real_time_object_detection.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 time
import cv2
from urllib.request import urlopen


host = 'http://192.168.0.101:8080/'
url = host + 'shot.jpg'

# 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)

time.sleep(2.0)

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
	if args["source"] == "webcam":
		ret, frame = vs.read()
	else:
		imgResp=urlopen(url)
		imgNp=np.array(bytearray(imgResp.read()),dtype=np.uint8)
		frame=cv2.imdecode(imgNp,-1)
	
	frame = imutils.resize(frame, 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


# do a bit of cleanup
cv2.destroyAllWindows()