# Utilities import cv2 import numpy as np from PIL import Image def load_img(img_path, input_shape): # Loading image image = cv2.imread(img_path) image_height, image_width = image.shape[:2] Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) input_height, input_width = input_shape[2:] image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) resized = cv2.resize(image_rgb, (input_width, input_height)) # image resized as req by onnx model input_image = resized / 255.0 # scaling image input_image = input_image.transpose(2,0,1) # dim rearranged as req by onnx (batch_size, channel, height, width) input_tensor = input_image[np.newaxis, :, :, :].astype(np.float32) return image, image_height, image_width, input_height, input_width, input_tensor 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 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