这段时间部署Yolov5系列模型，想着先使用ONNXRuntime运行一下转换后的onnx模型，但是看着官方给出的detect.py，我陷入了沉思。
我是真的不想抠代码！于是乎，秉承着程序猿的优良传统，我打开了百度。
结果可想而知，不然你也不会看到我的文章了。草草看了一下，大部分是就给出一部分代码，然后告诉你后处理的部分源码里都有............
拜托，我就是不想抠源代码才来百度的啊，哎，还是要靠自己抠代码啊。
2023.04.23--完善了代码后处理部分，使其能够应用到任何一张图片。
yolov5官方代码，链接如下 https://github.com/ultralytics/yolov5.git
目录
一. pytorch模型转onnx模型
什么，你不会导出onnx模型？那就看看官方集成的export.py吧
Yolo官方给出的exporty.py确实很好用，在命令行运行如下代码就能轻松的导出onnx模型。
python3 export.py --weights=./demo.pt --include=onnx --img=640 --batch=1 --opset=12 --simplify当然，你也可以参看我的另一篇文章，学习并尝试如何转化onnx模型：ONNX系列一：ONNX的使用,从转化到推理
参数介绍如下：
| --weights | 原pytorch模型 |
| --include | 转换的目标格式，支持onnx, torchscript等 |
| --img | 模型的输入大小，默认是640 |
| --batch | batch size |
| --opset | onnx算子集版本 |
| --simplify | 简化onnx模型 |
| --half | 生成fp16模型，使用该参数时需要指定cuda device，eg：--device=0 |
二.使用ONNXRuntime进行推理
1.环境要求
python3.7
2.代码实现
okok，能抠的不能抠的都给你抠出来了，你只要动动小手改一下main函数中的路径就能用了!
import cv2
import numpy as np
import onnxruntime as rt
CLASSES = {
0: 'person',
1: 'bicycle',
2: 'car',
3: 'motorbike',
4: 'aeroplane',
5: 'bus',
6: 'train',
7: 'truck',
8: 'boat',
9: 'traffic light',
10: 'fire hydrant',
11: 'stop sign',
12: 'parking meter',
13: 'bench',
14: 'bird',
15: 'cat',
16: 'dog',
17: 'horse',
18: 'sheep',
19: 'cow',
20: 'elephant',
21: 'bear',
22: 'zebra',
23: 'giraffe',
24: 'backpack',
25: 'umbrella',
26: 'handbag',
27: 'tie',
28: 'suitcase',
29: 'frisbee',
30: 'skis',
31: 'snowboard',
32: 'sports ball',
33: 'kite',
34: 'baseball bat',
35: 'baseball glove',
36: 'skateboard',
37: 'surfboard',
38: 'tennis racket',
39: 'bottle',
40: 'wine glass',
41: 'cup',
42: 'fork',
43: 'knife',
44: 'spoon',
45: 'bowl',
46: 'banana',
47: 'apple',
48: 'sandwich',
49: 'orange',
50: 'broccoli',
51: 'carrot',
52: 'hot dog',
53: 'pizza',
54: 'donut',
55: 'cake',
56: 'chair',
57: 'sofa',
58: 'potted plant',
59: 'bed',
60: 'dining table',
61: 'toilet',
62: 'tvmonitor',
63: 'laptop',
64: 'mouse',
65: 'remote',
66: 'keyboard',
67: 'cell phone',
68: 'microwave',
69: 'oven',
70: 'toaster',
71: 'sink',
72: 'refrigerator',
73: 'book',
74: 'clock',
75: 'vase',
76: 'scissors',
77: 'teddy bear',
78: 'hair drier',
79: 'toothbrush'
}
def box_iou(box1, box2, eps=1e-7):
(a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2)
inter = (np.min(a2, b2) - np.max(a1, b1)).clamp(0).prod(2)
return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps)
def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):
# Resize and pad image while meeting stride-multiple constraints
shape = im.shape[:2] # current shape [height, width]
if isinstance(new_shape, int):
new_shape = (new_shape, new_shape)
# Scale ratio (new / old)
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
if not scaleup: # only scale down, do not scale up (for better val mAP)
r = min(r, 1.0)
# Compute padding
ratio = r, r # width, height ratios
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
if auto: # minimum rectangle
dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding
elif scaleFill: # stretch
dw, dh = 0.0, 0.0
new_unpad = (new_shape[1], new_shape[0])
ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios
dw /= 2 # divide padding into 2 sides
dh /= 2
if shape[::-1] != new_unpad: # resize
im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
return im, ratio, (dw, dh)
def onnx_inf(onnxModulePath, data):
sess = rt.InferenceSession(onnxModulePath)
input_name = sess.get_inputs()[0].name
output_name = sess.get_outputs()[0].name
pred_onnx = sess.run([output_name], {input_name: data.reshape(1, 3, 640, 640).astype(np.float32)})
return pred_onnx
def xywh2xyxy(x):
# Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
# isinstance 用来判断某个变量是否属于某种类型
y = np.copy(x)
y[..., 0] = x[..., 0] - x[..., 2] / 2 # top left x
y[..., 1] = x[..., 1] - x[..., 3] / 2 # top left y
y[..., 2] = x[..., 0] + x[..., 2] / 2 # bottom right x
y[..., 3] = x[..., 1] + x[..., 3] / 2 # bottom right y
return y
def nms_boxes(boxes, scores):
x = boxes[:, 0]
y = boxes[:, 1]
w = boxes[:, 2] - boxes[:, 0]
h = boxes[:, 3] - boxes[:, 1]
areas = w * h
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
i = order[0]
keep.append(i)
xx1 = np.maximum(x[i], x[order[1:]])
yy1 = np.maximum(y[i], y[order[1:]])
xx2 = np.minimum(x[i] + w[i], x[order[1:]] + w[order[1:]])
yy2 = np.minimum(y[i] + h[i], y[order[1:]] + h[order[1:]])
w1 = np.maximum(0.0, xx2 - xx1 + 0.00001)
h1 = np.maximum(0.0, yy2 - yy1 + 0.00001)
inter = w1 * h1
ovr = inter / (areas[i] + areas[order[1:]] - inter)
inds = np.where(ovr <= 0.45)[0]
order = order[inds + 1]
keep = np.array(keep)
return keep
def non_max_suppression(
prediction,
conf_thres=0.25,
iou_thres=0.45,
classes=None,
agnostic=False,
multi_label=False,
labels=(),
max_det=300,
nm=0, # number of masks
):
"""Non-Maximum Suppression (NMS) on inference results to reject overlapping detections
Returns:
list of detections, on (n,6) tensor per image [xyxy, conf, cls]
"""
# Checks
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'
if isinstance(prediction, (list, tuple)): # YOLOv5 model in validation model, output = (inference_out, loss_out)
prediction = prediction[0] # select only inference output
bs = prediction.shape[0] # batch size
nc = prediction.shape[2] - nm - 5 # number of classes
xc = prediction[..., 4] > conf_thres # candidates
# Settings
max_wh = 7680 # (pixels) maximum box width and height
max_nms = 30000 # maximum number of boxes into torchvision.ops.nms()
redundant = True # require redundant detections
multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img)
merge = False # use merge-NMS
mi = 5 + nc # mask start index
output = [np.zeros((0, 6 + nm))] * bs
for xi, x in enumerate(prediction): # image index, image inference
x = x[xc[xi]] # confidence
if labels and len(labels[xi]):
lb = labels[xi]
v = np.zeros(len(lb), nc + nm + 5)
v[:, :4] = lb[:, 1:5] # box
v[:, 4] = 1.0 # conf
v[range(len(lb)), lb[:, 0].long() + 5] = 1.0 # cls
x = np.concatenate((x, v), 0)
# If none remain process next image
if not x.shape[0]:
continue
x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf
# Box/Mask
box = xywh2xyxy(x[:, :4]) # center_x, center_y, width, height) to (x1, y1, x2, y2)
mask = x[:, mi:] # zero columns if no masks
# Detections matrix nx6 (xyxy, conf, cls)
if multi_label:
i, j = (x[:, 5:mi] > conf_thres).nonzero(as_tuple=False).T
x = np.concatenate((box[i], x[i, 5 + j, None], j[:, None].float(), mask[i]), 1)
else: # best class only
conf = np.max(x[:, 5:mi], 1).reshape(box.shape[:1][0], 1)
j = np.argmax(x[:, 5:mi], 1).reshape(box.shape[:1][0], 1)
x = np.concatenate((box, conf, j, mask), 1)[conf.reshape(box.shape[:1][0]) > conf_thres]
# Filter by class
if classes is not None:
x = x[(x[:, 5:6] == np.array(classes, device=x.device)).any(1)]
# Check shape
n = x.shape[0] # number of boxes
if not n: # no boxes
continue
index = x[:, 4].argsort(axis=0)[:max_nms][::-1]
x = x[index]
# Batched NMS
c = x[:, 5:6] * (0 if agnostic else max_wh) # classes
boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores
i = nms_boxes(boxes, scores)
i = i[:max_det] # limit detections
# 用来合并框的
if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean)
iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix
weights = iou * scores[None] # box weights
x[i, :4] = np.multiply(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes
if redundant:
i = i[iou.sum(1) > 1] # require redundancy
output[xi] = x[i]
return output
def clip_boxes(boxes, shape):
# Clip boxes (xyxy) to image shape (height, width)
boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1]) # x1, x2
boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0]) # y1, y2
def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None):
# Rescale boxes (xyxy) from img1_shape to img0_shape
if ratio_pad is None: # calculate from img0_shape
gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding
else:
gain = ratio_pad[0][0]
pad = ratio_pad[1]
boxes[..., [0, 2]] -= pad[0] # x padding
boxes[..., [1, 3]] -= pad[1] # y padding
boxes[..., :4] /= gain
clip_boxes(boxes, img0_shape)
return boxes
if __name__ == "__main__":
onnxModulePath = "/PATH_to_Yolov5x.onnx"
IMG_Path = "/PATH_to_test.jpg"
imgsz = (640, 640)
img = cv2.imread(IMG_Path)
img = cv2.resize(img, (640, 640))
# preprocess
im = letterbox(img, imgsz, auto=True)[0] # padded resize
im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
im = np.ascontiguousarray(im) # contiguous
im = im.astype(np.float32)
im /= 255 # 0 - 255 to 0.0 - 1.0
if len(im.shape) == 3:
im = im[None] # expand for batch dim
# inference
pred = onnx_inf(onnxModulePath, im)
# NMS
conf_thres = 0.25 # confidence threshold
iou_thres = 0.45 # NMS IOU threshold
max_det = 1000 # maximum detections per image
classes = None # filter by class: --class 0, or --class 0 2 3
agnostic_nms = False # class-agnostic NMS
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
# Process predictions
seen = 0
for i, det in enumerate(pred): # per image
seen += 1
if len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], img.shape).round()
# print(pred)
outputs = pred[0][:, :6]
if len(outputs[:, 4:] > 0):
for i in outputs:
prob = i[4]
cls = int(i[5])
prob = np.around(prob, decimals=2)
if prob >= 0.4:
all_pred_boxes = i[:4]
for b in range(len(all_pred_boxes)):
x1 = int(all_pred_boxes[0])
y1 = int(all_pred_boxes[1])
x2 = int(all_pred_boxes[2])
y2 = int(all_pred_boxes[3])
cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 1)
cv2.putText(img, CLASSES[cls]+' '+str(prob), (x1, y1), cv2.FONT_HERSHEY_TRIPLEX, 0.8, (0, 255, 0), 1, 4)
cv2.imwrite('./data/images/test123456789.png', img)运行结果如下：
