video_showcase.py

# import the necessary packages
import matplotlib.pyplot as plt
from scipy.spatial import distance as dist
from imutils.video import FileVideoStream
from imutils.video import VideoStream
from imutils import face_utils
import numpy as np
import pandas as pd
import argparse
import imutils
import time
import dlib
import cv2

#fetch arguments
ap = argparse.ArgumentParser()
# ap.add_argument("-p", "--shape-predictor", required=True,
#                 help="path to facial landmark predictor")
ap.add_argument("-v", "--video", type=str, default="",
                help="path to input video file")
args = vars(ap.parse_args())

# #facial detector
# detector = dlib.get_frontal_face_detector()
# predictor = dlib.shape_predictor(args["shape_predictor"])

# start the video stream thread
vs = FileVideoStream(args["video"]).start()
fileStream = True
time.sleep(1.0)

'''
try:
    SHOWCASE_DATA = pd.read_csv("showcase_data/{}.tag".format(args["video"][6:-4]), sep='\t',
                                header=0, names=['frame', 'Manual_Detection', 'Auto_Detection'], index_col="frame")
except FileNotFoundError:
    SHOWCASE_DATA = pd.read_csv("showcase_data/{}.tag".format(args["video"][7:-4]), sep='\t',
                                header=0, names=['frame', 'Manual_Detection', 'Auto_Detection'], index_col="frame")
'''
raw_data=pd.read_csv("video_test_2.csv", index_col="frame")
result=pd.read_csv("results.csv", index_col="frame")
raw_data_1=raw_data.threshold
SHOWCASE_DATA=pd.concat([raw_data_1, result], axis=1 )
SHOWCASE_DATA=SHOWCASE_DATA.fillna(0)
SHOWCASE_DATA=SHOWCASE_DATA.cumsum(axis=0)
FRAME = 0

while True:
    if fileStream and not vs.more():
        break

    frame = vs.read()
    frame = imutils.resize(frame, width=450)
    # gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    # rects = detector(gray, 0)
    plot = raw_data.ear.plot()
    plt.savefig('plot.png')
    plot = cv2.imread('plot.png')
    plot = imutils.resize(plot, width=450)
    # for rect in rects:

        # shape = predictor(gray, rect)
        # shape = face_utils.shape_to_np(shape)
    try:
        frame=np.concatenate((frame,plot), axis=0)
        cv2.putText(frame, "OpenCV blink detection: {}".format(SHOWCASE_DATA.threshold[FRAME]), (10, 30),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
        cv2.putText(frame, "Frame: {}".format(FRAME), (10, 300),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
        cv2.putText(frame, "SVM    blink detection: {}".format(SHOWCASE_DATA.blink[FRAME]), (10, 60),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
    except:
        cv2.putText(frame, "End of Data", (10, 60),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
        # for (x, y) in shape:
        #     cv2.circle(frame, (x, y), 1, (0, 0, 255), -1)

    try:
        cv2.imwrite("final_video/frame{}.jpg".format(FRAME), frame)
        print("image frame{}.jpg saved".format(FRAME))
    except:
        print("imwrite error")
    FRAME += 1

cv2.destroyAllWindows()
vs.stop()