"""USAGE
python examples/yolo_obj_detector.py \
    -c ~/dev/obj-tracking/yolov3.cfg \
    -w ~/dev/obj-tracking/yolov3.weights \
    -cl ~/dev/obj-tracking/yolo/darknet/data/coco.names \
    -i ~/dev/obj-tracking/person.jpg

python examples/yolo_obj_detector.py \
    -c ~/syncthing/dropbox/tracking-obj/mytrain.cfg \
    -w ~/syncthing/dropbox/tracking-obj/mytrain_final.weights \
    -cl ~/syncthing/dropbox/tracking-obj/mytrain.names \
    -i /media/sipp11/500BUP/handai_photos/test/6294.jpg
"""
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()


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.readNet(args.weights, args.config)
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()