is_pedestrian.py

from skimage.feature import hog
from skimage import color
import cv2
import numpy as np
import pickle
import image_pyramid as ip
import scale_bboxes as sb

def detector(my_im, weight,bias, scale):
    window_size = [128, 64]
    block_size = 4
    cell_size = 8
    min_height = 128
    min_width = 64
    orient = 9
    thresh = 0

    total_block_size = block_size * cell_size; 

    curr_depth = 0

    for im in ip.createImagePyramid(my_im, scale, min_height, min_width):
        curr_depth +=1
        H = im.shape[0]
        W = im.shape[1]
        dim_size_feat = weight.shape[1];
        for h in xrange(0,H,total_block_size / 2):
            for w in xrange(0,W,total_block_size / 2):
                if ((window_size[1] + w <= W) and (window_size[0]+h) <= H):
                    fd, _ = hog(im[h:(window_size[0]+h), w:(window_size[1]+w)], orientations=orient, pixels_per_cell=(cell_size, cell_size),
                    cells_per_block=(block_size, block_size), visualise=True)
                
                    score_calc =  np.dot(np.reshape(fd, (1, dim_size_feat)) , np.transpose(weight)) + bias
                    if(score_calc[0][0] >= thresh):
                        print score_calc[0][0]
                        cv2.imshow("Detected Pedestrian", my_im)
                        cv2.waitKey(25)
                        return score_calc[0][0]
                    
    return False