# USAGE
# python roi_imgs.py --input videos/two_people.mp4 --output detected/two_people --yolo yolo-coco

import numpy as np
import argparse
import imutils
import time
import cv2
import os

ap = argparse.ArgumentParser()
ap.add_argument("-i", "--input", required=True, help="path to input video")
ap.add_argument("-o", "--output", required=True, help="path to output video")
ap.add_argument("-y", "--yolo", required=True, help="base path to YOLO directory")
ap.add_argument("-c", "--confidence", type=float, default=0.5, help="minimum probability to filter weak detections")
ap.add_argument("-t", "--threshold", type=float, default=0.3, help="threshold when applying non-maxima suppression")
args = vars(ap.parse_args())

labelsPath = os.path.sep.join([args["yolo"], "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")

weightsPath = os.path.sep.join([args["yolo"], "yolov3.weights"])
configPath = os.path.sep.join([args["yolo"], "yolov3.cfg"])

print("[INFO] loading YOLO from disk...")
net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)
ln = net.getLayerNames()
ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]

vs = cv2.VideoCapture(args["input"])
writer = None
(W, H) = (None, None)

try:
    prop = cv2.cv.CV_CAP_PROP_FRAME_COUNT if imutils.is_cv2() else cv2.CAP_PROP_FRAME_COUNT
    total = int(vs.get(prop))
    print("[INFO] {} total frames in video".format(total))

except:
    print("[INFO] could not determine # of frames in video")
    print("[INFO] no approx. completion time can be provided")
    total = -1
f = 0
while True:
    (grabbed, frame) = vs.read()

    if not grabbed:
        break

    if W is None or H is None:
        (H, W) = frame.shape[:2]

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

    boxes = []
    confidences = []
    classIDs = []

    for output in layerOutputs:
        for detection in output:
            scores = detection[5:]
            classID = np.argmax(scores)
            confidence = scores[classID]

            if confidence > args["confidence"]:
                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))

                if LABELS[classID] == 'person':
                    boxes.append([x, y, int(width), int(height)])
                    confidences.append(float(confidence))
                    classIDs.append(classID)

    idxs = cv2.dnn.NMSBoxes(boxes, confidences, args["confidence"], args["threshold"])

    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])
            x_center = int((x+x+w)/2)
            y_bottom = y+h

            file_id = str(f) + "_" + str(i)
            print(file_id)
            roi = frame[y:y+h, x:x+w]
            cv2.imwrite(args["output"]+"/"+file_id+'.jpg', roi)


            # color = [int(c) for c in COLORS[classIDs[i]]]
            # cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
            # cv2.circle(frame, (x_center, y_bottom), 5, (0,0,255), -1)
            # text = "{}: {:.4f}".format(LABELS[classIDs[i]], confidences[i])
            # cv2.putText(frame, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
    f += 1

    # if writer is None:
    #     fourcc = cv2.VideoWriter_fourcc(*"MJPG")
    #     writer = cv2.VideoWriter(args["output"], fourcc, 30, (frame.shape[1], frame.shape[0]), True)
    #
    #     if total > 0:
    #         elap = (end - start)
    #         print("[INFO] single frame took {:.4f} seconds".format(elap))
    #         print("[INFO] estimated total time to finish: {:.4f}".format(elap * total))
    #
    # writer.write(frame)

print("[INFO] cleaning up...")
# writer.release()
vs.release()
print("[INFO] Finished!")
