def start(self):
self.location = self.CCTVOption.set_location
if self.location is None:
QMessageBox.about(self, "정보", "위치를 입력해주세요.")
return self.exec_setting()
try:
self.setCamera = cv2.VideoCapture(0)
_, self.img_o = self.setCamera.read()
self.img_o = cv2.cvtColor(self.img_o, cv2.COLOR_RGB2GRAY)
except:
QMessageBox.about(self, "정보", "카메라를 확인해주세요.")
return
self.main_record = True
self.event_record = False
self.event_switch = False
self.event_status = False
self.timer = QTimer()
self.timer.timeout.connect(self.nextframe)
self.timer.start(1000 / self.fps)
port = 3030
require_login = False
self.streamer = Streamer(port, require_login)
def main() -> None:
"""
The main launcher logic, using a cuda streamer to stream from an IP camera
Arguments:
None
Returns:
None
"""
cuda_streamer = CUDAStreamer(
"rtsp://*****:*****@10.199.51.222/axis-media/media.amp?streamprofile=H264-OpenCV-Optimized",
1920, 1080)
streamer = Streamer(3030, False, (1920, 1080), frame_rate=30)
while True:
frame = cuda_streamer.get_image()
streamer.update_frame(frame)
if not streamer.is_streaming:
streamer.start_streaming()
from flask_opencv_streamer.streamer import Streamer
from cv2 import VideoCapture, waitKey
port = 5000
require_login = False
streamer = Streamer(port, require_login)
cap = VideoCapture(0)
print('Starting to read camera and host flask in port:', port)
while True:
success, frame = cap.read()
streamer.update_frame(frame)
if not streamer.is_streaming:
streamer.start_streaming()
waitKey(66)
def vision():
src = int(args['source']) if args['source'].isdigit() else args['source']
flip = args['flip']
rotate = args['rotate']
view = args['view']
debug = args['debug']
threshold = args['threshold'] if args['threshold'] < 50 else 0
angle_threshold = args['athreshold'] if 0 < args['athreshold'] < 30 else 0
filter_threshold = args[
'fthreshold'] if 0 < args['fthreshold'] <= 0.8 else 0.5
net_table = args['networktables']
is_pi = args['pi']
crash = args['crash']
window_moved = False
sequence = False
frame = []
def capture_frame(frame, table, value):
if value:
cv2.imwrite('/tmp/gv_frame.jpg', frame),
table.putBoolean('capture_frame', False)
cap = cv2.VideoCapture(src)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, data['image-width'])
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, data['image-height'])
port1 = 3030
streamer = Streamer(port1, False)
if net_table:
nt.NetworkTables.initialize(server=data['server-ip'])
camera_table = nt.NetworkTables.getTable("CameraPublisher")
sub_table = camera_table.getSubTable("Camera")
sub_table.getEntry("streams").setStringArray(
["mjpg:http://10.18.16.16:3030/video_feed"])
table = nt.NetworkTables.getTable('SmartDashboard')
if table:
print('table OK')
table.putNumber('center_x', -1)
table.putNumber('center_y', -1)
table.putNumber('contours', -1)
table.putNumber('targets', -1)
table.putNumber('width', data['image-width'])
table.putNumber('height', data['image-height'])
table.putBoolean('capture_frame', False)
table.addEntryListener(lambda table, key, value, isNew: capture_frame(
frame, table, value),
key='capture_frame')
# values = {'vision_active': False}
# table.addEntryListener(value_changed, key='vision_active')
if debug:
print(
'----------------------------------------------------------------')
print('Current Source: {}'.format(src))
print('View Flag: {}'.format(view))
print('Debug Flag: {}'.format(debug))
print('Threshold Value: {}'.format(threshold))
print('Angle Threshold Value: {}'.format(angle_threshold))
print('Network Tables Flag: {}'.format(net_table))
print(
'----------------------------------------------------------------\n'
)
v_focal_length = data['camera_matrix'][1][1]
h_focal_length = data['camera_matrix'][0][0]
lower_color = np.array([
data['lower-color-list'][0] - threshold, data['lower-color-list'][1],
data['lower-color-list'][2]
]) # HSV to test: 0, 220, 25
upper_color = np.array([
data['upper-color-list'][0] + threshold, data['upper-color-list'][1],
data['upper-color-list'][2]
])
center_coords = (int(data['image-width'] / 2),
int(data['image-height'] / 2))
screen_c_x = data['image-width'] / 2 - 0.5
screen_c_y = data['image-height'] / 2 - 0.5
first_read = True
rectangle_list = []
sorted_contours = []
average_coord_list = []
append = average_coord_list.append
while True:
fps = FPS().start()
if crash:
raise Exception('Get bamboozled...')
start_time = time.time()
biggest_contour_area = -1
best_center_average_coords = (-1, -1)
index = -1
pitch = -999
yaw = -999
if view:
if not first_read:
key = cv2.waitKey(30) & 0xFF
if key == ord('q'):
break
if sequence and key != ord(' '):
continue
first_read = False
_, frame = cap.read()
if frame is None:
continue
if flip:
frame = cv2.flip(frame, -1)
if rotate:
frame = imutils.rotate_bound(frame, 90)
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv, lower_color, upper_color)
# find contours from mask
all_contours, _ = cv2.findContours(mask, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
# remove super small or super big contours that exist due to light noise/objects
filtered_contours = [
c for c in all_contours if 50 < cv2.contourArea(c) < 15000
]
filtered_contours_area = [
cv2.contourArea(c) for c in all_contours if 50 < cv2.contourArea(c)
]
# find the contour with the biggest area so we can further remove contours created from light noise
if len(all_contours) > 0:
biggest_contour_area = max(
[cv2.contourArea(c) for c in all_contours])
# create a contour list that removes contours smaller than the biggest * some constant
filtered_contours = [
c for c in filtered_contours
if cv2.contourArea(c) > filter_threshold * biggest_contour_area
]
# sort contours by left to right, top to bottom
if len(filtered_contours) > 1:
bounding_boxes = [cv2.boundingRect(c) for c in filtered_contours]
sorted_contours, _ = zip(
*sorted(zip(filtered_contours, bounding_boxes),
key=lambda b: b[1][0],
reverse=False))
sorted_contours = list(sorted_contours)
if len(sorted_contours) > 1:
# gets ((cx, cy), (width, height), angle of rot) for each contour
rectangle_list = [cv2.minAreaRect(c) for c in sorted_contours]
for pos, rect in enumerate(rectangle_list):
if biggest_contour_area < 10000:
if -78 - angle_threshold < rect[
2] < -74 + angle_threshold and pos != len(
rectangle_list) - 1:
if view:
color = (0, 255, 255)
box = np.int0(cv2.boxPoints(rect))
cv2.drawContours(frame, [box], 0, color, 2)
# only add rect if the second rect is the correct angle
if -16 - angle_threshold < rectangle_list[
pos + 1][2] < -12 + angle_threshold:
if view:
color = (0, 0, 255)
rect2 = rectangle_list[pos + 1]
box2 = np.int0(cv2.boxPoints(rect2))
cv2.drawContours(frame, [box2], 0, color, 2)
cx = int(
(rect[0][0] + rectangle_list[pos + 1][0][0]) /
2)
cy = int(
(rect[0][1] + rectangle_list[pos + 1][0][1]) /
2)
append((cx, cy))
else:
if pos != len(rectangle_list) - 1:
if view:
color = (0, 255, 255)
box = np.int0(cv2.boxPoints(rect))
cv2.drawContours(frame, [box], 0, color, 2)
rect2 = rectangle_list[pos + 1]
box2 = np.int0(cv2.boxPoints(rect2))
color = (255, 255, 0)
cv2.drawContours(frame, [box2], 0, color, 2)
cx = int(
(rect[0][0] + rectangle_list[pos + 1][0][0]) / 2)
cy = int(
(rect[0][1] + rectangle_list[pos + 1][0][1]) / 2)
append((cx, cy))
if len(average_coord_list) == 1:
best_center_average_coords = average_coord_list[index]
index = 0
yaw = math.degrees(
math.atan((best_center_average_coords[0] - screen_c_x) /
h_focal_length))
pitch = math.degrees(
math.atan((best_center_average_coords[1] - screen_c_y) /
v_focal_length))
if view:
cv2.line(frame, best_center_average_coords, center_coords,
(0, 255, 0), 2)
cv2.line(frame, best_center_average_coords,
best_center_average_coords, (255, 0, 0), 5)
elif len(average_coord_list) > 1:
# finds c_x that is closest to the center of the center
best_center_average_x = min(
average_coord_list,
key=lambda xy: abs(xy[0] - data['image-width'] / 2))[0]
index = [coord[0] for coord in average_coord_list
].index(best_center_average_x)
best_center_average_y = average_coord_list[index][1]
best_center_average_coords = (best_center_average_x,
best_center_average_y)
yaw = math.degrees(
math.atan((best_center_average_coords[0] - screen_c_x) /
h_focal_length))
pitch = math.degrees(
math.atan((best_center_average_coords[1] - screen_c_y) /
v_focal_length))
if view:
cv2.line(frame, best_center_average_coords, center_coords,
(0, 255, 0), 2)
for coord in average_coord_list:
cv2.line(frame, coord, coord, (255, 0, 0), 5)
new_h = 320
new_w = 240
resize = cv2.resize(frame, (new_w, new_h))
streamer.update_frame(resize)
if not streamer.is_streaming:
streamer.start_streaming()
# if view:
# cv2.imshow('Mask', mask)
# cv2.imshow('Contour Window', frame)
# if not window_moved:
# cv2.moveWindow('Mask', 300, 250)
# cv2.moveWindow('Contour Window', 1100, 250)
# window_moved = True
if cv2.waitKey(1) & 0xFF == ord('q'):
break
end_time = time.time()
fps.update()
fps.stop()
curr_fps = fps.fps()
if debug:
sys.stdout.write("""
=========================================================
Filtered Contour Area: {}
Sorted Contour Area: {}
Biggest Contour Area: {}
Rectangle List: {}
Contours: {}
Targets: {}
Avg_center_list: {}
Best Center Coords: {}
Index: {}
Pitch: {}
Yaw: {}
FPS: {}
Execute time: {}\r""".format(
filtered_contours_area,
[cv2.contourArea(contour)
for contour in sorted_contours], biggest_contour_area,
len(rectangle_list), len(sorted_contours),
len(average_coord_list), average_coord_list,
best_center_average_coords, index, pitch, yaw, curr_fps,
end_time - start_time))
if net_table:
table.putNumber('center_x', best_center_average_coords[0])
table.putNumber('center_y', best_center_average_coords[1])
table.putNumber('yaw', yaw)
table.putNumber('contours', len(sorted_contours))
table.putNumber('targets', len(average_coord_list))
table.putNumber('pitch', pitch)
table.putNumber('fps', curr_fps)
filtered_contours.clear()
sorted_contours.clear()
rectangle_list.clear()
average_coord_list.clear()
cap.release()
cv2.destroyAllWindows()
import cv2
import os
import sys
import shutil
import socket
from flask_opencv_streamer.streamer import Streamer
streamer = Streamer(8080, False)
port_getcommand = 9999
port_filesend = 7777
ss = socket.socket()
print('Data_Gathering initiating')
print('[+] Server socket is created.')
receiveno = 0
ss.bind(('', port_getcommand))
print('[+] Socket is binded to {}'.format(port_getcommand))
minW = 100
minH = 100
face_detector = cv2.CascadeClassifier(cv2.data.haarcascades +
"haarcascade_frontalface_default.xml")
font = cv2.FONT_HERSHEY_SIMPLEX
while True: #socket listen loop start
ss.listen(3)
receiveno = receiveno + 1
print('[+] Waiting for connection no.{}'.format(receiveno))
con, addr = ss.accept()
print('[+] Got connection from {}'.format(addr[0]))
data = con.recv(1024)
face_id = int(str(data, 'utf8'))
print("\n [INFO] Initializing face capture. Look the camera and wait ...")
import os
import cv2
import numpy as np
import tensorflow as tf
import argparse
import sys
from flask_opencv_streamer.streamer import Streamer
# Set up camera constants
IM_WIDTH = 640
IM_HEIGHT = 480
camera_type = 'usb'
port = 5000
require_login = False
streamer = Streamer(port, require_login, stream_res=(640, 480))
# This is needed since the working directory is the object_detection folder.
sys.path.append('..')
# Import utilites
from utils import label_map_util
from utils import visualization_utils as vis_util
# Name of the directory containing the object detection module we're using
MODEL_NAME = 'ssdlite_mobilenet_v2_coco_2018_05_09'
# Grab path to current working directory
CWD_PATH = os.getcwd()
# Path to frozen detection graph .pb file, which contains the model that is used
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('trainer/trainer.yml')
cascadePath = cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascadePath)
font = cv2.FONT_HERSHEY_SIMPLEX
#iniciate id counter
id = 0
cam = cv2.VideoCapture(0) # Initialize and start video capture
cam.set(3, 640) # set video widht
cam.set(4, 480) # set video height
# Define min window size to be recognized as a face
minW = 100
minH = 100
streamer = Streamer(8080, False) #for web
nowtime = datetime.now()
trainer_updatedday = nowtime.day
print("Camera ON")
while True:
ret, img = cam.read()
img = cv2.flip(img, -1) # Flip vertically
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=1.2,
minNeighbors=5,
minSize=(int(minW), int(minH)),
)
from flask_opencv_streamer.streamer import Streamer
from time import sleep
s = Streamer(80, True, "logins.txt", ".login")
s.start_streaming()
while True:
try:
sleep(1 / 30)
except KeyboardInterrupt:
break
import queue
import threading
import json
import time
import send
import dlib
import cv2
import os
import base64
import zmq
from flask_opencv_streamer.streamer import Streamer
# context = zmq.Context()
# footage_socket = context.socket(zmq.PUB)
# footage_socket.connect('tcp://localhost:5555')7
stream = Streamer(5555, False, stream_res=(1280, 720))
protoPath = os.path.join("face_detection_model", "deploy.prototxt")
modelPath = os.path.join("face_detection_model",
"res10_300x300_ssd_iter_140000.caffemodel")
detector = cv2.dnn.readNetFromCaffe(protoPath, modelPath)
embedder = cv2.dnn.readNetFromTorch("openface_nn4.small2.v1.t7")
unlocked = False
thumbnail_created = False
recognizer = pickle.loads(open("output/recognizer.pickle", "rb").read())
le = pickle.loads(open("output/le.pickle", "rb").read())
passes = 0
step = 0
datas = queue.Queue(maxsize=0)
temp = queue.Queue(maxsize=0)
attended = {"user_id": ""}
sent = {"sent": False}
else:
mqttc2.publish("RpiToWeb", "no")
#for x in myresult:
# print(x)
print("Write complete")
#mydb.commit()
cur.close()
mydb.close()
#except:
#mydb.rollback()
#print("We have a problem")
except Exception: #方法一：捕获所有异常
#如果发生异常，则回滚
print("发生异常", Exception)
mydb.rollback()
while True:
if message == "go":
port = 3030
require_login = False
stream_res = (400, 300)
streamer = Streamer(port, require_login, stream_res)
detect_recognize_face()
break
def main(blur_level=(69, 69)):
streamer = Streamer(3000, False)
# Generate blank images
by_type = np.zeros((1080, 1920, 4), np.uint8)
bg = cv2.imread("./Peeples blank.png")
key_type = cv2.imread("./key_type.png")
# Make sure all images have alpha channel
bg = cv2.cvtColor(bg, cv2.COLOR_RGB2RGBA)
key_type = cv2.cvtColor(key_type, cv2.COLOR_RGB2RGBA)
# Kafka stuff -- going to start reading this in real-time
consumer = Consumer({
"bootstrap.servers": "sckafka1.simcenter.utc.edu",
"group.id": "gradient-overlay",
})
consumer.subscribe(["cuip_vision_events"])
last_write_time = time.time()
while True:
try:
consumer.poll(1)
msg = consumer.consume()
if msg is not None:
for m in msg:
if m.error():
continue
j = json.loads(m.value())
if j["camera_id"] != "mlk-peeples-cam-3":
continue
current = 0
next = 1
while next < (len(j["locations"]) - 1):
x1 = int(j["locations"][current]["coords"][0])
y1 = int(j["locations"][current]["coords"][1])
x2 = int(j["locations"][next]["coords"][0])
y2 = int(j["locations"][next]["coords"][1])
cv2.line(
by_type,
get_center(j["locations"][0]["coords"]),
get_center(j["locations"][-1]["coords"]),
get_color_from_label(j["label"]),
2,
)
del x1, y1, x2, y2
current += 1
next += 1
if time.time() - last_write_time >= 15: # write every 15 seconds
print("Writing to disk")
output = cv2.GaussianBlur(by_type.copy(), blur_level, 0)
output = cv2.add(output, bg)
rows, cols = key_type.shape[:2]
output[0:rows, 0:cols] = key_type
cv2.imwrite("by_type.png", output)
del output
last_write_time = time.time()
streamer.update_frame(
cv2.add(cv2.GaussianBlur(by_type, blur_level, 0), bg))
if not streamer.is_streaming:
streamer.start_streaming()
time.sleep(1 / 30)
except KeyboardInterrupt:
break
by_type = cv2.GaussianBlur(by_type, blur_level, 0)
by_type = cv2.add(by_type, bg)
rows, cols = key_type.shape[:2]
by_type[0:rows, 0:cols] = key_type
cv2.imwrite("by_type.png", by_type)
consumer.close()
cv2.destroyAllWindows()