prepare.py

prepare.py

import numpy as np
import cv2
import time
import onnxruntime

class YOLOv8:
    def __init__(self, path, conf_thres=0.3, iou_thres=0.3):
        self.conf_threshold = conf_thres
        self.iou_threshold = iou_thres

        # Initialize model
        self.initialize_model(path)

    def __call__(self, image):
        return self.detect_objects(image)

    def initialize_model(self, path):
        available_providers = onnxruntime.get_available_providers()

        providers = ['TensorrtExecutionProvider'] if 'TensorrtExecutionProvider' in available_providers else []

        if 'CUDAExecutionProvider' in available_providers:
            providers.append('CUDAExecutionProvider')
        else:
            providers.append('CPUExecutionProvider')
        self.session = onnxruntime.InferenceSession(path, providers=providers)
        self.get_input_details()
        self.get_output_details()

    def detect_objects(self, image):
        input_tensor = self.prepare_input(image)
        outputs = self.inference(input_tensor)
        self.boxes, self.scores, self.class_ids = self.process_output(outputs)
        #print(self.boxes)
        return self.boxes, self.scores, self.class_ids


    #"""
    def prepare_input(self, image):
        self.img_height, self.img_width = image.shape[:2]
        
        input_img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

        # Resize input image
        input_img = cv2.resize(input_img, (320, 320))  # Adjust the size according to your model's input shape

        # Scale input pixel values to 0 to 1
        input_img = input_img / 255
        #input_img = np.transpose(input_img, (0, 1,2))  # NHWC to NCHW
        input_tensor = np.expand_dims(input_img, axis=0).astype(np.float32)

        return {'serving_default_images:0': input_tensor}


    def inference(self, input_tensor):
        start = time.perf_counter()
        #outputs = self.session.run([self.output_names[0]], {self.input_names[0]: input_tensor})
        outputs = self.session.run([self.output_names[0]], input_tensor)
        return outputs

    def process_output(self, output):
        predictions = np.squeeze(output[0]).T
        #print(predictions)

        # Filter out object confidence scores below threshold
        scores = np.max(predictions[:, 4:], axis=1)
        predictions = predictions[scores > self.conf_threshold, :]
        scores = scores[scores > self.conf_threshold]

        if len(scores) == 0:
            return [], [], []

        # Get the class with the highest confidence
        class_ids = np.argmax(predictions[:, 4:], axis=1)

        # Get bounding boxes for each object
        boxes = self.extract_boxes(predictions)

        # Apply non-maxima suppression
        indices = multiclass_nms(boxes, scores, class_ids, self.iou_threshold)

        return boxes[indices], scores[indices], class_ids[indices]

    def extract_boxes(self, predictions):
        # Extract boxes from predictions
        boxes = predictions[:, :4]

        # Scale boxes to original image dimensions
        boxes = self.rescale_boxes(boxes)

        # Convert boxes to xyxy format
        boxes = xywh2xyxy(boxes)

        return boxes

    def rescale_boxes(self, boxes):
        # Rescale boxes to original image dimensions
        input_shape = np.array([self.input_width, self.input_height, self.input_width, self.input_height])
        boxes = np.divide(boxes, input_shape, dtype=np.float32)
        boxes *= np.array([self.img_width, self.img_height, self.img_width, self.img_height])
        return boxes

    def draw_detections(self, image, draw_scores=True, mask_alpha=0.4):
        return draw_detections(image, self.boxes, self.scores, self.class_ids, mask_alpha)

    def get_input_details(self):
        model_inputs = self.session.get_inputs()
        self.input_names = [model_inputs[i].name for i in range(len(model_inputs))]
        self.input_shape = model_inputs[0].shape
        self.input_height = self.input_shape[2]
        self.input_width = self.input_shape[3]

    def get_output_details(self):
        model_outputs = self.session.get_outputs()
        #print(model_outputs)
        self.output_names = [model_outputs[i].name for i in range(len(model_outputs))]

class_names = ['cars', 'light']

# 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, box, color=(0, 0, 255), thickness=2):
    x1, y1, x2, y2 = box.astype(int)
    return cv2.rectangle(image, (x1, y1), (x2, y2), color, thickness)


def draw_text(image, text, box, color=(0, 0, 255), font_size=0.001, text_thickness=2):
    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)