????????最近在折腾yolov5，训练了一个识别纸牌的模型，最后使用onnxruntime进行部署，感兴趣的可以上github上clone下来玩玩，模型的权重文件上传到了百度网盘，链接和提取码写在readme里。
? ? ? ? 模型的训练使用了yolov5l的权重模型，训练的时候使用的batchsize为8（理论可以设置的更大，gpu的占用还没吃满），训练了200个epoch，取了效果最好的权重模型。
? ? ? ? 从git上下载下来后的文件结构大致是这样的：
?????????function文件夹中存放了一些工具函数，image存放待检测的图片，model则存放模型的权重文件。
config.py
LABEL_DICT = {'cardlabel': ['10C', '10D', '10H', '10S', '2C', '2D', '2H', '2S', '3C', '3D', '3H', '3S',
'4C', '4D', '4H', '4S', '5C', '5D', '5H', '5S', '6C', '6D', '6H', '6S', '7C',
'7D', '7H', '7S', '8C', '8D', '8H', '8S', '9C', '9D', '9H', '9S', '1C', '1D',
'1H', '1S', '11C', '11D', '11H', '11S', '13C', '13D', '13H', '13S', '12C', '12D', '12H', '12S']}????????这里主要是包括52张牌（不包括大小王）的label，J,Q,K,A我都用对应的数字进行替代，数字后面的H,C,D,S对应的是花色，分别是红桃，草花，方片和黑桃。
utils.py（只贴部分代码）
class LoadImages:
def __init__(self, path, img_size=640, stride=32, auto=True):
p = str(Path(path).resolve())
if '*' in p:
files = sorted(glob.glob(p, recursive=True))
elif os.path.isdir(p):
files = sorted(glob.glob(os.path.join(p, '*.*')))
elif os.path.isfile(p):
files = [p]
else:
raise Exception(f'ERROR: {p} does not exist')
images = [x for x in files if x.split('.')[-1].lower() in IMG_FORMATS]
videos = [x for x in files if x.split('.')[-1].lower() in VID_FORMATS]
ni, nv = len(images), len(videos)
self.img_size = img_size
self.stride = stride
self.files = images + videos
self.nf = ni + nv
self.video_flag = [False] * ni + [True] * nv
self.mode = 'image'
self.auto = auto
if any(videos):
self.new_video(videos[0])
else:
self.cap = None
assert self.nf > 0, f'No images or videos found in {p}. ' \
f'Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {VID_FORMATS}'
def __iter__(self):
self.count = 0
return self
def __next__(self):
if self.count == self.nf:
raise StopIteration
path = self.files[self.count]
if self.video_flag[self.count]:
# Read video
self.mode = 'video'
ret_val, img0 = self.cap.read()
if not ret_val:
self.count += 1
self.cap.release()
if self.count == self.nf:
raise StopIteration
else:
path = self.files[self.count]
self.new_video(path)
ret_val, img0 = self.cap.read()
self.frame += 1
print(f'video {self.count + 1}/{self.nf} ({self.frame}/{self.frames}) {path}: ', end='')
else:
# Read image
self.count += 1
img0 = cv2.imread(path) # BGR
assert img0 is not None, 'Image Not Found ' + path
print(f'image {self.count}/{self.nf} {path}: ', end='')
# Padded resize
img = letterbox(img0, self.img_size, stride=self.stride, auto=self.auto)[0]
# Convert
img = img.transpose((2, 0, 1))[::-1]
img = np.ascontiguousarray(img)
return path, img, img0, self.cap
def new_video(self, path):
self.frame = 0
self.cap = cv2.VideoCapture(path)
self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
def __len__(self):
return self.nf????????这个类主要负责读取图像数据。
def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False,
labels=(), max_det=300):
nc = prediction.shape[2] - 5
xc = prediction[..., 4] > conf_thres
assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0'
assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'
min_wh, max_wh = 2, 4096
max_nms = 30000
time_limit = 10.0
redundant = True
multi_label &= nc > 1
merge = False
t = time.time()
output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0]
for xi, x in enumerate(prediction):
# Apply constraints
x = x[xc[xi]]
# Cat apriori labels if autolabelling
if labels and len(labels[xi]):
l = labels[xi]
v = torch.zeros((len(l), nc + 5), device=x.device)
v[:, :4] = l[:, 1:5]
v[:, 4] = 1.0
v[range(len(l)), l[:, 0].long() + 5] = 1.0
x = torch.cat((x, v), 0)
# If none remain process next image
if not x.shape[0]:
continue
# Compute conf
x[:, 5:] *= x[:, 4:5]
box = xywh2xyxy(x[:, :4])
if multi_label:
i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T
x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)
else:
conf, j = x[:, 5:].max(1, keepdim=True)
x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]
# Filter by class
if classes is not None:
x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
# Check shape
n = x.shape[0]
if not n:
continue
elif n > max_nms:
x = x[x[:, 4].argsort(descending=True)[:max_nms]]
# Batched NMS
c = x[:, 5:6] * (0 if agnostic else max_wh)
boxes, scores = x[:, :4] + c, x[:, 4]
i = torchvision.ops.nms(boxes, scores, iou_thres)
if i.shape[0] > max_det:
i = i[:max_det]
if merge and (1 < n < 3E3):
# update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
iou = box_iou(boxes[i], boxes) > iou_thres
weights = iou * scores[None]
x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True)
if redundant:
i = i[iou.sum(1) > 1]
output[xi] = x[i]
if (time.time() - t) > time_limit:
print(f'WARNING: NMS time limit {time_limit}s exceeded')
break
return output? ? ? ? 这里主要是非极大值抑制，输出最终的预测结果，包括坐标值，置信度以及标签索引值
# -*- coding: utf-8 -*-
"""
Time: 2021.10.26
Author: Athrunsunny
Version: V 0.1
File: inference.py
Describe: Functions in this file is use to inference
"""
import cv2
import torch
import time
import onnxruntime
import numpy as np
from function.utils import LoadImages, Annotator, colors, check_img_size, non_max_suppression, scale_coords
from function import config as CFG
def load_model(weights, **options):
imgsz = options.pop('imgsz', 640)
stride = options.pop('stride', 64)
w = str(weights[0] if isinstance(weights, list) else weights)
session = onnxruntime.InferenceSession(w, None)
imgsz = check_img_size(imgsz, s=stride)
return session, imgsz, stride
def image_process(img):
assert isinstance(img, np.ndarray)
img = img.astype('float32')
img /= 255.0
if len(img.shape) == 3:
img = img[None]
return img
def inference(session, img, **options):
conf_thres = options.pop('conf_thres', 0.25)
iou_thres = options.pop('iou_thres', 0.45)
classes = options.pop('classes', None)
agnostic = options.pop('agnostic', False)
max_det = options.pop('max_det', 1000)
pred = torch.tensor(session.run([session.get_outputs()[0].name], {session.get_inputs()[0].name: img}))
pred = non_max_suppression(pred, conf_thres=conf_thres, iou_thres=iou_thres, classes=classes, max_det=max_det,
agnostic=agnostic)
return pred
def post_process(pred, img, im0s, dataset, **options):
showImg = options.pop('showImg', False)
hide_conf = options.pop('hide_conf', False)
hide_labels = options.pop('hide_labels', False)
line_thickness = options.pop('line_thickness', 1)
labelDict = options.pop('labelDict', None)
labels = labelDict['cardlabel']
res_label = []
for i, det in enumerate(pred):
s, im0, frame = '', im0s.copy(), getattr(dataset, 'frame', 0)
annotator = Annotator(im0, line_width=line_thickness, example=str(labels))
if len(det):
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
for *xyxy, conf, cls in reversed(det):
c = int(cls)
label = None if hide_labels else (labels[c] if hide_conf else f'{labels[c]} {conf:.2f}')
label_no_conf = None if hide_labels else (labels[c] if hide_conf else f'{labels[c]}')
res_label.append(label_no_conf)
annotator.box_label(xyxy, label, color=colors(c, True))
print(f'{s}')
im0 = annotator.result()
if showImg:
cv2.imshow('result', im0)
cv2.waitKey(0)
return res_label
def run(weights, source, **options):
conf_thres = options.pop('conf_thres', 0.25) # confidence threshold
iou_thres = options.pop('iou_thres', 0.45) # NMS IOU threshold
classes = options.pop('classes', None) # filter by class: --class 0, or --class 0 2 3
agnostic = options.pop('agnostic', False) # class-agnostic NMS
max_det = options.pop('max_det', 1000) # maximum detections per image
hide_conf = options.pop('hide_conf', False) # hide confidences
hide_labels = options.pop('hide_labels', False) # hide labels
line_thickness = options.pop('line_thickness', 1) # bounding box thickness (pixels)
imgsz = options.pop('imgsz', 640) # inference size (pixels)
showImg = options.pop('showImg', False) # show results
labelDict = options.pop('labelDict', CFG.LABEL_DICT) # config labels
session, imgsz, stride = load_model(weights=weights, imgsz=imgsz)
dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=False)
res = []
for path, img, im0s, vid_cap in dataset:
img = image_process(img)
t1 = time.time()
pred = inference(session, img, conf_thres=conf_thres, iou_thres=iou_thres, max_det=max_det, classes=classes,
agnostic=agnostic)
t2 = time.time()
print('Inference time:%.3fs' % (t2 - t1))
res = post_process(pred, img, im0s, dataset, hide_conf=hide_conf, hide_labels=hide_labels,
line_thickness=line_thickness, showImg=showImg, labelDict=labelDict)
return res
if __name__ == '__main__':
imagepath = 'image/1.jpg'
modelpath = 'model/weight.onnx'
res = run(modelpath, imagepath, showImg=True)
print(res)????????该项目到这里也就结束了，代码量也比较少，比较容易理解，以下附一张实际检测的效果图
????????由于用的是yolov5l的模型，最后检测的时候，也是比较耗时，cpu上平均检测耗时为500ms左右。