real_time_object_detection_edison.py

# USAGE
# python real_time_object_detection.py --prototxt MobileNetSSD_deploy.prototxt.txt --model MobileNetSSD_deploy.caffemodel --source webcam

# import the necessary packages
from imutils.video import VideoStream
import numpy as np
import sys
import argparse
import imutils
import urllib.request
import time
import cv2
from urllib.request import urlopen


host = 'http://192.168.0.105:8887/'
url = host + 'video'

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("--prototxt", required=True,
	help="path to Caffe 'deploy' prototxt file")
ap.add_argument("--model", required=True,
	help="path to Caffe pre-trained model")
ap.add_argument("--source", required=True, 
	help="Source of video stream (webcam/host)")
ap.add_argument("-c", "--confidence", type=float, default=0.2,
	help="minimum probability to filter weak detections")
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
CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
	"bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
	"dog", "horse", "motorbike", "person", "pottedplant", "sheep",
	"sofa", "train", "tvmonitor"]
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))

# load our serialized model from disk
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])

# initialize the video stream, allow the cammera sensor to warmup,
print("[INFO] starting video stream...")

if args["source"] == "webcam":
	vs = cv2.VideoCapture(0)
elif args["source"] == "edison":
	vs = cv2.VideoCapture(url)
time.sleep(2.0)

stream = urllib.request.urlopen(url)
bytes = bytes()
detected_objects = []
# 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 400 pixels
	bytes += stream.read(1024)
	a = bytes.find(b'\xff\xd8')
	b = bytes.find(b'\xff\xd9')
	if a != -1 and b != -1:
		jpg = bytes[a:b+2]
		bytes = bytes[b+2:]
		i = cv2.imdecode(np.fromstring(jpg, dtype=np.uint8), cv2.IMREAD_COLOR)
		frame = imutils.resize(i, width=800)
		
		# 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
		net.setInput(blob)
		detections = 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 > 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(CLASSES[idx],
					confidence * 100)
				detected_objects.append(label)
				cv2.rectangle(frame, (startX, startY), (endX, endY),
					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, COLORS[idx], 2)
				
		
		# show the output frame
		cv2.imshow("Frame", frame)
		key = cv2.waitKey(1) & 0xFF

		# if the `q` key was pressed, break from the loop
		if key == ord("q"):
			break

			cv2.imshow('i', i)
			if cv2.waitKey(1) == 27:
				exit(0)

	

# do a bit of cleanup
cv2.destroyAllWindows()