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# Object detection - YOLO - OpenCV
# Author : Arun Ponnusamy   (July 16, 2018)
# Website : http://www.arunponnusamy.com
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import cv2
import argparse
import numpy as np

'''ap = argparse.ArgumentParser()
ap.add_argument('-i', '--image', required=True,
                help = 'path to input image')
ap.add_argument('-c', '--config', required=True,
                help = 'path to yolo config file')
ap.add_argument('-w', '--weights', required=True,
                help = 'path to yolo pre-trained weights')
ap.add_argument('-cl', '--classes', required=True,
                help = 'path to text file containing class names')
args = ap.parse_args()
'''
ap = argparse.ArgumentParser()
ap.add_argument('--image', default='6.jpg',
                help='path to input image')
ap.add_argument('--config', default='yolov3.cfg',
                help='path to yolo config file')
ap.add_argument('--weights', default='yolov3.weights',
                help='path to yolo pre-trained weights')
ap.add_argument('--classes', default='yolov3.txt',
                help='path to text file containing class names')
args = ap.parse_args()


def get_output_layers(net):
    layer_names = net.getLayerNames()

    output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

    return output_layers


def draw_prediction(img, class_id, confidence, x, y, x_plus_w, y_plus_h):
    label = str(classes[class_id])

    color = COLORS[class_id]

    cv2.rectangle(img, (x, y), (x_plus_w, y_plus_h), color, 2)

    cv2.putText(img, label, (x - 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)


image = cv2.imread(args.image)

Width = image.shape[1]
Height = image.shape[0]
scale = 0.00392

classes = None

with open(args.classes, 'r') as f:
    classes = [line.strip() for line in f.readlines()]

COLORS = np.random.uniform(0, 255, size=(len(classes), 3))

net = cv2.dnn.readNetFromDarknet(args.config, args.weights)

blob = cv2.dnn.blobFromImage(image, scale, (416, 416), (0, 0, 0), True, crop=False)

net.setInput(blob)

outs = net.forward(get_output_layers(net))

class_ids = []
confidences = []
boxes = []
conf_threshold = 0.5
nms_threshold = 0.4

for out in outs:
    for detection in out:
        scores = detection[5:]
        class_id = np.argmax(scores)
        confidence = scores[class_id]
        if confidence > 0.5:
            center_x = int(detection[0] * Width)
            center_y = int(detection[1] * Height)
            w = int(detection[2] * Width)
            h = int(detection[3] * Height)
            x = center_x - w / 2
            y = center_y - h / 2
            class_ids.append(class_id)
            confidences.append(float(confidence))
            boxes.append([x, y, w, h])

indices = cv2.dnn.NMSBoxes(boxes, confidences, conf_threshold, nms_threshold)

for i in indices:
    i = i[0]
    box = boxes[i]
    x = box[0]
    y = box[1]
    w = box[2]
    h = box[3]
    draw_prediction(image, class_ids[i], confidences[i], round(x), round(y), round(x + w), round(y + h))

cv2.imshow("object detection", image)
cv2.waitKey()

cv2.imwrite("object-detection.jpg", image)
cv2.destroyAllWindows()