Commit 5c1c3cca authored by I.K Seneviratne's avatar I.K Seneviratne

Merge remote-tracking branch 'origin/QA_RELEASE' into monitoring_student_behavior_IT17138000

parents 1931d23b 8b6b583d
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 imutils
import cv2,os,urllib.request
import numpy as np
from django.conf import settings
face_detection_videocam = cv2.CascadeClassifier(os.path.join(
settings.BASE_DIR,'opencv_haarcascade_data/haarcascade_frontalface_default.xml'))
face_detection_webcam = cv2.CascadeClassifier(os.path.join(
settings.BASE_DIR,'opencv_haarcascade_data/haarcascade_frontalface_default.xml'))
# load our serialized face detector model from disk
prototxtPath = os.path.sep.join([settings.BASE_DIR, "face_detector/deploy.prototxt"])
weightsPath = os.path.sep.join([settings.BASE_DIR,"face_detector/res10_300x300_ssd_iter_140000.caffemodel"])
faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)
maskNet = load_model(os.path.join(settings.BASE_DIR,'face_detector/mask_detector.model'))
# class VideoCamera(object):
# def __init__(self):
# self.video = cv2.VideoCapture(0)
#
# def __del__(self):
# self.video.release()
#
# def get_frame(self):
# success, image = self.video.read()
# # We are using Motion JPEG, but OpenCV defaults to capture raw images,
# # so we must encode it into JPEG in order to correctly display the
# # video stream.
#
# gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# faces_detected = face_detection_videocam.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5)
# for (x, y, w, h) in faces_detected:
# cv2.rectangle(image, pt1=(x, y), pt2=(x + w, y + h), color=(255, 0, 0), thickness=2)
# frame_flip = cv2.flip(image,1)
# ret, jpeg = cv2.imencode('.jpg', frame_flip)
# return jpeg.tobytes()
class IPWebCam(object):
def __init__(self):
self.url = "http://192.168.8.100:8080/shot.jpg"
def __del__(self):
cv2.destroyAllWindows()
def get_frame(self):
imgResp = urllib.request.urlopen(self.url)
imgNp = np.array(bytearray(imgResp.read()),dtype=np.uint8)
img= cv2.imdecode(imgNp,-1)
# We are using Motion JPEG, but OpenCV defaults to capture raw images,
# so we must encode it into JPEG in order to correctly display the
# video stream
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces_detected = face_detection_webcam.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5)
for (x, y, w, h) in faces_detected:
cv2.rectangle(img, pt1=(x, y), pt2=(x + w, y + h), color=(255, 0, 0), thickness=2)
resize = cv2.resize(img, (640, 480), interpolation = cv2.INTER_LINEAR)
frame_flip = cv2.flip(resize,1)
ret, jpeg = cv2.imencode('.jpg', frame_flip)
return jpeg.tobytes()
class MaskDetect(object):
def __init__(self):
self.vs = VideoStream(src=0).start()
def __del__(self):
cv2.destroyAllWindows()
def detect_and_predict_mask(self,frame, faceNet, maskNet):
# grab the dimensions of the frame and then construct a blob
# from it
(h, w) = frame.shape[:2]
blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300),
(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 > 0.5:
# 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, resize it to 224x224, 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:
# for faster inference we'll make batch predictions on *all*
# faces at the same time rather than one-by-one predictions
# in the above `for` loop
faces = np.array(faces, dtype="float32")
preds = maskNet.predict(faces, batch_size=32)
# return a 2-tuple of the face locations and their corresponding
# locations
return (locs, preds)
def get_frame(self):
frame = self.vs.read()
frame = imutils.resize(frame, width=650)
frame = cv2.flip(frame, 1)
# detect faces in the frame and determine if they are wearing a
# face mask or not
(locs, preds) = self.detect_and_predict_mask(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)
# 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)
ret, jpeg = cv2.imencode('.jpg', frame)
return jpeg.tobytes()
# class LiveWebCam(object):
# def __init__(self):
# self.url = cv2.VideoCapture("rtsp://admin:Mumbai@123@203.192.228.175:554/")
#
# def __del__(self):
# cv2.destroyAllWindows()
#
# def get_frame(self):
# success,imgNp = self.url.read()
# resize = cv2.resize(imgNp, (640, 480), interpolation = cv2.INTER_LINEAR)
# ret, jpeg = cv2.imencode('.jpg', resize)
# return jpeg.tobytes()
......@@ -31,18 +31,18 @@
</script>
<script type="text/javascript">
$(document).ready(function() {
$('#initiate_btn').click(function() {
fetch('http://127.0.0.1:8000/attendance/process-initiate-lecture')
.then((res) => res.json())
.then((out) => alert(out.response))
.catch((err) => alert('error: ' + err))
});
})
<script>
function toggleLectureLive() {
var x = document.getElementById("liveStreamLecture");
var y = document.getElementById("liveStreamLectureStartButton");
if (x.style.display === "none") {
x.style.display = "block";
y.style.display = "block";
} else {
x.style.display = "none";
y.style.display = "none";
}
}
</script>
{% endblock %}
......@@ -55,11 +55,19 @@
<div class="text-center">
<div class="card">
<div class="card-header">
<h4 class="card-title">Starting the lecture....</h4>
<h4 class="card-title">Lecture Live</h4>
</div>
<div class="card-body">
<button type="button" class="btn btn-success" id="initiate_btn">Initiate Lecture</button>
<button type="button" class="btn btn-success" id="initiate_btn" onclick="toggleLectureLive()">Show Live Stream</button>
</div>
<div style="vertical-align: middle; border-style: none; background-color: #055270; height: 500px; width: 100%">
<div class="row justify-content-center">
<img id="liveStreamLecture" style="display: none; height: inherit; margin-bottom: -25px;" src="{% url 'webcam_feed' %}">
</div>
<div class="row justify-content-center">
<button style="display: none; width: 70px; height: 70px;" id="liveStreamLectureStartButton" class="btn btn-warning btn-circle"><i class="fas fa-video"></i></button>
</div>
</div>
</div>
</div>
......
......@@ -17,7 +17,7 @@ urlpatterns = [
path('student/', StudentAPIView.as_view()),
path('student/<str:pk>', StudentDetails.as_view()),
url(r'^upload/$', FileView.as_view(), name='file-upload'),
path('webcam_feed', views.webcam_feed, name='webcam_feed'),
# this url will initiate the lecture
url(r'^process-initiate-lecture/$', InitiateLecture.as_view())
]
from django.shortcuts import render
from django.http.response import StreamingHttpResponse
from AttendanceApp.camera import IPWebCam
def initiate_lecture(request):
return render(request, "AttendanceApp/Initiate_lecture.html")
def gen(camera):
while True:
frame = camera.get_frame()
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n\r\n')
def webcam_feed(request):
return StreamingHttpResponse(gen(IPWebCam()),
content_type='multipart/x-mixed-replace; boundary=frame')
\ No newline at end of file
......@@ -29,7 +29,7 @@ def read_file(file_name):
# less_noise = AudioEffectsChain().lowshelf(gain=-12.0, frequency=threshold_l, slope=0.5).highshelf(gain=-12.0, frequency=threshold_h, slope=0.5).limiter(gain=6.0)
# y_cleaned = less_noise(y)
# return y_cleaned
#jijoijij
'''MFCC'''
......
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
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 16
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv4_3_norm_mbox_loc_perm"
type: "Permute"
bottom: "conv4_3_norm_mbox_loc"
top: "conv4_3_norm_mbox_loc_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "conv4_3_norm_mbox_loc_flat"
type: "Flatten"
bottom: "conv4_3_norm_mbox_loc_perm"
top: "conv4_3_norm_mbox_loc_flat"
flatten_param {
axis: 1
}
}
layer {
name: "conv4_3_norm_mbox_conf"
type: "Convolution"
bottom: "conv4_3_norm"
top: "conv4_3_norm_mbox_conf"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 8 # 84
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv4_3_norm_mbox_conf_perm"
type: "Permute"
bottom: "conv4_3_norm_mbox_conf"
top: "conv4_3_norm_mbox_conf_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "conv4_3_norm_mbox_conf_flat"
type: "Flatten"
bottom: "conv4_3_norm_mbox_conf_perm"
top: "conv4_3_norm_mbox_conf_flat"
flatten_param {
axis: 1
}
}
layer {
name: "conv4_3_norm_mbox_priorbox"
type: "PriorBox"
bottom: "conv4_3_norm"
bottom: "data"
top: "conv4_3_norm_mbox_priorbox"
prior_box_param {
min_size: 30.0
max_size: 60.0
aspect_ratio: 2
flip: true
clip: false
variance: 0.1
variance: 0.1
variance: 0.2
variance: 0.2
step: 8
offset: 0.5
}
}
layer {
name: "fc7_mbox_loc"
type: "Convolution"
bottom: "fc7"
top: "fc7_mbox_loc"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 24
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "fc7_mbox_loc_perm"
type: "Permute"
bottom: "fc7_mbox_loc"
top: "fc7_mbox_loc_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "fc7_mbox_loc_flat"
type: "Flatten"
bottom: "fc7_mbox_loc_perm"
top: "fc7_mbox_loc_flat"
flatten_param {
axis: 1
}
}
layer {
name: "fc7_mbox_conf"
type: "Convolution"
bottom: "fc7"
top: "fc7_mbox_conf"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 12 # 126
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "fc7_mbox_conf_perm"
type: "Permute"
bottom: "fc7_mbox_conf"
top: "fc7_mbox_conf_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "fc7_mbox_conf_flat"
type: "Flatten"
bottom: "fc7_mbox_conf_perm"
top: "fc7_mbox_conf_flat"
flatten_param {
axis: 1
}
}
layer {
name: "fc7_mbox_priorbox"
type: "PriorBox"
bottom: "fc7"
bottom: "data"
top: "fc7_mbox_priorbox"
prior_box_param {
min_size: 60.0
max_size: 111.0
aspect_ratio: 2
aspect_ratio: 3
flip: true
clip: false
variance: 0.1
variance: 0.1
variance: 0.2
variance: 0.2
step: 16
offset: 0.5
}
}
layer {
name: "conv6_2_mbox_loc"
type: "Convolution"
bottom: "conv6_2_h"
top: "conv6_2_mbox_loc"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 24
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv6_2_mbox_loc_perm"
type: "Permute"
bottom: "conv6_2_mbox_loc"
top: "conv6_2_mbox_loc_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "conv6_2_mbox_loc_flat"
type: "Flatten"
bottom: "conv6_2_mbox_loc_perm"
top: "conv6_2_mbox_loc_flat"
flatten_param {
axis: 1
}
}
layer {
name: "conv6_2_mbox_conf"
type: "Convolution"
bottom: "conv6_2_h"
top: "conv6_2_mbox_conf"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 12 # 126
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv6_2_mbox_conf_perm"
type: "Permute"
bottom: "conv6_2_mbox_conf"
top: "conv6_2_mbox_conf_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "conv6_2_mbox_conf_flat"
type: "Flatten"
bottom: "conv6_2_mbox_conf_perm"
top: "conv6_2_mbox_conf_flat"
flatten_param {
axis: 1
}
}
layer {
name: "conv6_2_mbox_priorbox"
type: "PriorBox"
bottom: "conv6_2_h"
bottom: "data"
top: "conv6_2_mbox_priorbox"
prior_box_param {
min_size: 111.0
max_size: 162.0
aspect_ratio: 2
aspect_ratio: 3
flip: true
clip: false
variance: 0.1
variance: 0.1
variance: 0.2
variance: 0.2
step: 32
offset: 0.5
}
}
layer {
name: "conv7_2_mbox_loc"
type: "Convolution"
bottom: "conv7_2_h"
top: "conv7_2_mbox_loc"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 24
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv7_2_mbox_loc_perm"
type: "Permute"
bottom: "conv7_2_mbox_loc"
top: "conv7_2_mbox_loc_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "conv7_2_mbox_loc_flat"
type: "Flatten"
bottom: "conv7_2_mbox_loc_perm"
top: "conv7_2_mbox_loc_flat"
flatten_param {
axis: 1
}
}
layer {
name: "conv7_2_mbox_conf"
type: "Convolution"
bottom: "conv7_2_h"
top: "conv7_2_mbox_conf"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 12 # 126
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv7_2_mbox_conf_perm"
type: "Permute"
bottom: "conv7_2_mbox_conf"
top: "conv7_2_mbox_conf_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "conv7_2_mbox_conf_flat"
type: "Flatten"
bottom: "conv7_2_mbox_conf_perm"
top: "conv7_2_mbox_conf_flat"
flatten_param {
axis: 1
}
}
layer {
name: "conv7_2_mbox_priorbox"
type: "PriorBox"
bottom: "conv7_2_h"
bottom: "data"
top: "conv7_2_mbox_priorbox"
prior_box_param {
min_size: 162.0
max_size: 213.0
aspect_ratio: 2
aspect_ratio: 3
flip: true
clip: false
variance: 0.1
variance: 0.1
variance: 0.2
variance: 0.2
step: 64
offset: 0.5
}
}
layer {
name: "conv8_2_mbox_loc"
type: "Convolution"
bottom: "conv8_2_h"
top: "conv8_2_mbox_loc"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 16
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv8_2_mbox_loc_perm"
type: "Permute"
bottom: "conv8_2_mbox_loc"
top: "conv8_2_mbox_loc_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "conv8_2_mbox_loc_flat"
type: "Flatten"
bottom: "conv8_2_mbox_loc_perm"
top: "conv8_2_mbox_loc_flat"
flatten_param {
axis: 1
}
}
layer {
name: "conv8_2_mbox_conf"
type: "Convolution"
bottom: "conv8_2_h"
top: "conv8_2_mbox_conf"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 8 # 84
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv8_2_mbox_conf_perm"
type: "Permute"
bottom: "conv8_2_mbox_conf"
top: "conv8_2_mbox_conf_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "conv8_2_mbox_conf_flat"
type: "Flatten"
bottom: "conv8_2_mbox_conf_perm"
top: "conv8_2_mbox_conf_flat"
flatten_param {
axis: 1
}
}
layer {
name: "conv8_2_mbox_priorbox"
type: "PriorBox"
bottom: "conv8_2_h"
bottom: "data"
top: "conv8_2_mbox_priorbox"
prior_box_param {
min_size: 213.0
max_size: 264.0
aspect_ratio: 2
flip: true
clip: false
variance: 0.1
variance: 0.1
variance: 0.2
variance: 0.2
step: 100
offset: 0.5
}
}
layer {
name: "conv9_2_mbox_loc"
type: "Convolution"
bottom: "conv9_2_h"
top: "conv9_2_mbox_loc"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 16
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv9_2_mbox_loc_perm"
type: "Permute"
bottom: "conv9_2_mbox_loc"
top: "conv9_2_mbox_loc_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "conv9_2_mbox_loc_flat"
type: "Flatten"
bottom: "conv9_2_mbox_loc_perm"
top: "conv9_2_mbox_loc_flat"
flatten_param {
axis: 1
}
}
layer {
name: "conv9_2_mbox_conf"
type: "Convolution"
bottom: "conv9_2_h"
top: "conv9_2_mbox_conf"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 8 # 84
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv9_2_mbox_conf_perm"
type: "Permute"
bottom: "conv9_2_mbox_conf"
top: "conv9_2_mbox_conf_perm"
permute_param {
order: 0
order: 2
order: 3
order: 1
}
}
layer {
name: "conv9_2_mbox_conf_flat"
type: "Flatten"
bottom: "conv9_2_mbox_conf_perm"
top: "conv9_2_mbox_conf_flat"
flatten_param {
axis: 1
}
}
layer {
name: "conv9_2_mbox_priorbox"
type: "PriorBox"
bottom: "conv9_2_h"
bottom: "data"
top: "conv9_2_mbox_priorbox"
prior_box_param {
min_size: 264.0
max_size: 315.0
aspect_ratio: 2
flip: true
clip: false
variance: 0.1
variance: 0.1
variance: 0.2
variance: 0.2
step: 300
offset: 0.5
}
}
layer {
name: "mbox_loc"
type: "Concat"
bottom: "conv4_3_norm_mbox_loc_flat"
bottom: "fc7_mbox_loc_flat"
bottom: "conv6_2_mbox_loc_flat"
bottom: "conv7_2_mbox_loc_flat"
bottom: "conv8_2_mbox_loc_flat"
bottom: "conv9_2_mbox_loc_flat"
top: "mbox_loc"
concat_param {
axis: 1
}
}
layer {
name: "mbox_conf"
type: "Concat"
bottom: "conv4_3_norm_mbox_conf_flat"
bottom: "fc7_mbox_conf_flat"
bottom: "conv6_2_mbox_conf_flat"
bottom: "conv7_2_mbox_conf_flat"
bottom: "conv8_2_mbox_conf_flat"
bottom: "conv9_2_mbox_conf_flat"
top: "mbox_conf"
concat_param {
axis: 1
}
}
layer {
name: "mbox_priorbox"
type: "Concat"
bottom: "conv4_3_norm_mbox_priorbox"
bottom: "fc7_mbox_priorbox"
bottom: "conv6_2_mbox_priorbox"
bottom: "conv7_2_mbox_priorbox"
bottom: "conv8_2_mbox_priorbox"
bottom: "conv9_2_mbox_priorbox"
top: "mbox_priorbox"
concat_param {
axis: 2
}
}
layer {
name: "mbox_conf_reshape"
type: "Reshape"
bottom: "mbox_conf"
top: "mbox_conf_reshape"
reshape_param {
shape {
dim: 0
dim: -1
dim: 2
}
}
}
layer {
name: "mbox_conf_softmax"
type: "Softmax"
bottom: "mbox_conf_reshape"
top: "mbox_conf_softmax"
softmax_param {
axis: 2
}
}
layer {
name: "mbox_conf_flatten"
type: "Flatten"
bottom: "mbox_conf_softmax"
top: "mbox_conf_flatten"
flatten_param {
axis: 1
}
}
layer {
name: "detection_out"
type: "DetectionOutput"
bottom: "mbox_loc"
bottom: "mbox_conf_flatten"
bottom: "mbox_priorbox"
top: "detection_out"
include {
phase: TEST
}
detection_output_param {
num_classes: 2
share_location: true
background_label_id: 0
nms_param {
nms_threshold: 0.45
top_k: 400
}
code_type: CENTER_SIZE
keep_top_k: 200
confidence_threshold: 0.01
}
}
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