import numpy as np
import argparse
import cv2

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())

theObject = ["background", "aeroplane", "sepeda", "burung", "perahu", "botol",
             "bus", "mobil", "kucing", "kursi", "sapi", "mejaMakan",
			 "anjing", "kuda", "motor", "manusia", "pot bunga", "domba",
			 "sofa", "kereta", "monitor"]
COLORS = np.random.uniform(0, 255, size=(len(theObject), 3))

print("[ALERT] Silahkan tunggu...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])

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)

print("[ALERT] Menjalankan Object Detections...")
net.setInput(blob)
detections = net.forward()

for i in np.arange(0, detections.shape[2]):
	confidence = detections[0, 0, i, 2]

	if confidence > args["confidence"]:

		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")

		label = "{}: {:.2f}%".format(theObject[idx], confidence * 100)
		print("[ALERT] {}".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)

# python deep_learning_object_detection.py --image assets/pic1.jpeg --prototxt MobileNetSSD_deploy.prototxt.txt --model MobileNetSSD_deploy.caffemodel


