detection.py

#!/usr/bin/python

import numpy as np
import random as rnd
import cv2
from utils import *
from make_model import *

seed = 11
rnd.seed(seed)
np.random.seed(seed)

############################
#### EDIT ONLY THIS BLOCK

videofile = './test_video.mp4'
cap = cv2.VideoCapture(videofile)

model = make_model()
model.load_weights('weights_best.h5')

lower = [0, 0, 0]
upper = [100, 100, 100]

stepSize = 30

############################

lower = np.array(lower)
upper = np.array(upper)

while(True):

    ret,frame = cap.read()
    if(ret == False):
        print("Done")
        break

############################
#### EDIT ONLY THIS BLOCK

    #Convert image to HSV from BGR
    img_hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

    # Find the pixels that correspond to road
    img_out = cv2.inRange(img_hsv, lower, upper)

############################

    # Clean from noisy pixels and keep only the largest connected segment
    img_out = post_process(img_out)

    image_masked = frame.copy()

    # Get masked image
    image_masked[img_out == 0] = (0, 0, 0)
    s=0.25

    #Resize images for computational efficiency
    frame = cv2.resize(frame,None, fx=s,fy=s)
    image_masked = cv2.resize(image_masked,None, fx=s,fy=s)

    #Run the sliding window detection process
    bbox_list, totalWindows, correct, score = detectionProcess(cv2.cvtColor(frame,cv2.COLOR_BGR2RGB), model, winH=50, winW=50, depth=3, nb_images=1, scale=1, stepSize=stepSize, thres_score=0.05)

    #Draw the detections
    drawBoxes(frame, bbox_list)

    # Draw detections and road masks
    cv2.imshow('video',sidebyside(frame,image_masked))
    k = cv2.waitKey(3)

    #QUIT
    if(k & 0xFF == ord('q')):
        cv2.destroyWindow("video")
        break

cap.release()
cv2.destroyAllWindows()