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Story
"Prevention is better than cure" is one of the effective measures to prevent the spreading of COVID-19 and to protect mankind. Many researchers and doctors are working on medication and vaccination for corona.
COVID-19 spreads mostly by droplet infection when people cough or if we touch someone who is ill and then to our face (i.e rubbing eyes or nose). Ongoing pandemic shows that it is much more contagious and spreads fast. Depending on the infection spreading, we have two cases: Fast and Slow spread.
A fast pandemic will be terrible and will cost many lives. It occurs due to a rapid rate of infection because there are no countermeasures to slow it down. This is because, if the numbers of infected people get too large, healthcare systems become unable to handle it. We will lack resources such as medical staff or equipment like a ventilator.
To avoid the above situation, we need to do what we can to turn this into a slow pandemic. A pandemic can be slowed down only by the right responses, mainly in the early phase. In this phase, everyone who is sick can get treatment and there is no emergency point with flooded hospitals.
In this pandemic, we need to engineer our behavior as a vaccine. that is, "Not getting infected" and "Not infecting others". The best thing we can do is to wash our hands with soap or a hand sanitizer. The next best thing is social distancing.
To avoid getting infected or spreading it, It is essential to wear a face mask while going out from home especially to public places such as markets or hospitals.
About The Project
The system is designed to detect the faces and to determine whether the person wears a face mask or not. Using the above data, we can decide whether the concerned person can be allowed inside public places such as the market, or a hospital. This project can be used in the hospital, market, bus terminals, restaurants, and other public gatherings where the monitoring has to be done.
This project consists of a camera that will capture the image of the people entering public places and detect whether the person wears a face mask or not using their facial features.
The FaceMask Detection project was developed at the National Institute of Applied Science and Technology (INSAT) in September 2020.
Visit my personal portfolio: karembenchikha.me
Check the full Project on GitHub: https://github.com/KaremBenChikha/FaceMaskDetection/
A quick demo of the project (I used a simple green LED to simulate a door opening):
To build such a project, you have to follow 3 main steps:
Step 1
You will create a neural network model with TensorFlow and will train it on a dataset of both people who are wearing facemasks and people who are not.
The dataset can be downloaded from here: https://drive.google.com/file/d/1uKAOauSUqH6wtk_wfpSsjNpju1ralPOI/view?usp=sharing
Note that the algorithm runs on Jupyter notebooks and requires a lot of GPU power to train the model. However, if you execute my code without changing the Model settings, I can guarantee total confidence of 98%.
Step 2
Here, you will create a face recognition algorithm that will be able to detect facemasks on people's faces using the trained model in the previous step.
if you don't have the GPU power or the needed dependencies or knowledge to work with neural network models. I have included my pre-trained model that can be used in this step without going by step 1. name of the model fil: mask_detector.model
Step 3
Finally, you will add a simple Serial Command to the facemask detection algorithm that will order the Arduino to switch LED on or off based on the state of detection.
(you must also deploy the Arduino code on your Ardunio and do the correct wiring)
This project needs the following libraries:
TensorFlow - Keras - imutils - numpy - opencv - matplotlib - scipy - argparse - pyserial
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"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"#Import Libraries"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
"from tensorflow.keras.applications import MobileNetV2\n",
"from tensorflow.keras.layers import AveragePooling2D\n",
"from tensorflow.keras.layers import Dropout\n",
"from tensorflow.keras.layers import Flatten\n",
"from tensorflow.keras.layers import Dense\n",
"from tensorflow.keras.layers import Input\n",
"from tensorflow.keras.models import Model\n",
"from tensorflow.keras.optimizers import Adam\n",
"from tensorflow.keras.applications.mobilenet_v2 import preprocess_input\n",
"from tensorflow.keras.preprocessing.image import img_to_array\n",
"from tensorflow.keras.preprocessing.image import load_img\n",
"from tensorflow.keras.utils import to_categorical\n",
"from sklearn.preprocessing import LabelBinarizer\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import classification_report\n",
"from imutils import paths\n",
"import matplotlib.pyplot as plot \n",
"import numpy as np \n",
"import os "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"#Initialization with 20 Epochs and 32 Batches and set images' path"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"initLayer = 1e-4\n",
"epochs = 5\n",
"batch = 32"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"#SettingUp Image Data"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"directory = \"/home/karem/Documents/Python/FaceMaskDetection/dataset\"\n",
"categories = [\"withMask\", \"withoutMask\"]\n",
"data = []\n",
"labels = []\n",
"for category in categories:\n",
" path = os.path.join(directory, category)\n",
" for img in os.listdir(path):\n",
" \timg_path = os.path.join(path, img)\n",
" \timage = load_img(img_path, target_size=(224, 224))\n",
" \timage = img_to_array(image)\n",
" \timage = preprocess_input(image)\n",
" \tdata.append(image)\n",
" \tlabels.append(category)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"#Encode Data and Labels"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"lb = LabelBinarizer()\n",
"labels = lb.fit_transform(labels)\n",
"labels = to_categorical(labels)\n",
"data = np.array(data,dtype=\"float32\")\n",
"labels = np.array(labels)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"#Set 80% of Images for training and 20% for testing"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"(trainX,testX,trainY,testY) = train_test_split(data,labels,test_size=0.20,stratify=labels,random_state=20)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"aug = ImageDataGenerator(\n",
"\trotation_range=20,\n",
"\tzoom_range=0.15,\n",
"\twidth_shift_range=0.2,\n",
"\theight_shift_range=0.2,\n",
"\tshear_range=0.15,\n",
"\thorizontal_flip=True,\n",
"\tfill_mode=\"nearest\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"#Load MibileNetV2 network"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
}
],
"source": [
"baseModel = MobileNetV2(weights=\"imagenet\", include_top=False,\n",
"\tinput_tensor=Input(shape=(224, 224, 3)))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"#Construct the head module"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"headModel = baseModel.output\n",
"headModel = AveragePooling2D(pool_size=(7, 7))(headModel)\n",
"headModel = Flatten(name=\"flatten\")(headModel)\n",
"headModel = Dense(128, activation=\"relu\")(headModel)\n",
"headModel = Dropout(0.5)(headModel)\n",
"headModel = Dense(2, activation=\"softmax\")(headModel)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# Pace the head FC model on top of the base model (this will become the actual model we will train)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"model = Model(inputs=baseModel.input, outputs=headModel)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"#Loop over all layers in the base model and freeze them so they will not be updated during the first training process"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"for layer in baseModel.layers:\n",
"\tlayer.trainable = False"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"#Compile the neural network Module"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"opt = Adam(lr=initLayer, decay=initLayer / epochs)\n",
"model.compile(loss=\"binary_crossentropy\", optimizer=opt,\n",
"\tmetrics=[\"accuracy\"])"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"#Train the head of the module"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "Epoch 1/5\n95/95 [==============================] - 98s 1s/step - loss: 0.2674 - accuracy: 0.8813 - val_loss: 0.0824 - val_accuracy: 0.9752\nEpoch 2/5\n95/95 [==============================] - 98s 1s/step - loss: 0.1011 - accuracy: 0.9667 - val_loss: 0.0577 - val_accuracy: 0.9831\nEpoch 3/5\n95/95 [==============================] - 96s 1s/step - loss: 0.0831 - accuracy: 0.9726 - val_loss: 0.0477 - val_accuracy: 0.9844\nEpoch 4/5\n95/95 [==============================] - 99s 1s/step - loss: 0.0648 - accuracy: 0.9763 - val_loss: 0.0381 - val_accuracy: 0.9844\nEpoch 5/5\n95/95 [==============================] - 97s 1s/step - loss: 0.0554 - accuracy: 0.9806 - val_loss: 0.0310 - val_accuracy: 0.9870\n"
}
],
"source": [
"H = model.fit(aug.flow(trainX, trainY, batch_size=batch),\n",
"\tsteps_per_epoch=len(trainX) // batch,\n",
"\tvalidation_data=(testX, testY),\n",
"\tvalidation_steps=len(testX) // batch,\n",
"\tepochs=epochs)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"#Serialize the model to disk"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"model.save(\"mask_detector.model\", save_format=\"h5\")"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"# plot the training loss and accuracy\n"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
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\n"
},
"metadata": {}
}
],
"source": [
"N = epochs\n",
"plot.style.use(\"ggplot\")\n",
"plot.figure()\n",
"plot.plot(np.arange(0, N), H.history[\"loss\"], label=\"train_loss\")\n",
"plot.plot(np.arange(0, N), H.history[\"val_loss\"], label=\"val_loss\")\n",
"plot.plot(np.arange(0, N), H.history[\"accuracy\"], label=\"train_acc\")\n",
"plot.plot(np.arange(0, N), H.history[\"val_accuracy\"], label=\"val_acc\")\n",
"plot.title(\"Training Loss and Accuracy\")\n",
"plot.xlabel(\"Epoch #\")\n",
"plot.ylabel(\"Loss/Accuracy\")\n",
"plot.legend(loc=\"lower left\")\n",
"plot.savefig(\"plot.png\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
]
}
# import the necessary packages
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.models import load_model
from imutils.video import VideoStream
import numpy as np
import imutils
import time
import cv2
import os
import serial
import time
#Setting up your arduino
arduino = serial.Serial('/dev/ttyUSB0',9600)
lowConfidence = 0.75
#face detectinon function
def detectAndPredictMask(frame, faceNet, maskNet):
(h, w) = frame.shape[:2]
blob = cv2.dnn.blobFromImage(frame, 1.0, (224, 224),
(104.0, 177.0, 123.0))
# pass the blob through the network and obtain the face detections
faceNet.setInput(blob)
detections = faceNet.forward()
# initialize our list of faces, their corresponding locations and the list of predictions from our face mask network
faces = []
locs = []
preds = []
# loop over the detections
for i in range(0, detections.shape[2]):
# extract the confidence (i.e., probability) associated with the detection
confidence = detections[0, 0, i, 2]
# filter out weak detections by ensuring the confidence is greater than the minimum confidence
if confidence > lowConfidence:
# compute the (x, y)-coordinates of the bounding box for the object
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
# ensure the bounding boxes fall within the dimensions of the frame
(startX, startY) = (max(0, startX), max(0, startY))
(endX, endY) = (min(w - 1, endX), min(h - 1, endY))
# extract the face ROI, convert it from BGR to RGB channel ordering, and preprocess it
face = frame[startY:endY, startX:endX]
face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
face = cv2.resize(face, (224, 224))
face = img_to_array(face)
face = preprocess_input(face)
# add the face and bounding boxes to their respective lists
faces.append(face)
locs.append((startX, startY, endX, endY))
# only make a predictions if at least one face was detected
if len(faces) > 0:
faces = np.array(faces, dtype="float32")
preds = maskNet.predict(faces, batch_size=32)
# return a 2-tuple of the face locations and their corresponding
return (locs, preds)
# load our serialized face detector model from disk
prototxtPath = r"deploy.prototxt"
weightsPath = r"res10_300x300_ssd_iter_140000.caffemodel"
faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)
# load the face mask detector model from disk
maskNet = load_model("mask_detector.model")
# initialize the video stream
vs = VideoStream(src=0).start()
# loop over the frames from the video stream
while True:
# grab the frame from the threaded video stream and resize it to have a maximum width of 900 pixels
frame = vs.read()
frame = imutils.resize(frame, width=900)
# detect faces in the frame and determine if they are wearing a face mask or not
(locs, preds) = detectAndPredictMask(frame, faceNet, maskNet)
# loop over the detected face locations and their corresponding locations
for (box, pred) in zip(locs, preds):
# unpack the bounding box and predictions
(startX, startY, endX, endY) = box
(mask, withoutMask) = pred
# determine the class label and color we'll use to draw the bounding box and text
label = "Mask" if mask > withoutMask else "No Mask"
color = (0, 255, 0) if label == "Mask" else (0, 0, 255)
if label =="Mask":
print("ACCESS GRANTED")
arduino.write(b'H')
else:
print("ACCESS DENIED")
arduino.write(b'L')
# include the probability in the label
label = "{}: {:.2f}%".format(label, max(mask, withoutMask) * 100)
# display the label and bounding box rectangle on the output frame
cv2.putText(frame, label, (startX, startY - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)
# show the output frame
cv2.imshow("FaceMask Detection by KAREM -- q to quit", frame)
key = cv2.waitKey(1) & 0xFF
# if the `q` key was pressed, break from the loop
if key == ord("q"):
break
# do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()
input: "data"
input_shape {
dim: 1
dim: 3
dim: 300
dim: 300
}
layer {
name: "data_bn"
type: "BatchNorm"
bottom: "data"
top: "data_bn"
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
}
layer {
name: "data_scale"
type: "Scale"
bottom: "data_bn"
top: "data_bn"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 1.0
}
scale_param {
bias_term: true
}
}
layer {
name: "conv1_h"
type: "Convolution"
bottom: "data_bn"
top: "conv1_h"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 1.0
}
convolution_param {
num_output: 32
pad: 3
kernel_size: 7
stride: 2
weight_filler {
type: "msra"
variance_norm: FAN_OUT
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv1_bn_h"
type: "BatchNorm"
bottom: "conv1_h"
top: "conv1_h"
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
}
layer {
name: "conv1_scale_h"
type: "Scale"
bottom: "conv1_h"
top: "conv1_h"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 1.0
}
scale_param {
bias_term: true
}
}
layer {
name: "conv1_relu"
type: "ReLU"
bottom: "conv1_h"
top: "conv1_h"
}
layer {
name: "conv1_pool"
type: "Pooling"
bottom: "conv1_h"
top: "conv1_pool"
pooling_param {
kernel_size: 3
stride: 2
}
}
layer {
name: "layer_64_1_conv1_h"
type: "Convolution"
bottom: "conv1_pool"
top: "layer_64_1_conv1_h"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 32
bias_term: false
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "layer_64_1_bn2_h"
type: "BatchNorm"
bottom: "layer_64_1_conv1_h"
top: "layer_64_1_conv1_h"
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
}
layer {
name: "layer_64_1_scale2_h"
type: "Scale"
bottom: "layer_64_1_conv1_h"
top: "layer_64_1_conv1_h"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 1.0
}
scale_param {
bias_term: true
}
}
layer {
name: "layer_64_1_relu2"
type: "ReLU"
bottom: "layer_64_1_conv1_h"
top: "layer_64_1_conv1_h"
}
layer {
name: "layer_64_1_conv2_h"
type: "Convolution"
bottom: "layer_64_1_conv1_h"
top: "layer_64_1_conv2_h"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 32
bias_term: false
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "layer_64_1_sum"
type: "Eltwise"
bottom: "layer_64_1_conv2_h"
bottom: "conv1_pool"
top: "layer_64_1_sum"
}
layer {
name: "layer_128_1_bn1_h"
type: "BatchNorm"
bottom: "layer_64_1_sum"
top: "layer_128_1_bn1_h"
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
}
layer {
name: "layer_128_1_scale1_h"
type: "Scale"
bottom: "layer_128_1_bn1_h"
top: "layer_128_1_bn1_h"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 1.0
}
scale_param {
bias_term: true
}
}
layer {
name: "layer_128_1_relu1"
type: "ReLU"
bottom: "layer_128_1_bn1_h"
top: "layer_128_1_bn1_h"
}
layer {
name: "layer_128_1_conv1_h"
type: "Convolution"
bottom: "layer_128_1_bn1_h"
top: "layer_128_1_conv1_h"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 128
bias_term: false
pad: 1
kernel_size: 3
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "layer_128_1_bn2"
type: "BatchNorm"
bottom: "layer_128_1_conv1_h"
top: "layer_128_1_conv1_h"
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
}
layer {
name: "layer_128_1_scale2"
type: "Scale"
bottom: "layer_128_1_conv1_h"
top: "layer_128_1_conv1_h"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 1.0
}
scale_param {
bias_term: true
}
}
layer {
name: "layer_128_1_relu2"
type: "ReLU"
bottom: "layer_128_1_conv1_h"
top: "layer_128_1_conv1_h"
}
layer {
name: "layer_128_1_conv2"
type: "Convolution"
bottom: "layer_128_1_conv1_h"
top: "layer_128_1_conv2"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 128
bias_term: false
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "layer_128_1_conv_expand_h"
type: "Convolution"
bottom: "layer_128_1_bn1_h"
top: "layer_128_1_conv_expand_h"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 128
bias_term: false
pad: 0
kernel_size: 1
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "layer_128_1_sum"
type: "Eltwise"
bottom: "layer_128_1_conv2"
bottom: "layer_128_1_conv_expand_h"
top: "layer_128_1_sum"
}
layer {
name: "layer_256_1_bn1"
type: "BatchNorm"
bottom: "layer_128_1_sum"
top: "layer_256_1_bn1"
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
}
layer {
name: "layer_256_1_scale1"
type: "Scale"
bottom: "layer_256_1_bn1"
top: "layer_256_1_bn1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 1.0
}
scale_param {
bias_term: true
}
}
layer {
name: "layer_256_1_relu1"
type: "ReLU"
bottom: "layer_256_1_bn1"
top: "layer_256_1_bn1"
}
layer {
name: "layer_256_1_conv1"
type: "Convolution"
bottom: "layer_256_1_bn1"
top: "layer_256_1_conv1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 256
bias_term: false
pad: 1
kernel_size: 3
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "layer_256_1_bn2"
type: "BatchNorm"
bottom: "layer_256_1_conv1"
top: "layer_256_1_conv1"
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
}
layer {
name: "layer_256_1_scale2"
type: "Scale"
bottom: "layer_256_1_conv1"
top: "layer_256_1_conv1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 1.0
}
scale_param {
bias_term: true
}
}
layer {
name: "layer_256_1_relu2"
type: "ReLU"
bottom: "layer_256_1_conv1"
top: "layer_256_1_conv1"
}
layer {
name: "layer_256_1_conv2"
type: "Convolution"
bottom: "layer_256_1_conv1"
top: "layer_256_1_conv2"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 256
bias_term: false
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "layer_256_1_conv_expand"
type: "Convolution"
bottom: "layer_256_1_bn1"
top: "layer_256_1_conv_expand"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 256
bias_term: false
pad: 0
kernel_size: 1
stride: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "layer_256_1_sum"
type: "Eltwise"
bottom: "layer_256_1_conv2"
bottom: "layer_256_1_conv_expand"
top: "layer_256_1_sum"
}
layer {
name: "layer_512_1_bn1"
type: "BatchNorm"
bottom: "layer_256_1_sum"
top: "layer_512_1_bn1"
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
}
layer {
name: "layer_512_1_scale1"
type: "Scale"
bottom: "layer_512_1_bn1"
top: "layer_512_1_bn1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 1.0
}
scale_param {
bias_term: true
}
}
layer {
name: "layer_512_1_relu1"
type: "ReLU"
bottom: "layer_512_1_bn1"
top: "layer_512_1_bn1"
}
layer {
name: "layer_512_1_conv1_h"
type: "Convolution"
bottom: "layer_512_1_bn1"
top: "layer_512_1_conv1_h"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 128
bias_term: false
pad: 1
kernel_size: 3
stride: 1 # 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "layer_512_1_bn2_h"
type: "BatchNorm"
bottom: "layer_512_1_conv1_h"
top: "layer_512_1_conv1_h"
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
}
layer {
name: "layer_512_1_scale2_h"
type: "Scale"
bottom: "layer_512_1_conv1_h"
top: "layer_512_1_conv1_h"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 1.0
}
scale_param {
bias_term: true
}
}
layer {
name: "layer_512_1_relu2"
type: "ReLU"
bottom: "layer_512_1_conv1_h"
top: "layer_512_1_conv1_h"
}
layer {
name: "layer_512_1_conv2_h"
type: "Convolution"
bottom: "layer_512_1_conv1_h"
top: "layer_512_1_conv2_h"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 256
bias_term: false
pad: 2 # 1
kernel_size: 3
stride: 1
dilation: 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "layer_512_1_conv_expand_h"
type: "Convolution"
bottom: "layer_512_1_bn1"
top: "layer_512_1_conv_expand_h"
param {
lr_mult: 1.0
decay_mult: 1.0
}
convolution_param {
num_output: 256
bias_term: false
pad: 0
kernel_size: 1
stride: 1 # 2
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "layer_512_1_sum"
type: "Eltwise"
bottom: "layer_512_1_conv2_h"
bottom: "layer_512_1_conv_expand_h"
top: "layer_512_1_sum"
}
layer {
name: "last_bn_h"
type: "BatchNorm"
bottom: "layer_512_1_sum"
top: "layer_512_1_sum"
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
}
layer {
name: "last_scale_h"
type: "Scale"
bottom: "layer_512_1_sum"
top: "layer_512_1_sum"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 1.0
}
scale_param {
bias_term: true
}
}
layer {
name: "last_relu"
type: "ReLU"
bottom: "layer_512_1_sum"
top: "fc7"
}
layer {
name: "conv6_1_h"
type: "Convolution"
bottom: "fc7"
top: "conv6_1_h"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv6_1_relu"
type: "ReLU"
bottom: "conv6_1_h"
top: "conv6_1_h"
}
layer {
name: "conv6_2_h"
type: "Convolution"
bottom: "conv6_1_h"
top: "conv6_2_h"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
stride: 2
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv6_2_relu"
type: "ReLU"
bottom: "conv6_2_h"
top: "conv6_2_h"
}
layer {
name: "conv7_1_h"
type: "Convolution"
bottom: "conv6_2_h"
top: "conv7_1_h"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv7_1_relu"
type: "ReLU"
bottom: "conv7_1_h"
top: "conv7_1_h"
}
layer {
name: "conv7_2_h"
type: "Convolution"
bottom: "conv7_1_h"
top: "conv7_2_h"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
stride: 2
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv7_2_relu"
type: "ReLU"
bottom: "conv7_2_h"
top: "conv7_2_h"
}
layer {
name: "conv8_1_h"
type: "Convolution"
bottom: "conv7_2_h"
top: "conv8_1_h"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv8_1_relu"
type: "ReLU"
bottom: "conv8_1_h"
top: "conv8_1_h"
}
layer {
name: "conv8_2_h"
type: "Convolution"
bottom: "conv8_1_h"
top: "conv8_2_h"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv8_2_relu"
type: "ReLU"
bottom: "conv8_2_h"
top: "conv8_2_h"
}
layer {
name: "conv9_1_h"
type: "Convolution"
bottom: "conv8_2_h"
top: "conv9_1_h"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv9_1_relu"
type: "ReLU"
bottom: "conv9_1_h"
top: "conv9_1_h"
}
layer {
name: "conv9_2_h"
type: "Convolution"
bottom: "conv9_1_h"
top: "conv9_2_h"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv9_2_relu"
type: "ReLU"
bottom: "conv9_2_h"
top: "conv9_2_h"
}
layer {
name: "conv4_3_norm"
type: "Normalize"
bottom: "layer_256_1_bn1"
top: "conv4_3_norm"
norm_param {
across_spatial: false
scale_filler {
type: "constant"
value: 20
}
channel_shared: false
}
}
layer {
name: "conv4_3_norm_mbox_loc"
type: "Convolution"
bottom: "conv4_3_norm"
top: "conv4_3_norm_mbox_loc"
param {
lr_mult: 1
decay_mult: 1
...
This file has been truncated, please download it to see its full contents.
int inByte = 0; // initialize the variable inByte
const int ledPin = 13; // pin that the LED is attached to
void setup(){
pinMode(ledPin, OUTPUT); // initialize the LED pin as an output
Serial.begin(57600); // set serial monitor to same speed
}
void loop(){
if (Serial.available()>0) { // check if any data received
inByte = Serial.read(); // yes, so read it from incoming buffer
if (inByte == 1){
digitalWrite(ledPin, HIGH);
}
else {
digitalWrite(ledPin,LOW);
}
}
}
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