mdnet_utils.py

from scipy.misc import imresize
import numpy as np

def overlap_ratio(rect1, rect2):
    '''
    Compute overlap ratio between two rects
    - rect: 1d array of [x,y,w,h] or 
            2d array of N x [x,y,w,h]
    '''

    if rect1.ndim==1:
        rect1 = rect1[None,:]
    if rect2.ndim==1:
        rect2 = rect2[None,:]

    left = np.maximum(rect1[:,0], rect2[:,0])
    right = np.minimum(rect1[:,0]+rect1[:,2], rect2[:,0]+rect2[:,2])
    top = np.maximum(rect1[:,1], rect2[:,1])
    bottom = np.minimum(rect1[:,1]+rect1[:,3], rect2[:,1]+rect2[:,3])

    intersect = np.maximum(0,right - left) * np.maximum(0,bottom - top)
    union = rect1[:,2]*rect1[:,3] + rect2[:,2]*rect2[:,3] - intersect
    iou = np.clip(intersect / union, 0, 1)
    return iou


def crop_image(img, bbox, img_size=107, padding=16, valid=False):
    
    x,y,w,h = np.array(bbox,dtype='float32')

    half_w, half_h = w/2, h/2
    center_x, center_y = x + half_w, y + half_h

    if padding > 0:
        pad_w = padding * w/img_size
        pad_h = padding * h/img_size
        half_w += pad_w
        half_h += pad_h
        
    img_h, img_w, _ = img.shape
    min_x = int(center_x - half_w + 0.5)
    min_y = int(center_y - half_h + 0.5)
    max_x = int(center_x + half_w + 0.5)
    max_y = int(center_y + half_h + 0.5)
    
    if valid:
        min_x = max(0, min_x)
        min_y = max(0, min_y)
        max_x = min(img_w, max_x)
        max_y = min(img_h, max_y)

    if min_x >=0 and min_y >= 0 and max_x <= img_w and max_y <= img_h:
        cropped = img[min_y:max_y, min_x:max_x, :]

    else:
        min_x_val = max(0, min_x)
        min_y_val = max(0, min_y)
        max_x_val = min(img_w, max_x)
        max_y_val = min(img_h, max_y)
        
        cropped = 128 * np.ones((max_y-min_y, max_x-min_x, 3), dtype='uint8')
        cropped[min_y_val-min_y:max_y_val-min_y, min_x_val-min_x:max_x_val-min_x, :] \
            = img[min_y_val:max_y_val, min_x_val:max_x_val, :]
    try:
        scaled = imresize(cropped, (img_size, img_size))
    except  ValueError:
        print "a"
    return scaled

def extract_regions(image,samples,crop_size=107,padding=16,shuffle=False):
    regions = np.zeros((samples.shape[0], crop_size,crop_size,3), dtype = 'uint8')
    for t in range(samples.shape[0]):
        regions[t] = crop_image(image,samples[t],crop_size,padding)

    regions = regions - 128.
    return regions