AI代码解释 defcv2ImgAddText(img,text,left,top,textColor=(0,255,0),textSize=25):# 图像从OpenCV格式转换成PIL格式if(isinstance(img,np.ndarray)):# 判断是否OpenCV图片类型 img=Image.fromarray(cv2.cvtColor(img,cv2.COLOR_BGR2RGB))draw=ImageDraw.Draw(img)fontText=ImageFont.truetype("Font/simhei.t...
import matplotlib.image as mpimg model_path = '/content/runs/detect/train/weights/best.pt' model = YOLOv10(model_path) results = model(source='/content/X-Ray-Baggage-3/test/images', conf=0.25,save=True) images = glob.glob('/content/runs/detect/predict/*.jpg') images_to_display = im...
setText(self.image_path) model = YOLO(self.weight_file_path) results = model(self.image_path) annotated_frame = results[0].plot() # 将图像数据转换为QImage格式 height, width, channel = annotated_frame.shape bytes_per_line = 3 * width qimage = QtGui.QImage(annotated_frame.data, width, ...
b2, b3, b4)bb = convert((w, h), b)out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')wd = getcwd()for image_set in sets:if not os.path.exists('data/labels/'):os.makedirs('data/labels/')image_ids = open('data/ImageSets/%s.txt' %...
image_id): in_file = open('Poker/VOC/Annotations/%s.xml' % image_id) out_file = open('Poker/VOC/Labels/%s.txt' % image_id, 'w') tree = ET.parse(in_file) root = tree.getroot() size = root.find('size') w = int(size.find('width').text) h = int(...
-- image:输入图像的路径; -- yolo:YOLO文件路径,脚本将加载所需的YOLO文件,以便在图像上执行对象检测; -- confidence:过滤弱检测的最小概率,默认值设置为0.5,但该值也可以随意设置; -- threshold:非最大值抑制阈值,默认值设置为0.3,可以在此处阅读有关非最大值抑制的更多信息。
python深色版本 import os import cv2 import numpy as np import matplotlib.pyplot as plt from matplotlib.patches import Rectangle # 数据集目录路径 data_dir = 'path/to/sign_language_gesture_dataset' train_image_dir = os.path.join(data_dir, 'images/train') train_label_dir = os.path.join(data...
label_path = os.path.join(data_path, 'train_label', image_ind +'.xml') root = ET.parse(label_path).getroot() objects_size = root.findall('size') image_width = int(objects_size[0].find('width').text) image_height = int(objects_size[0].find('height').text) ...
Speed: 4.7ms pre-process, 584.3ms inference, 14.8ms NMS per image at shape (1, 3, 640, 640) Results saved to runs/detect/exp 运行截图: 识别图片结果,生成目录位置为 runs/detect/exp 三、体验 内置AI 应用样例 香橙派 AIpro中预装了 Jupyter Lab 软件。Jupyter Lab 软件是一个基于 web ...
def convert_annotation(image_id, list_file):print(image_id)in_file = open('dataset/Annotations/%s.xml'% image_id)tree = ET.parse(in_file)root = tree.getroot() forobj in root.iter('object'):difficult = obj.find('difficult').textcls = obj...