import numpy as np
import argparse
import time
import cv2
import os
from matplotlib import pyplot as plt

model = 'yolov2.weights'
config = 'yolov2.cfg'
net = cv2.dnn.readNetFromDarknet(config, model)

labelsPath = os.path.sep.join(["yolo-coco", "coco.names"])
LABELS = open(labelsPath).read().strip().split("\n")

np.random.seed(42)
COLORS = np.random.randint(0, 255, size=(len(LABELS), 3),
	dtype="uint8")


image = cv2.imread("./peoples.jpeg")
(H, W) = image.shape[:2]

ln = net.getLayerNames()
ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]

blob = cv2.dnn.blobFromImage(image, 1 / 255.0, (416, 416),
	swapRB=True, crop=False)
net.setInput(blob)
start = time.time()
layerOutputs = net.forward(ln)
end = time.time()

print("[INFO] YOLO took {:.6f} seconds".format(end - start))

boxes = []
confidences = []
classIDs = []
conf = 0.3


for output in layerOutputs:
	for detection in output:
		scores = detection[5:]
		classID = np.argmax(scores)
		confidence = scores[classID]
		if confidence > conf:
			box = detection[0:4] * np.array([W, H, W, H])
			(centerX, centerY, width, height) = box.astype("int")
			x = int(centerX - (width / 2))
			y = int(centerY - (height / 2))
			boxes.append([x, y, int(width), int(height)])
			confidences.append(float(confidence))
			classIDs.append(classID)

idxs = cv2.dnn.NMSBoxes(boxes, confidences, conf,
	10)

if len(idxs) > 0:
	for i in idxs.flatten():
		(x, y) = (boxes[i][0], boxes[i][1])
		(w, h) = (boxes[i][2], boxes[i][3])
		color = [int(c) for c in COLORS[classIDs[i]]]
		cv2.rectangle(image, (x, y), (x + w, y + h), color, 2)
		text = "{}: {:.4f}".format(LABELS[classIDs[i]], confidences[i])
		cv2.putText(image, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX,
			0.5, color, 2)
plt.imshow(image,),plt.show()
