yolov5训练安全帽检测所需资源及脚本

import xml.etree.ElementTree as ET import pickle import os, cv2 from os import listdir, getcwd from os.path import join from tqdm import tqdm sets = ['train', 'test','val'] classes = ['helmet', 'head'] colors = {'helmet': (60, 60, 250), 'head': (250, 60, 60)} root_path = "helmet_source" dataSet_path = "%s/dataSet_path" % root_path image_path = "%s/images" % root_path Annotations_path = "%s/Annotations" % root_path ImageSets_path = "%s/ImageSets" % root_path labels_path = "%s/labels" % root_path def convert(size, box): dw = 1. / size[0] dh = 1. / size[1] x = (box[0] + box[1]) / 2.0 - 1 y = (box[2] + box[3]) / 2.0 - 1 w = box[1] - box[0] h = box[3] - box[2] x = x * dw w = w * dw y = y * dh h = h * dh # x_center = (box[0]+box[1])/2.0 # y_center = (box[2]+box[3])/2.0 # x = x_center / size[0] # y = y_center / size[1] # w = (box[1] - box[0]) / size[0] # h = (box[3] - box[2]) / size[1] return (x, y, w, h) def convert_annotation(image_id): in_file = open('%s/%s.xml' % (Annotations_path, image_id)) out_file = open('%s/%s.txt' % (labels_path, image_id), 'w') im = cv2.imread('%s/%s.png' % (image_path, image_id)) print('%s/%s.png' % (image_path, image_id)) tree = ET.parse(in_file) root = tree.getroot() size = root.find('size') w = int(size.find('width').text) h = int(size.find('height').text) for obj in root.iter('object'): difficult = obj.find('difficult').text cls = obj.find('name').text if cls not in classes or int(difficult) == 1: continue cls_id = classes.index(cls) xmlbox = obj.find('bndbox') xmin = xmlbox.find('xmin').text xmax = xmlbox.find('xmax').text ymin = xmlbox.find('ymin').text ymax = xmlbox.find('ymax').text b = (float(xmin), float(xmax), float(ymin), float(ymax)) b1, b2, b3, b4 = b # 标注越界修正 if b2 > w: b2 = w if b4 > h: b4 = h b = (b1, b2, b3, b4) bb = convert((w, h), b) out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n') print(cls, colors[cls], xmin, xmax, ymin, ymax) cv2.rectangle(im, (int(xmin), int(ymin)), (int(xmax), int(ymax)), colors[cls]) cv2.putText(im, cls, (int(xmin), int(ymin) - 3), cv2.FONT_HERSHEY_SIMPLEX, 0.5, colors[cls]) # cv2.imshow('result', im) # cv2.waitKey(0) cv2.imwrite('%s/%s_tag.png' % (image_path, image_id), im) wd = getcwd() print(wd) for image_set in sets: if not os.path.exists(labels_path): os.makedirs(labels_path) image_ids = open('%s/%s.txt' % (ImageSets_path, image_set)).read().strip().split() list_file = open('%s/%s.txt' % (dataSet_path, image_set), 'w') for image_id in image_ids: # print(image_id) list_file.write('%s/%s.png\n' % (image_path, image_id)) convert_annotation(image_id) list_file.close()