FasterRCNN 口罩检测：基于 Python 代码实现实时摄像头和图片识别

首先，需要安装 FasterRCNN 和 OpenCV 库，可以使用以下命令进行安装：

pip install tensorflow==1.15.0
pip install opencv-python==4.2.0.32
pip install keras==2.3.1
pip install numpy==1.19.3
pip install pillow==8.0.1
pip install Cython==0.29.21
pip install pycocotools==2.0.1
pip install matplotlib==3.3.3
pip install tensorflow-gpu==1.15.0

接下来，可以使用以下代码实现 FasterRCNN 口罩检测：

import cv2
import numpy as np
import os
import sys
import tensorflow as tf

# 加载模型
sys.path.append(os.getcwd())
from keras_frcnn import roi_helpers
from keras_frcnn import config
from keras_frcnn import resnet as nn

# 设置配置参数
config_output_filename = 'config.pickle'
with open(config_output_filename, 'rb') as f_in:
    C = pickle.load(f_in)

# 定义类别
classnames = ['BG', 'mask', 'no_mask']

# 加载模型权重
num_features = 1024
input_shape_img = (None, None, 3)
input_shape_features = (None, None, num_features)
img_input = tf.keras.layers.Input(shape=input_shape_img)
roi_input = tf.keras.layers.Input(shape=(C.num_rois, 4))
feature_map_input = tf.keras.layers.Input(shape=input_shape_features)
shared_layers = nn.nn_base(img_input, trainable=True)
rpn_layers = nn.rpn(shared_layers, num_anchors=len(C.anchor_box_ratios))
classifier = nn.classifier(feature_map_input, roi_input, C.num_rois, nb_classes=len(classnames), trainable=True)
model_rpn = tf.keras.models.Model(img_input, rpn_layers)
model_classifier = tf.keras.models.Model([feature_map_input, roi_input], classifier)
model_all = tf.keras.models.Model([img_input, roi_input], classifier)
model_all.load_weights(C.model_path)

# 定义颜色
color_dict = {0: (0, 0, 0), 1: (0, 255, 0), 2: (0, 0, 255)}

# 定义函数进行检测
def detect_mask(image_path):
    # 加载图片
    img = cv2.imread(image_path)

    # 运行 FasterRCNN 检测
    X, ratio = roi_helpers.format_img(img, C)
    X = np.transpose(X, (0, 2, 3, 1))
    [Y1, Y2, F] = model_rpn.predict(X)
    R = roi_helpers.rpn_to_roi(Y1, Y2, C, K.image_data_format(), overlap_thresh=0.7)
    R[:, 2] -= R[:, 0]
    R[:, 3] -= R[:, 1]
    bboxes = {}
    probs = {}

    for jk in range(R.shape[0] // C.num_rois + 1):
        ROIs = np.expand_dims(R[C.num_rois * jk:C.num_rois * (jk + 1), :], axis=0)
        if ROIs.shape[1] == 0:
            break

        if jk == R.shape[0] // C.num_rois:
            # pad R
            curr_shape = ROIs.shape
            target_shape = (curr_shape[0], C.num_rois, curr_shape[2])
            ROIs_padded = np.zeros(target_shape).astype(ROIs.dtype)
            ROIs_padded[:, :curr_shape[1], :] = ROIs
            ROIs_padded[0, curr_shape[1]:, :] = ROIs[0, 0, :]
            ROIs = ROIs_padded

        [P_cls, P_regr] = model_classifier.predict([F, ROIs])

        for ii in range(P_cls.shape[1]):
            if np.max(P_cls[0, ii, :]) < 0.8 or np.argmax(P_cls[0, ii, :]) == 0:
                continue

            cls_name = classnames[np.argmax(P_cls[0, ii, :])]
            if cls_name not in bboxes:
                bboxes[cls_name] = []
                probs[cls_name] = []

            (x, y, w, h) = ROIs[0, ii, :]
            cls_num = np.argmax(P_cls[0, ii, :])
            try:
                (tx, ty, tw, th) = P_regr[0, ii, 4*cls_num:4*(cls_num+1)]
                tx /= C.classifier_regr_std[0]
                ty /= C.classifier_regr_std[1]
                tw /= C.classifier_regr_std[2]
                th /= C.classifier_regr_std[3]
                x, y, w, h = roi_helpers.apply_regr(x, y, w, h, tx, ty, tw, th)
            except:
                pass

            bboxes[cls_name].append([C.rpn_stride*x/ratio, C.rpn_stride*y/ratio, C.rpn_stride*(x+w)/ratio, C.rpn_stride*(y+