DETR实现目标检测(二)-利用自己训练的模型进行预测

1、图片预测（CPU）

关于DETR模型训练自己的数据集参考上篇文章：

DETR实现目标检测(一)-训练自己的数据集-CSDN博客

训练完成后的模型文件保存位置如下：

准备好要预测的图片：

然后直接调用模型进行预测，并设置置信度阈值来输出检测框：

最后用plot函数来画出图片及预测框，效果如下：

最后附上完整代码：

from PIL import Image
import matplotlib.pyplot as plt
import torchvision.transforms as T
from hubconf import *
from util.misc import nested_tensor_from_tensor_list
 
torch.set_grad_enabled(False)
 
# COCO classes
CLASSES = [
    '1'
]
 
# colors for visualization
COLORS = [[0.850, 0.325, 0.098]]
 
# standard PyTorch mean-std input image normalization
transform = T.Compose([
    T.Resize(800),
    T.ToTensor(),
    T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
 
 
# for output bounding box post-processing
def box_cxcywh_to_xyxy(x):
    x_c, y_c, w, h = x.unbind(1)
    b = [(x_c - 0.5 * w), (y_c - 0.5 * h),
         (x_c + 0.5 * w), (y_c + 0.5 * h)]
    return torch.stack(b, dim=1)
 
 
def rescale_bboxes(out_bbox, size):
    img_w, img_h = size
    b = box_cxcywh_to_xyxy(out_bbox)
    b = b * torch.tensor([img_w, img_h, img_w, img_h], dtype=torch.float32)
    return b
 
 
def predict(im, model, transform):
    # mean-std normalize the input image (batch-size: 1)
    anImg = transform(im)
    data = nested_tensor_from_tensor_list([anImg])
 
    # propagate through the model
    outputs = model(data)
 
    # keep only predictions with 0.7+ confidence
    probas = outputs['pred_logits'].softmax(-1)[0, :, :-1]
 
    keep = probas.max(-1).values > 5*1e-8  # 置信度阈值
 
    # convert boxes from [0; 1] to image scales
    bboxes_scaled = rescale_bboxes(outputs['pred_boxes'][0, keep], im.size)
    return probas[keep], bboxes_scaled
 
 
def plot_results(pil_img, prob, boxes):
    plt.figure(figsize=(16, 10))
    plt.imshow(pil_img)
    ax = plt.gca()
    colors = COLORS * 100
    prob2 = prob[:, 1:]  # 第一列是背景，剔除
    for p, (xmin, ymin, xmax, ymax), c in zip(prob2, boxes.tolist(), colors):
        ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin,
                                   fill=False, color=c, linewidth=3))
        cl = p.argmax()
        text = f'{CLASSES[cl]}: {p[cl]:0.2f}'
        ax.text(xmin, ymin, text, fontsize=15,
                bbox=dict(facecolor='yellow', alpha=0.5))
    plt.axis('off')
    plt.show()
 
 
if __name__ == "__main__":
    model = detr_resnet50(False, 1) # 这里与前面的num_classes数值相同，就是最大的category id值 + 1
    state_dict = torch.load(r"C:\Users\90539\Downloads\detr-main\detr-main\data\output\checkpoint.pth", map_location='cpu')
    model.load_state_dict(state_dict["model"])
    model.eval()
 
    # im = Image.open('data/coco_frame_count/train2017/001554.jpg')
    im = Image.open(r'C:\Users\90539\Downloads\detr-main\detr-main\data/coco_frame_count/val2017/09-12-52-0.png')
 
    scores, boxes = predict(im, model, transform)
    plot_results(im, scores, boxes)