标签：yolov5 滑块 img -- self import model 识别 image
一、数据集准备
文件夹创建
images=>存放图片
labels=>存放标注坐标
gap.yaml =>yolov5训练配置文件
使用https://github.com/tzutalin/labelImg进行人工标注，将标注结果保存在lables文件夹
labelImg添加标注类型
注意红色位置
二、下载yolov5训练
下载地址https://github.com/ultralytics/yolov5
配置好环境，直接在命令行输入：
python train.py --img 640 --batch-size 4 --epochs 10 --data d:\MyDataset\Captcha_gap\gap.yaml --weights yolov5s.pt --nosave --cache
主要修改--batch-size，--epochs，--data
--batch-size，--epochs根据你电脑的配置修改，数值越大训练效果和时间越长，但有可能会爆内存错误，此时就得调小
--data是你准备的训练配置文件
训练结束后会在run/train下生成一个文件夹exp开头的，训练成功会生成一个weights文件夹，里面有2个文件，一个是best.pt(最优模型)一个是last.pt(最后模型)
三、进行接口推理
三种方式：
1.命令行：
python detect.py --weights runs/train/exp14/weights/best.pt --img 640 --conf 0.25 --source D:\MyDataset\Captcha_gap\images\0ca47576-3b27-4b67-97bc-bf0205fad9fd.png
2.torch.hub:
import torch
# Model
model = torch.hub.load('ultralytics/yolov5',"custom",path=r'D:\PycharmProjects\pytorch_pro\yolov5\runs\train\exp14\weights\best.pt') # or yolov5m, yolov5l, yolov5x, custom
# Images
img = r'D:\MyDataset\Captcha_gap\images\5e6e475c-754d-4723-9412-4c048988a4d0.png' # or file, Path, PIL, OpenCV, numpy, list
# Inference
results = model(img)
# Results
results.show() # or .show(), .save(), .crop(), .pandas(), etc.
3.inference
import torch
import numpy as np
from models.experimental import attempt_load
from utils.general import non_max_suppression, scale_coords
from utils.torch_utils import select_device
import cv2
from random import randint
from utils.datasets import letterbox
class Detector(object):
def __init__(self):
self.img_size = 640
self.threshold = 0.4
self.max_frame = 160
self.init_model()
def init_model(self):
self.weights = r'D:\PycharmProjects\pytorch_pro\yolov5\runs\train\exp14\weights\best.pt'
self.device = '-1' if torch.cuda.is_available() else 'cpu'
self.device = select_device(self.device)
model = attempt_load(self.weights, map_location=self.device)
model.to(self.device).eval()
model.half()
# torch.save(model, 'test.pt')
self.m = model
self.names = model.module.names if hasattr(
model, 'module') else model.names
self.colors = [
(randint(0, 255), randint(0, 255), randint(0, 255)) for _ in self.names
]
def preprocess(self, img):
img0 = img.copy()
img = letterbox(img, new_shape=self.img_size)[0]
img = img[:, :, ::-1].transpose(2, 0, 1)
img = np.ascontiguousarray(img)
img = torch.from_numpy(img).to(self.device)
img = img.half() # 半精度
img /= 255.0 # 图像归一化
if img.ndimension() == 3:
img = img.unsqueeze(0)
return img0, img
def plot_bboxes(self, image, bboxes, line_thickness=None):
tl = line_thickness or round(
0.002 * (image.shape[0] + image.shape[1]) / 2) + 1 # line/font thickness
for (x1, y1, x2, y2, cls_id, conf) in bboxes:
color = self.colors[self.names.index(cls_id)]
c1, c2 = (x1, y1), (x2, y2)
cv2.rectangle(image, c1, c2, color,
thickness=tl, lineType=cv2.LINE_AA)
tf = max(tl - 1, 1) # font thickness
t_size = cv2.getTextSize(
cls_id, 0, fontScale=tl / 3, thickness=tf)[0]
c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
cv2.rectangle(image, c1, c2, color, -1, cv2.LINE_AA) # filled
cv2.putText(image, '{} ID-{:.2f}'.format(cls_id, conf), (c1[0], c1[1] - 2), 0, tl / 3,
[225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
return image
def detect(self, im):
im0, img = self.preprocess(im)
pred = self.m(img, augment=False)[0]
pred = pred.float()
pred = non_max_suppression(pred, self.threshold, 0.3)
pred_boxes = []
image_info = {}
count = 0
for det in pred:
if det is not None and len(det):
det[:, :4] = scale_coords(
img.shape[2:], det[:, :4], im0.shape).round()
for *x, conf, cls_id in det:
lbl = self.names[int(cls_id)]
x1, y1 = int(x[0]), int(x[1])
x2, y2 = int(x[2]), int(x[3])
pred_boxes.append(
(x1, y1, x2, y2, lbl, conf))
count += 1
key = '{}-{:02}'.format(lbl, count)
# image_info[key] = ['{}×{}'.format(
# x2-x1, y2-y1), np.round(float(conf), 3)]
image_info[key]={"conf":np.round(float(conf), 3),"x1":x1,"y1":y1,"x2":x2,"y2":y2}
im = self.plot_bboxes(im, pred_boxes)
return im, image_info
if __name__ == '__main__':
model = Detector()
img=cv2.imread(r'D:\MyDataset\Captcha_gap\images\5e6e475c-754d-4723-9412-4c048988a4d0.png')
img_y, image_info = model.detect(img)
print(img_y, image_info)
标签：yolov5,滑块,img,--,self,import,model,识别,image 来源： https://www.cnblogs.com/angelyan/p/15857547.html
本站声明： 1. iCode9 技术分享网（下文简称本站）提供的所有内容，仅供技术学习、探讨和分享； 2. 关于本站的所有留言、评论、转载及引用，纯属内容发起人的个人观点，与本站观点和立场无关； 3. 关于本站的所有言论和文字，纯属内容发起人的个人观点，与本站观点和立场无关； 4. 本站文章均是网友提供，不完全保证技术分享内容的完整性、准确性、时效性、风险性和版权归属；如您发现该文章侵犯了您的权益，可联系我们第一时间进行删除； 5. 本站为非盈利性的个人网站，所有内容不会用来进行牟利，也不会利用任何形式的广告来间接获益，纯粹是为了广大技术爱好者提供技术内容和技术思想的分享性交流网站。