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Ruff Docstring formatting (#15793)

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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
This commit is contained in:
Glenn Jocher 2024-08-25 04:27:55 +08:00 • committed by GitHub
parent d27664216b
commit 776ca86369
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60 changed files with 241 additions and 309 deletions
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1
examples/YOLOv8-Action-Recognition/action_recognition.py
View file

@ -203,7 +203,6 @@ class HuggingFaceVideoClassifier:
Returns:
torch.Tensor: The model's output.
"""
input_ids = self.processor(text=self.labels, return_tensors="pt", padding=True)["input_ids"].to(self.device)
inputs = {"pixel_values": sequences, "input_ids": input_ids}

2
examples/YOLOv8-ONNXRuntime/main.py
View file

@ -48,7 +48,6 @@ class YOLOv8:
Returns:
None
"""
# Extract the coordinates of the bounding box
x1, y1, w, h = box
@ -118,7 +117,6 @@ class YOLOv8:
Returns:
numpy.ndarray: The input image with detections drawn on it.
"""
# Transpose and squeeze the output to match the expected shape
outputs = np.transpose(np.squeeze(output[0]))

7
examples/YOLOv8-OpenCV-int8-tflite-Python/main.py
View file

@ -30,7 +30,6 @@ class LetterBox:
def __call__(self, labels=None, image=None):
"""Return updated labels and image with added border."""
if labels is None:
labels = {}
img = labels.get("img") if image is None else image
@ -79,7 +78,6 @@ class LetterBox:
def _update_labels(self, labels, ratio, padw, padh):
"""Update labels."""
labels["instances"].convert_bbox(format="xyxy")
labels["instances"].denormalize(*labels["img"].shape[:2][::-1])
labels["instances"].scale(*ratio)
@ -100,7 +98,6 @@ class Yolov8TFLite:
confidence_thres: Confidence threshold for filtering detections.
iou_thres: IoU (Intersection over Union) threshold for non-maximum suppression.
"""
self.tflite_model = tflite_model
self.input_image = input_image
self.confidence_thres = confidence_thres
@ -125,7 +122,6 @@ class Yolov8TFLite:
Returns:
None
"""
# Extract the coordinates of the bounding box
x1, y1, w, h = box
@ -164,7 +160,6 @@ class Yolov8TFLite:
Returns:
image_data: Preprocessed image data ready for inference.
"""
# Read the input image using OpenCV
self.img = cv2.imread(self.input_image)
@ -193,7 +188,6 @@ class Yolov8TFLite:
Returns:
numpy.ndarray: The input image with detections drawn on it.
"""
boxes = []
scores = []
class_ids = []
@ -238,7 +232,6 @@ class Yolov8TFLite:
Returns:
output_img: The output image with drawn detections.
"""
# Create an interpreter for the TFLite model
interpreter = tflite.Interpreter(model_path=self.tflite_model)
self.model = interpreter

2
examples/YOLOv8-Region-Counter/yolov8_region_counter.py
View file

@ -40,7 +40,7 @@ def mouse_callback(event, x, y, flags, param):
"""
Handles mouse events for region manipulation.
Parameters:
Args:
event (int): The mouse event type (e.g., cv2.EVENT_LBUTTONDOWN).
x (int): The x-coordinate of the mouse pointer.
y (int): The y-coordinate of the mouse pointer.

22
examples/YOLOv8-Segmentation-ONNXRuntime-Python/main.py
View file

@ -21,7 +21,6 @@ class YOLOv8Seg:
Args:
onnx_model (str): Path to the ONNX model.
"""
# Build Ort session
self.session = ort.InferenceSession(
onnx_model,
@ -57,7 +56,6 @@ class YOLOv8Seg:
segments (List): list of segments.
masks (np.ndarray): [N, H, W], output masks.
"""
# Pre-process
im, ratio, (pad_w, pad_h) = self.preprocess(im0)
@ -90,7 +88,6 @@ class YOLOv8Seg:
pad_w (float): width padding in letterbox.
pad_h (float): height padding in letterbox.
"""
# Resize and pad input image using letterbox() (Borrowed from Ultralytics)
shape = img.shape[:2] # original image shape
new_shape = (self.model_height, self.model_width)
@ -130,7 +127,7 @@ class YOLOv8Seg:
"""
x, protos = preds[0], preds[1] # Two outputs: predictions and protos
# Transpose the first output: (Batch_size, xywh_conf_cls_nm, Num_anchors) -> (Batch_size, Num_anchors, xywh_conf_cls_nm)
# Transpose dim 1: (Batch_size, xywh_conf_cls_nm, Num_anchors) -> (Batch_size, Num_anchors, xywh_conf_cls_nm)
x = np.einsum("bcn->bnc", x)
# Predictions filtering by conf-threshold
@ -169,8 +166,8 @@ class YOLOv8Seg:
@staticmethod
def masks2segments(masks):
"""
It takes a list of masks(n,h,w) and returns a list of segments(n,xy) (Borrowed from
https://github.com/ultralytics/ultralytics/blob/465df3024f44fa97d4fad9986530d5a13cdabdca/ultralytics/utils/ops.py#L750)
Takes a list of masks(n,h,w) and returns a list of segments(n,xy), from
https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/ops.py.
Args:
masks (numpy.ndarray): the output of the model, which is a tensor of shape (batch_size, 160, 160).
@ -191,8 +188,8 @@ class YOLOv8Seg:
@staticmethod
def crop_mask(masks, boxes):
"""
It takes a mask and a bounding box, and returns a mask that is cropped to the bounding box. (Borrowed from
https://github.com/ultralytics/ultralytics/blob/465df3024f44fa97d4fad9986530d5a13cdabdca/ultralytics/utils/ops.py#L599)
Takes a mask and a bounding box, and returns a mask that is cropped to the bounding box, from
https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/ops.py.
Args:
masks (Numpy.ndarray): [n, h, w] tensor of masks.
@ -209,8 +206,8 @@ class YOLOv8Seg:
def process_mask(self, protos, masks_in, bboxes, im0_shape):
"""
Takes the output of the mask head, and applies the mask to the bounding boxes. This produces masks of higher quality
but is slower. (Borrowed from https://github.com/ultralytics/ultralytics/blob/465df3024f44fa97d4fad9986530d5a13cdabdca/ultralytics/utils/ops.py#L618)
Takes the output of the mask head, and applies the mask to the bounding boxes. This produces masks of higher
quality but is slower, from https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/ops.py.
Args:
protos (numpy.ndarray): [mask_dim, mask_h, mask_w].
@ -232,8 +229,8 @@ class YOLOv8Seg:
@staticmethod
def scale_mask(masks, im0_shape, ratio_pad=None):
"""
Takes a mask, and resizes it to the original image size. (Borrowed from
https://github.com/ultralytics/ultralytics/blob/465df3024f44fa97d4fad9986530d5a13cdabdca/ultralytics/utils/ops.py#L305)
Takes a mask, and resizes it to the original image size, from
https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/ops.py.
Args:
masks (np.ndarray): resized and padded masks/images, [h, w, num]/[h, w, 3].
@ -277,7 +274,6 @@ class YOLOv8Seg:
Returns:
None
"""
# Draw rectangles and polygons
im_canvas = im.copy()
for (*box, conf, cls_), segment in zip(bboxes, segments):