gradio-webrtc/demo/object_detection/utils.py

import cv2
import numpy as np

class_names = [
    "person",
    "bicycle",
    "car",
    "motorcycle",
    "airplane",
    "bus",
    "train",
    "truck",
    "boat",
    "traffic light",
    "fire hydrant",
    "stop sign",
    "parking meter",
    "bench",
    "bird",
    "cat",
    "dog",
    "horse",
    "sheep",
    "cow",
    "elephant",
    "bear",
    "zebra",
    "giraffe",
    "backpack",
    "umbrella",
    "handbag",
    "tie",
    "suitcase",
    "frisbee",
    "skis",
    "snowboard",
    "sports ball",
    "kite",
    "baseball bat",
    "baseball glove",
    "skateboard",
    "surfboard",
    "tennis racket",
    "bottle",
    "wine glass",
    "cup",
    "fork",
    "knife",
    "spoon",
    "bowl",
    "banana",
    "apple",
    "sandwich",
    "orange",
    "broccoli",
    "carrot",
    "hot dog",
    "pizza",
    "donut",
    "cake",
    "chair",
    "couch",
    "potted plant",
    "bed",
    "dining table",
    "toilet",
    "tv",
    "laptop",
    "mouse",
    "remote",
    "keyboard",
    "cell phone",
    "microwave",
    "oven",
    "toaster",
    "sink",
    "refrigerator",
    "book",
    "clock",
    "vase",
    "scissors",
    "teddy bear",
    "hair drier",
    "toothbrush",
]

# 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)  # type: ignore

        label = class_names[class_id]
        caption = f"{label} {int(score * 100)}%"
        draw_text(det_img, caption, box, color, font_size, text_thickness)  # type: ignore

    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)  # type: ignore

    return cv2.addWeighted(mask_img, mask_alpha, image, 1 - mask_alpha, 0)