import numpy as np
import argparse
import cv2
import glob

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())


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))

print("[INFO] loading model...")
net=cv2.dnn.readNetFromCaffe(args["prototxt"],args["model"])

for imagepath in glob.glob(args['image']+"/*.jpg"):
    image=cv2.imread(imagepath)
    (h,w)=image.shape[:2]
    blob=cv2.dnn.blobFromImage(cv2.resize(image,(300,300)),0.007843,(300,300),127.5)

    print("[INFO] computing object detectin..")
    net.setInput(blob)
    detection=net.forward()

    for i in np.arange(0, detection.shape[2]):
        confidence=detection[0,0,i,2]
        if confidence > args["confidence"]:
            idx=int(detection[0,0,i,1])
            box=detection[0,0,i,3:7] * np.array([w,h,w,h])
            (startX,startY,endX,endY)=box.astype('int')
            
            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 startY - 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)


