"""USAGE
python yolo_vdo_obj_detector.py \
    -c ~/syncthing/dropbox/handai/obj_tracking/mytrain.cfg \
    -w ~/syncthing/dropbox/handai/obj_tracking/mytrain_final.weights \
    -cl ~/syncthing/dropbox/handai/obj_tracking/mytrain.names \
    -i ~/syncthing/dropbox/handai/data/5min.mp4
"""
from imutils.video import FPS
import cv2
import csv
import argparse
import numpy as np

ap = argparse.ArgumentParser()
ap.add_argument("-i", "--input", required=True, help="path to input vdo")
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)


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)

vs = cv2.VideoCapture(args.input)
_fps = vs.get(cv2.CAP_PROP_FPS)
Width = vs.get(cv2.CAP_PROP_FRAME_WIDTH)
Height = vs.get(cv2.CAP_PROP_FRAME_HEIGHT)
# Width = Height = None
scale = 0.00392
print(f'{_fps} fps {Width}x{Height} px')

writer = None


# initialize the list of object trackers and corresponding class
# labels
# trackers = []
labels = []
# start the frames per second throughput estimator
fps = FPS().start()
frame_count = 0

f = open('yolo_output_txt.csv', 'wt')
fieldnames = ['frame', 'what', 'x', 'y', 'w', 'h']
cf = csv.DictWriter(f, fieldnames=fieldnames)
cf.writeheader()

# loop over frames from the video file stream
while True:
    # grab the next frame from the video file
    (grabbed, frame) = vs.read()
    frame_count += 1
    _duration = frame_count / _fps

    # check to see if we have reached the end of the video file
    if frame is None:
        break

    blob = cv2.dnn.blobFromImage(frame, 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 = round(box[0])
        y = round(box[1])
        w = round(box[2])
        h = round(box[3])
        _cls_id = class_ids[i]
        _row = {
            'frame': frame_count,
            'what': str(classes[_cls_id]),
            'x': x,
            'y': y,
            'w': w,
            'h': h,
        }
        cf.writerow(_row)
        draw_prediction(
            frame,
            _cls_id,
            confidences[i],
            round(x),
            round(y),
            round(x + w),
            round(y + h),
        )

    # show the output frame
    cv2.imshow("Frame", frame)
    key = cv2.waitKey(1) & 0xFF

    # if the `q` key was pressed, break from the loop
    if key == ord("q"):
        break

    # update the FPS counter
    # fps.update()


# stop the timer and display FPS information
fps.stop()
print("[INFO] elapsed time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))


# do a bit of cleanup
cv2.destroyAllWindows()
vs.release()

f.close()