREST,
)
# Optionally pad the image
@ -273,7 +273,7 @@ class BoxCrop:
resized_img = cv2.resize(
cropped_img,
(new_w, new_h),
interpolation=cv2.INTER_LINEAR,
interpolation=cv2.INTER_NEAREST,
)
# Optionally pad the image
@ -309,6 +309,10 @@ class RTMDet(BaseModel):
self.conf_threshold = conf_threshold
self.letterbox = LetterBox(self.target_size, fill_value=114)
min_area_scale = 0.025 * 0.025
img_area = self.target_size[0] * self.target_size[1]
self.min_area = img_area * min_area_scale
def preprocess(self, image: np.ndarray):
image = self.letterbox.resize_image(image)
tensor = np.asarray(image).astype(self.input_types[0], copy=False)
@ -326,6 +330,11 @@ class RTMDet(BaseModel):
keep = boxes[:, 4] > self.conf_threshold
boxes = boxes[keep]
# Drop boxes with too small area
areas = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
keep = areas >= self.min_area
boxes = boxes[keep]
paddings, scale, _ = self.letterbox.calc_params(image.shape)
boxes[:, 0] -= paddings[0]