Create utils.py

utils.py

@@ -0,0 +1,183 @@
+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)