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Slicer: highlight & mask docstring

This commit is contained in:
Linas Kondrackis 2024-05-13 16:51:59 +03:00
parent 93398108ee
commit 85e4c0ec65
2 changed files with 40 additions and 22 deletions
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4
docs/how_to/detect_small_objects.md
View File

@ -271,7 +271,7 @@ objects within each, and aggregating the results.
=== "Inference"
```{ .py hl_lines="6 16 19" }
```{ .py hl_lines="6 16 19-20" }
import cv2
import numpy as np
import supervision as sv
@ -298,7 +298,7 @@ objects within each, and aggregating the results.
=== "Ultralytics"
```{ .py hl_lines="6 16 19" }
```{ .py hl_lines="6 16 19-20" }
import cv2
import numpy as np
import supervision as sv

58
supervision/detection/utils.py
View File

@ -55,7 +55,8 @@ def box_iou_batch(boxes_true: np.ndarray, boxes_detection: np.ndarray) -> np.nda
top_left = np.maximum(boxes_true[:, None, :2], boxes_detection[:, :2])
bottom_right = np.minimum(boxes_true[:, None, 2:], boxes_detection[:, 2:])
area_inter = np.prod(np.clip(bottom_right - top_left, a_min=0, a_max=None), 2)
area_inter = np.prod(
np.clip(bottom_right - top_left, a_min=0, a_max=None), 2)
return area_inter / (area_true[:, None] + area_detection - area_inter)
@ -80,7 +81,8 @@ def _mask_iou_batch_split(
masks_true_area = masks_true.sum(axis=(1, 2))
masks_detection_area = masks_detection.sum(axis=(1, 2))
union_area = masks_true_area[:, None] + masks_detection_area - intersection_area
union_area = masks_true_area[:, None] + \
masks_detection_area - intersection_area
return np.divide(
intersection_area,
@ -131,7 +133,8 @@ def mask_iou_batch(
1,
)
for i in range(0, masks_true.shape[0], step):
ious.append(_mask_iou_batch_split(masks_true[i : i + step], masks_detection))
ious.append(_mask_iou_batch_split(
masks_true[i: i + step], masks_detection))
return np.vstack(ious)
@ -161,7 +164,8 @@ def resize_masks(masks: np.ndarray, max_dimension: int = 640) -> np.ndarray:
resized_masks = masks[:, yv, xv]
resized_masks = resized_masks.reshape(masks.shape[0], new_height, new_width)
resized_masks = resized_masks.reshape(
masks.shape[0], new_height, new_width)
return resized_masks
@ -214,8 +218,9 @@ def mask_non_max_suppression(
keep = np.ones(rows, dtype=bool)
for i in range(rows):
if keep[i]:
condition = (ious[i] > iou_threshold) & (categories[i] == categories)
keep[i + 1 :] = np.where(condition[i + 1 :], False, keep[i + 1 :])
condition = (ious[i] > iou_threshold) & (
categories[i] == categories)
keep[i + 1:] = np.where(condition[i + 1:], False, keep[i + 1:])
return keep[sort_index.argsort()]
@ -447,7 +452,8 @@ def approximate_polygon(
approximated_points = polygon
while True:
epsilon += epsilon_step
new_approximated_points = cv2.approxPolyDP(polygon, epsilon, closed=True)
new_approximated_points = cv2.approxPolyDP(
polygon, epsilon, closed=True)
if len(new_approximated_points) > target_points:
approximated_points = new_approximated_points
else:
@ -476,7 +482,8 @@ def extract_ultralytics_masks(yolov8_results) -> Optional[np.ndarray]:
)
top, left = int(pad[1]), int(pad[0])
bottom, right = int(inference_shape[0] - pad[1]), int(inference_shape[1] - pad[0])
bottom, right = int(
inference_shape[0] - pad[1]), int(inference_shape[1] - pad[0])
mask_maps = []
masks = yolov8_results.masks.data.cpu().numpy()
@ -543,7 +550,8 @@ def process_roboflow_result(
polygon = np.array(
[[point["x"], point["y"]] for point in prediction["points"]], dtype=int
)
mask = polygon_to_mask(polygon, resolution_wh=(image_width, image_height))
mask = polygon_to_mask(
polygon, resolution_wh=(image_width, image_height))
xyxy.append([x_min, y_min, x_max, y_max])
class_id.append(prediction["class_id"])
class_name.append(prediction["class"])
@ -554,10 +562,12 @@ def process_roboflow_result(
xyxy = np.array(xyxy) if len(xyxy) > 0 else np.empty((0, 4))
confidence = np.array(confidence) if len(confidence) > 0 else np.empty(0)
class_id = np.array(class_id).astype(int) if len(class_id) > 0 else np.empty(0)
class_id = np.array(class_id).astype(
int) if len(class_id) > 0 else np.empty(0)
class_name = np.array(class_name) if len(class_name) > 0 else np.empty(0)
masks = np.array(masks, dtype=bool) if len(masks) > 0 else None
tracker_id = np.array(tracker_ids).astype(int) if len(tracker_ids) > 0 else None
tracker_id = np.array(tracker_ids).astype(
int) if len(tracker_ids) > 0 else None
data = {CLASS_NAME_DATA_FIELD: class_name}
return xyxy, confidence, class_id, masks, tracker_id, data
@ -601,7 +611,9 @@ def move_masks(
Offset the masks in an array by the specified (x, y) amount.
Args:
masks (np.ndarray): array of bools
masks (np.ndarray): A 3D array of binary masks corresponding to the predictions.
Shape: `(N, H, W)`, where N is the number of predictions, and H, W are the
dimensions of each mask.
offset (np.ndarray): An array of shape `(2,)` containing non-negative int values
`[dx, dy]`.
resolution_wh (Tuple[int, int]): The width and height of the desired mask
@ -612,13 +624,15 @@ def move_masks(
"""
if offset[0] < 0 or offset[1] < 0:
raise ValueError(f"Offset values must be non-negative integers. Got: {offset}")
raise ValueError(
f"Offset values must be non-negative integers. Got: {offset}")
mask_array = np.full((masks.shape[0], resolution_wh[1], resolution_wh[0]), False)
mask_array = np.full(
(masks.shape[0], resolution_wh[1], resolution_wh[0]), False)
mask_array[
:,
offset[1] : masks.shape[1] + offset[1],
offset[0] : masks.shape[2] + offset[0],
offset[1]: masks.shape[1] + offset[1],
offset[0]: masks.shape[2] + offset[0],
] = masks
return mask_array
@ -682,8 +696,10 @@ def calculate_masks_centroids(masks: np.ndarray) -> np.ndarray:
return np.tensordot(masks, indices, axes=axis)
aggregation_axis = ([1, 2], [0, 1])
centroid_x = sum_over_mask(horizontal_indices, aggregation_axis) / total_pixels
centroid_y = sum_over_mask(vertical_indices, aggregation_axis) / total_pixels
centroid_x = sum_over_mask(
horizontal_indices, aggregation_axis) / total_pixels
centroid_y = sum_over_mask(
vertical_indices, aggregation_axis) / total_pixels
return np.column_stack((centroid_x, centroid_y)).astype(int)
@ -761,7 +777,8 @@ def merge_data(
elif ndim > 1:
merged_data[key] = np.vstack(merged_data[key])
else:
raise ValueError(f"Unexpected array dimension for key '{key}'.")
raise ValueError(
f"Unexpected array dimension for key '{key}'.")
else:
raise ValueError(
f"Inconsistent data types for key '{key}'. Only np.ndarray and list "
@ -806,6 +823,7 @@ def get_data_item(
else:
raise TypeError(f"Unsupported index type: {type(index)}")
else:
raise TypeError(f"Unsupported data type for key '{key}': {type(value)}")
raise TypeError(
f"Unsupported data type for key '{key}': {type(value)}")
return subset_data