#!/usr/bin/env python # -*- coding: UTF-8 -*- ''' @Project ：AutoControlSystem-master @File ：utils.py @IDE ：PyCharm @Author ：hjw @Date ：2024/8/29 15:07 ''' import numpy as np import cv2 import psutil from psutil._common import bytes2human def get_disk_space(path='C:'): usage = psutil.disk_usage(path) space_free = bytes2human(usage.free) # space_total = bytes2human(usage.total) # space_used = bytes2human(usage.used) # space_free = bytes2human(usage.free) # space_used_percent = bytes2human(usage.percent) space_free = float(space_free[:-1]) return space_free def find_position(Depth_Z, RegionalArea, RegionalArea_Threshold, first_depth=True): if first_depth == True: sorted_id = sorted(range(len(Depth_Z)), key=lambda k: Depth_Z[k], reverse=False) Depth_Z1 = [Depth_Z[i] for i in sorted_id] RegionalArea1 = [RegionalArea[i] for i in sorted_id] for i in range(len(Depth_Z1)): if RegionalArea1[i] > RegionalArea_Threshold: return sorted_id[i] else: sorted_id = sorted(range(len(RegionalArea)), key=lambda k: RegionalArea[k], reverse=True) Depth_Z1 = [Depth_Z[i] for i in sorted_id] RegionalArea1 = [RegionalArea[i] for i in sorted_id] for i in range(len(Depth_Z1)): if RegionalArea1[i] > RegionalArea_Threshold: return sorted_id[i] class_names = ['box', 'other'] # 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 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 sigmoid(x): return 1 / (1 + np.exp(-x)) def draw_detections(image, boxes, scores, class_ids, mask_alpha=0.3, mask_maps=None): img_height, img_width = image.shape[:2] size = min([img_height, img_width]) * 0.0006 text_thickness = int(min([img_height, img_width]) * 0.001) mask_img = draw_masks(image, boxes, class_ids, mask_alpha, mask_maps) # Draw bounding boxes and labels of detections for box, score, class_id in zip(boxes, scores, class_ids): color = colors[class_id] x1, y1, x2, y2 = box.astype(int) # Draw rectangle cv2.rectangle(mask_img, (x1, y1), (x2, y2), color, 2) label = class_names[class_id] caption = f'{label} {int(score * 100)}%' (tw, th), _ = cv2.getTextSize(text=caption, fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=size, thickness=text_thickness) th = int(th * 1.2) cv2.rectangle(mask_img, (x1, y1), (x1 + tw, y1 - th), color, -1) cv2.putText(mask_img, caption, (x1, y1), cv2.FONT_HERSHEY_SIMPLEX, size, (255, 255, 255), text_thickness, cv2.LINE_AA) return mask_img def draw_masks(image, boxes, class_ids, mask_alpha=0.3, mask_maps=None): mask_img = image.copy() # Draw bounding boxes and labels of detections for i, (box, class_id) in enumerate(zip(boxes, class_ids)): color = colors[class_id] x1, y1, x2, y2 = box.astype(int) # Draw fill mask image if mask_maps is None: cv2.rectangle(mask_img, (x1, y1), (x2, y2), color, -1) else: crop_mask = mask_maps[i][y1:y2, x1:x2, np.newaxis] crop_mask_img = mask_img[y1:y2, x1:x2] crop_mask_img = crop_mask_img * (1 - crop_mask) + crop_mask * color mask_img[y1:y2, x1:x2] = crop_mask_img return cv2.addWeighted(mask_img, mask_alpha, image, 1 - mask_alpha, 0) def draw_comparison(img1, img2, name1, name2, fontsize=2.6, text_thickness=3): (tw, th), _ = cv2.getTextSize(text=name1, fontFace=cv2.FONT_HERSHEY_DUPLEX, fontScale=fontsize, thickness=text_thickness) x1 = img1.shape[1] // 3 y1 = th offset = th // 5 cv2.rectangle(img1, (x1 - offset * 2, y1 + offset), (x1 + tw + offset * 2, y1 - th - offset), (0, 115, 255), -1) cv2.putText(img1, name1, (x1, y1), cv2.FONT_HERSHEY_DUPLEX, fontsize, (255, 255, 255), text_thickness) (tw, th), _ = cv2.getTextSize(text=name2, fontFace=cv2.FONT_HERSHEY_DUPLEX, fontScale=fontsize, thickness=text_thickness) x1 = img2.shape[1] // 3 y1 = th offset = th // 5 cv2.rectangle(img2, (x1 - offset * 2, y1 + offset), (x1 + tw + offset * 2, y1 - th - offset), (94, 23, 235), -1) cv2.putText(img2, name2, (x1, y1), cv2.FONT_HERSHEY_DUPLEX, fontsize, (255, 255, 255), text_thickness) combined_img = cv2.hconcat([img1, img2]) if combined_img.shape[1] > 3840: combined_img = cv2.resize(combined_img, (3840, 2160)) return combined_img