基于PyTorch和Fast RCNN快速实现目标识别

Faster RCNN，相对于R-CNN在结构上，Faster RCNN已经将特征抽取(feature extraction)，proposal提取，bounding box regression(rect refine)，classification都整合在了一个网络中，使得综合性能有较大提高，在检测速度方面尤为明显。

PyTorch是一个开源的Python机器学习库，基于Torch，用于自然语言处理等应用程序。相对于TensorFlow更加轻量化，更适合于科研和小型项目的使用。

这里介绍一个简单的例子，基于Fast RCNN算法和PyTorch快速实现目标识别。这里使用的是coco数据集已经训练好的在线的模型，运行起来比较简单。

代码

from PIL import Image
import matplotlib.pyplot as plt
# pip install -U matplotlib
import torch
# pip install pytorch
import torchvision.transforms as T
import torchvision
# pip install torchvision
import numpy as np
import cv2

import os

os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"

# pip install opencv-python



# 下载已经训练好的模型
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
model.eval()

COCO_INSTANCE_CATEGORY_NAMES = [
    '__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
    'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'N/A', 'stop sign',
    'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
    'elephant', 'bear', 'zebra', 'giraffe', 'N/A', 'backpack', 'umbrella', 'N/A', 'N/A',
    'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
    'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
    'bottle', 'N/A', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
    'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
    'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'N/A', 'dining table',
    'N/A', 'N/A', 'toilet', 'N/A', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
    'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'N/A', 'book',
    'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush'
]


def get_prediction(img_path, threshold):
    img = Image.open(img_path)
    # 转换一个PIL库的图片或者numpy的数组为tensor张量类型；转换从[0,255]->[0,1]
    transform = T.Compose([T.ToTensor()])
    img = transform(img)

    pred = model([img])
    print(pred[0]['labels'].numpy())
    # 类别提取
    pred_class = [COCO_INSTANCE_CATEGORY_NAMES[i] for i in list(pred[0]['labels'].numpy())]
    # 坐标提取
    pred_boxes = [[(i[0], i[1]), (i[2], i[3])] for i in list(pred[0]['boxes'].detach().numpy())]

    # 找出符合相似度要求的
    pred_score = list(pred[0]['scores'].detach().numpy())
    pred_t = [pred_score.index(x) for x in pred_score if x > threshold][-1]
    pred_boxes = pred_boxes[:pred_t + 1]
    pred_class = pred_class[:pred_t + 1]
    print("pred_class:", pred_class)
    print("pred_boxes:", pred_boxes)
    return pred_boxes, pred_class


def object_detection_api(img_path, threshold=0.5, rect_th=3, text_size=3, text_th=3):
    boxes, pred_cls = get_prediction(img_path, threshold)
    img = cv2.imread(img_path)
    # 转换为RGB图像
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    for i in range(len(boxes)):

        # 根据坐标圈出目标
        cv2.rectangle(img, (int(boxes[i][0][0]), int(boxes[i][0][1])), (int(boxes[i][1][0]), int(boxes[i][1][1])),
                      color=(0, 255, 0),
                      thickness=rect_th)
        # 标注类别
        cv2.putText(img, pred_cls[i], (int(boxes[i][0][0]), int(boxes[i][0][1])), cv2.FONT_HERSHEY_SIMPLEX, text_size,
                    (0, 255, 0), thickness=text_th)
    plt.imshow(img)
    plt.show()



if __name__ == '__main__':
    object_detection_api(img_path=r"C:\Users\hanbo\Pictures\dog.jpg")

结果

示例1

示例2