def __init__(
self,
hell,
ip,
usern,
passw,
video_source=0,
):
# Open the video source
self.vid = cv2.VideoCapture("rtsp://" + usern + ":" + passw + "@" +
ip +
":554/cam/realmonitor?channel=3&subtype=0")
# self.vid = cv2.VideoCapture("videos/airport.mp4")
if not self.vid.isOpened():
raise ValueError("Unable to open video source", video_source)
ap = argparse.ArgumentParser()
ap.add_argument("-p",
"--prototxt",
required=True,
help="path to Caffe 'deploy' prototxt file")
ap.add_argument("-m",
"--model",
required=True,
help="path to Caffe pre-trained model")
ap.add_argument("-c",
"--confidence",
type=float,
default=0.2,
help="minimum probability to filter weak detections")
self.args = vars(ap.parse_args())
# initialize the list of class labels MobileNet SSD was trained to
# detect, then generate a set of bounding box colors for each class
self.CLASSES = [
"background", "aeroplane", "bicycle", "bird", "boat", "bottle",
"bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse",
"motorbike", "person", "pottedplant", "sheep", "sofa", "train",
"tvmonitor"
]
self.COLORS = np.random.uniform(0, 255, size=(len(self.CLASSES), 3))
# load our serialized model from disk
print("[INFO] loading model...")
self.net = cv2.dnn.readNetFromCaffe(self.args["prototxt"],
self.args["model"])
# initialize the video stream, allow the cammera sensor to warmup,
# and initialize the FPS counter
print("[INFO] starting video stream...")
self.vs = VideoStream(src=0).start()
time.sleep(2.0)
self.fps = FPS().start()
#Allow for user to put in experiment name
askWindow = tk.Tk()
d = StartDialog(askWindow)
[exptName, condit, syncFile, shutoffTime, trackingToggle, twoCam] = d.result
if len(shutoffTime) == 0:
shutoffTime = np.inf
else:
shutoffTime = float(shutoffTime)
askWindow.destroy()
h = 240
w = 320
if twoCam:
cap = VideoStream(src=2, resolution=(w, h)).start()
# cap2 = VideoStream(usePiCamera=True).start()
cap2 = VideoStream(src=0, resolution=(w, h)).start()
camList = [cap, cap2]
camTsList = [list(), list()]
ROIList = [list(), list()]
ROINameList = [list(), list()]
ROIStatList = [list(), list()]
ROIShowCheck = [0, 0]
camCtrXList = [list(), list()]
camCtrYList = [list(), list()]
camVelList = [list(), list()]
camRotList = [list(), list()]
anglePrev = [list(), list()]
else:
cap = VideoStream(src=2, resolution=(w * 2, h * 2)).start()
from ttk import * import tkinter as tk from tkinter import * import cv2 from PIL import Image, ImageTk import os import numpy as np import picamera from picamera.array import PiRGBArray import imutils.imutils as imutils from imutils.imutils.video import VideoStream import time import RPi.GPIO as GPIO cap = VideoStream(src=0).start() # cap2 = VideoStream(usePiCamera=True).start() cap2 = VideoStream(src=3).start() time.sleep(2.0) startTime = time.time() lineHolder = list() global lineHolder2 lineHolder2 = list() graphCheck = -1 global graphCheck2 graphCheck2 = -1 frac = 0 minTime = 0 inputPin1 = 24 GPIO.setmode(GPIO.BCM) GPIO.setup(inputPin1, GPIO.IN, pull_up_down=GPIO.PUD_UP)
class MyVideoCapture:
def __init__(
self,
hell,
ip,
usern,
passw,
video_source=0,
):
# Open the video source
self.vid = cv2.VideoCapture("rtsp://" + usern + ":" + passw + "@" +
ip +
":554/cam/realmonitor?channel=3&subtype=0")
# self.vid = cv2.VideoCapture("videos/airport.mp4")
if not self.vid.isOpened():
raise ValueError("Unable to open video source", video_source)
ap = argparse.ArgumentParser()
ap.add_argument("-p",
"--prototxt",
required=True,
help="path to Caffe 'deploy' prototxt file")
ap.add_argument("-m",
"--model",
required=True,
help="path to Caffe pre-trained model")
ap.add_argument("-c",
"--confidence",
type=float,
default=0.2,
help="minimum probability to filter weak detections")
self.args = vars(ap.parse_args())
# initialize the list of class labels MobileNet SSD was trained to
# detect, then generate a set of bounding box colors for each class
self.CLASSES = [
"background", "aeroplane", "bicycle", "bird", "boat", "bottle",
"bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse",
"motorbike", "person", "pottedplant", "sheep", "sofa", "train",
"tvmonitor"
]
self.COLORS = np.random.uniform(0, 255, size=(len(self.CLASSES), 3))
# load our serialized model from disk
print("[INFO] loading model...")
self.net = cv2.dnn.readNetFromCaffe(self.args["prototxt"],
self.args["model"])
# initialize the video stream, allow the cammera sensor to warmup,
# and initialize the FPS counter
print("[INFO] starting video stream...")
self.vs = VideoStream(src=0).start()
time.sleep(2.0)
self.fps = FPS().start()
# Get video source width and height
# self.width = self.vid.get(cv2.CAP_PROP_FRAME_WIDTH)
# self.height = self.vid.get(cv2.CAP_PROP_FRAME_HEIGHT)
# self.width=
def get_frame(self):
frame = self.vs.read()
frame = cv2.resize(frame, (1280, 720))
# grab the frame dimensions and convert it to a blob
(h, w) = frame.shape[:2]
blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)), 0.007843,
(300, 300), 127.5)
# pass the blob through the network and obtain the detections and
# predictions
self.net.setInput(blob)
detections = self.net.forward()
# loop over the detections
for i in np.arange(0, detections.shape[2]):
# extract the confidence (i.e., probability) associated with
# the prediction
confidence = detections[0, 0, i, 2]
# filter out weak detections by ensuring the `confidence` is
# greater than the minimum confidence
if confidence > self.args["confidence"]:
# extract the index of the class label from the
# `detections`, then compute the (x, y)-coordinates of
# the bounding box for the object
idx = int(detections[0, 0, i, 1])
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
# draw the prediction on the frame
label = "{}: {:.2f}%".format(self.CLASSES[idx],
confidence * 100)
cv2.rectangle(frame, (startX, startY), (endX, endY),
self.COLORS[idx], 2)
y = startY - 15 if startY - 15 > 15 else startY + 15
cv2.putText(frame, label, (startX, y),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, self.COLORS[idx], 2)
self.fps.update()
# Return a boolean success flag and the current frame converted to BGR
return cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Release the video source when the object is destroyed
def __del__(self):
if self.vid.isOpened():
self.vid.release()
import imutils.imutils as imutils
from imutils.imutils.video import VideoStream
import time
import pigpio
import datetime
import tkinter as tk
from tkinter import simpledialog
import numpy as np
from subprocess import call
import os
h = 480
w = 640
dispW, dispH = (1024, 768)
frameRate = 90
cap = VideoStream(usePiCamera=True, resolution=(w, h),
framerate=frameRate).start()
cap.camera.vflip = True
windName = "selfie cam"
preVid = 1
vid = 0
cv2.namedWindow(windName)
cv2.moveWindow(windName, dispW // 2 - w // 2, dispH // 2 - h // 2)
startTime = 0
saveName = ''
savePath = ''
time.sleep(.5)
whiteRedHold, whiteBlueHold = cap.camera.awb_gains
cap.camera.awb_mode = 'off'
cap.camera.awb_gains = (whiteRedHold, whiteBlueHold)
stop = False
charStart = 5