Delete utils.py

utils.py

@@ -1,183 +0,0 @@
-import numpy as np
-import cv2
-
-class_names = [
-    "A",
-    "B",
-    "C",
-    "D",
-    "E",
-    "F",
-    "G",
-    "H",
-    "I",
-    "J",
-    "K",
-    "L",
-    "M",
-    "N",
-    "O",
-    "P",
-    "Q",
-    "R",
-    "S",
-    "T",
-    "U",
-    "V",
-    "W",
-    "X",
-    "Y",
-    "Z",
-]
-
-# Create a list of colors for each class where each color is a tuple of 3 integer values
-rng = np.random.default_rng(3)
-colors = rng.uniform(0, 255, size=(len(class_names), 3))
-
-
-def nms(boxes, scores, iou_threshold):
-    # Sort by score
-    sorted_indices = np.argsort(scores)[::-1]
-
-    keep_boxes = []
-    while sorted_indices.size > 0:
-        # Pick the last box
-        box_id = sorted_indices[0]
-        keep_boxes.append(box_id)
-
-        # Compute IoU of the picked box with the rest
-        ious = compute_iou(boxes[box_id, :], boxes[sorted_indices[1:], :])
-
-        # Remove boxes with IoU over the threshold
-        keep_indices = np.where(ious < iou_threshold)[0]
-
-        # print(keep_indices.shape, sorted_indices.shape)
-        sorted_indices = sorted_indices[keep_indices + 1]
-
-    return keep_boxes
-
-
-def multiclass_nms(boxes, scores, class_ids, iou_threshold):
-    unique_class_ids = np.unique(class_ids)
-
-    keep_boxes = []
-    for class_id in unique_class_ids:
-        class_indices = np.where(class_ids == class_id)[0]
-        class_boxes = boxes[class_indices, :]
-        class_scores = scores[class_indices]
-
-        class_keep_boxes = nms(class_boxes, class_scores, iou_threshold)
-        keep_boxes.extend(class_indices[class_keep_boxes])
-
-    return keep_boxes
-
-
-def compute_iou(box, boxes):
-    # Compute xmin, ymin, xmax, ymax for both boxes
-    xmin = np.maximum(box[0], boxes[:, 0])
-    ymin = np.maximum(box[1], boxes[:, 1])
-    xmax = np.minimum(box[2], boxes[:, 2])
-    ymax = np.minimum(box[3], boxes[:, 3])
-
-    # Compute intersection area
-    intersection_area = np.maximum(0, xmax - xmin) * np.maximum(0, ymax - ymin)
-
-    # Compute union area
-    box_area = (box[2] - box[0]) * (box[3] - box[1])
-    boxes_area = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
-    union_area = box_area + boxes_area - intersection_area
-
-    # Compute IoU
-    iou = intersection_area / union_area
-
-    return iou
-
-
-def xywh2xyxy(x):
-    # Convert bounding box (x, y, w, h) to bounding box (x1, y1, x2, y2)
-    y = np.copy(x)
-    y[..., 0] = x[..., 0] - x[..., 2] / 2
-    y[..., 1] = x[..., 1] - x[..., 3] / 2
-    y[..., 2] = x[..., 0] + x[..., 2] / 2
-    y[..., 3] = x[..., 1] + x[..., 3] / 2
-    return y
-
-
-def draw_detections(image, boxes, scores, class_ids, mask_alpha=0.3):
-    det_img = image.copy()
-
-    img_height, img_width = image.shape[:2]
-    font_size = min([img_height, img_width]) * 0.0006
-    text_thickness = int(min([img_height, img_width]) * 0.001)
-
-    #det_img = draw_masks(det_img, boxes, class_ids, mask_alpha)
-
-    # Draw bounding boxes and labels of detections
-    for class_id, box, score in zip(class_ids, boxes, scores):
-        color = colors[class_id]
-
-        draw_box(det_img, box, color)
-
-        label = class_names[class_id]
-        caption = f"{label} {int(score * 100)}%"
-        draw_text(det_img, caption, box, color, font_size, text_thickness)
-
-    return det_img
-
-
-def draw_box(
-    image: np.ndarray,
-    box: np.ndarray,
-    color: tuple[int, int, int] = (0, 0, 255),
-    thickness: int = 2,
-) -> np.ndarray:
-    x1, y1, x2, y2 = box.astype(int)
-    return cv2.rectangle(image, (x1, y1), (x2, y2), color, thickness)
-
-
-def draw_text(
-    image: np.ndarray,
-    text: str,
-    box: np.ndarray,
-    color: tuple[int, int, int] = (0, 0, 255),
-    font_size: float = 0.001,
-    text_thickness: int = 2,
-) -> np.ndarray:
-    x1, y1, x2, y2 = box.astype(int)
-    (tw, th), _ = cv2.getTextSize(
-        text=text,
-        fontFace=cv2.FONT_HERSHEY_SIMPLEX,
-        fontScale=font_size,
-        thickness=text_thickness,
-    )
-    th = int(th * 1.2)
-
-    cv2.rectangle(image, (x1, y1), (x1 + tw, y1 - th), color, -1)
-
-    return cv2.putText(
-        image,
-        text,
-        (x1, y1),
-        cv2.FONT_HERSHEY_SIMPLEX,
-        font_size,
-        (255, 255, 255),
-        text_thickness,
-        cv2.LINE_AA,
-    )
-
-
-def draw_masks(
-    image: np.ndarray, boxes: np.ndarray, classes: np.ndarray, mask_alpha: float = 0.3
-) -> np.ndarray:
-    mask_img = image.copy()
-
-    # Draw bounding boxes and labels of detections
-    for box, class_id in zip(boxes, classes):
-        color = colors[class_id]
-
-        x1, y1, x2, y2 = box.astype(int)
-
-        # Draw fill rectangle in mask image
-        cv2.rectangle(mask_img, (x1, y1), (x2, y2), color, -1)
-
-    return cv2.addWeighted(mask_img, mask_alpha, image, 1 - mask_alpha, 0)