关于python 的实时口罩佩戴识别+接上篇

接求助

关于上次自己做的那个有些问题在查找一些opencv 的实例之后发现原来是自己的技术没有学好
看一篇获取摄像头的代码

#coding:utf-8
#Created by Pyangthon
import cv2 
    
# 获取视频流
cap = cv2.VideoCapture(0)
# 指定编码格式
fourcc = cv2.VideoWriter_fourcc(*'MJPG')
# cv2.VideoWriter的参数分别为：保存路径，编码格式，帧率，帧大小
out = cv2.VideoWriter("try.avi", fourcc, 20.0, (640,480))
 
while(cap.isOpened()):
    ret, frame = cap.read()
    
    if ret==True:
        # 如果帧的大小与上述的(640, 480)不一致，需要resize
        out.write(frame)
 
        cv2.imshow('frame',frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
    else:
        break
 
cap.release()
out.release()
cv2.destroyAllWindows()

我们看到是可以获取摄像头功能的
再来看看口罩识别

# -*- coding:utf-8 -*-
import cv2
import time
import argparse

import numpy as np
from PIL import Image
from keras.models import model_from_json
from utils.anchor_generator import generate_anchors
from utils.anchor_decode import decode_bbox
from utils.nms import single_class_non_max_suppression
from load_model.tensorflow_loader import load_tf_model, tf_inference


#导入口罩智能识别模型
sess, graph = load_tf_model('models\face_mask_detection.pb')
# anchor configuration锚点设置
feature_map_sizes = [[33, 33], [17, 17], [9, 9], [5, 5], [3, 3]]
anchor_sizes = [[0.04, 0.056], [0.08, 0.11], [0.16, 0.22], [0.32, 0.45], [0.64, 0.72]]
anchor_ratios = [[1, 0.62, 0.42]] * 5

# generate anchors生成锚点
anchors = generate_anchors(feature_map_sizes, anchor_sizes, anchor_ratios)

#以下用于推断(inference)，批大小为1，模型输出形状为[1，N，4]，因此将锚点的dim扩展为[1，anchor_num，4]
anchors_exp = np.expand_dims(anchors, axis=0)
#将推断结果分为两类，一类Mask，表示佩戴了口罩，另外一类是NoMask，表示没有佩戴口罩
id2class = {0: 'Mask', 1: 'NoMask'}


def inference(image, conf_thresh=0.5, iou_thresh=0.4, target_shape=(160, 160), draw_result=True, show_result=True):
    '''  检测推理的主要功能
   # ：param image：3D numpy图片数组
    #  ：param conf_thresh：分类概率的最小阈值。
   #  ：param iou_thresh：网管的IOU门限
   #  ：param target_shape：模型输入大小。
   #  ：param draw_result：是否将边框拖入图像。
   #  ：param show_result：是否显示图像。
    '''
    # image = np.copy(image)
    output_info = []
    height, width, _ = image.shape
    image_resized = cv2.resize(image, target_shape)
    image_np = image_resized / 255.0  # 归一化到0~1
    image_exp = np.expand_dims(image_np, axis=0)
    y_bboxes_output, y_cls_output = tf_inference(sess, graph, image_exp)

    # remove the batch dimension, for batch is always 1 for inference.
    y_bboxes = decode_bbox(anchors_exp, y_bboxes_output)[0]
    y_cls = y_cls_output[0]
    # 为了加快速度，请执行单类NMS，而不是多类NMS。
    bbox_max_scores = np.max(y_cls, axis=1)
    bbox_max_score_classes = np.argmax(y_cls, axis=1)

    # keep_idx是nms之后的活动边界框。
    keep_idxs = single_class_non_max_suppression(y_bboxes, bbox_max_scores, conf_thresh=conf_thresh,iou_thresh=iou_thresh)
    for idx in keep_idxs:
        conf = float(bbox_max_scores[idx])
        class_id = bbox_max_score_classes[idx]
        bbox = y_bboxes[idx]
        # 裁剪坐标，避免该值超出图像边界。
        xmin = max(0, int(bbox[0] * width))
        ymin = max(0, int(bbox[1] * height))
        xmax = min(int(bbox[2] * width), width)
        ymax = min(int(bbox[3] * height), height)

        if draw_result:
            if class_id == 0:
                color = (0, 255, 0)
            else:
                color = (255, 0, 0)
            cv2.rectangle(image, (xmin, ymin), (xmax, ymax), color, 2)
            cv2.putText(image, "%s: %.2f" % (id2class[class_id], conf), (xmin + 2, ymin - 2),
                        cv2.FONT_HERSHEY_SIMPLEX, 1, color)
        output_info.append([class_id, conf, xmin, ymin, xmax, ymax])

    if show_result:
        Image.fromarray(image).show()
    return output_info

def run_on_video(video_path, output_video_name, conf_thresh):
    cap = cv2.VideoCapture(video_path)
    height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
    width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
    fps = cap.get(cv2.CAP_PROP_FPS)
    fourcc = cv2.VideoWriter_fourcc(*'XVID')
    #writer = cv2.VideoWriter(output_video_name, fourcc, int(fps), (int(width), int(height)))
    total_frames = cap.get(cv2.CAP_PROP_FRAME_COUNT)
    if not cap.isOpened():
        raise ValueError("Video open failed.")
        return
    status = True
    idx = 0
    while status:
        start_stamp = time.time()
        status, img_raw = cap.read()
        img_raw = cv2.cvtColor(img_raw, cv2.COLOR_BGR2RGB)
        read_frame_stamp = time.time()
        if (status):
            inference(img_raw,
                      conf_thresh,
                      iou_thresh=0.5,
                      target_shape=(260, 260),
                      draw_result=True,
                      show_result=False)
            cv2.imshow('image', img_raw[:, :, ::-1])
            cv2.waitKey(1)
            inference_stamp = time.time()
            # writer.write(img_raw)
            write_frame_stamp = time.time()
            idx += 1
            print("%d of %d" % (idx, total_frames))
            print("read_frame:%f, infer time:%f, write time:%f" % (read_frame_stamp - start_stamp, inference_stamp - read_frame_stamp, write_frame_stamp - inference_stamp))
run_on_video("mp4/test.mp4", '', conf_thresh=0.5)

其实就是路径的问题和摄像头调用的问题修改口罩佩戴py里面的两处就好了


就可以接着本人上次的代码改就行吧！
接下来就是我的文件夹
链接：https://pan.baidu.com/s/1-JGCDS8i3BsEKe0Dd3NLNg
下载后调试几下就可以用了
这里附上我做成功的视频