# import the necessary packages
import numpy as np
import argparse
import cv2

#construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i","--image", required=True,help="path to input image")
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")
args = vars(ap.parse_args())

#init 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"]

CLASSES = ["background","person"]
#load our serialized model from disk
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])
#load the input image and construct an input blob for the image
# by resizing to a fixed 300x300 pixels and then normalizing it
# (note: normalization is done via the authors of the MobileNet SSD
# implementation)
image = cv2.imread(args["image"])
(h,w) = image.shape[:2]
blob = cv2.dnn.blobFromImage(cv2.resize(image,( 300,300)), 0.007843, (300,300), 127.5)

#pass the blobs through the network and obtain the detections and predictions
print("[INFO] computing object detections...")
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 'confidnce' 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")
		
		# display the prediction
		label = "{}: {:.2f}%".format(CLASSES[idx], confidence*100)
		print("[INFO] {}".format(label))
		cv2.rectangle(image, (startX, startY), (endX, endY), COLORS[idx],2)
		y = startY - 15 if staryY-15 > 15 else startY + 15
		cv2.putText(image, label, (startX,y), cv2.FONT_HERSHEY_SIMPLEX,0.5,COLORS[idx],2)

cv2.imshow("Output", image)
cv2.waitKey(0)
