utils.py

import cv2
import tensorflow as tf
import numpy as np
def resize_image(inputs, modelsize):
    inputs= tf.image.resize(inputs, modelsize)
    return inputs


def output_boxes (input  , box_cordinates) :
    for (x ,y , w , h) in box_cordinates :
        cv2.rectangle(input , (x,y) , (x+w ,y+h) , (255,0,0) , 2)

def crop_face(self, imgarray, section, margin=40, size=64):
        """
        :param imgarray: full image
        :param section: face detected area (x, y, w, h)
        :param margin: add some margin to the face detected area to include a full head
        :param size: the result image resolution with be (size x size)
        :return: resized image in numpy array with shape (size x size x 3)
        """
        img_h, img_w, _ = imgarray.shape
        if section is None:
            section = [0, 0, img_w, img_h]
        (x, y, w, h) = section
        margin = int(min(w, h) * margin / 100)
        x_a = x - margin
        y_a = y - margin
        x_b = x + w + margin
        y_b = y + h + margin
        if x_a < 0:
            x_b = min(x_b - x_a, img_w - 1)
            x_a = 0
        if y_a < 0:
            y_b = min(y_b - y_a, img_h - 1)
            y_a = 0
        if x_b > img_w:
            x_a = max(x_a - (x_b - img_w), 0)
            x_b = img_w
        if y_b > img_h:
            y_a = max(y_a - (y_b - img_h), 0)
            y_b = img_h
        cropped = imgarray[y_a: y_b, x_a: x_b]
        resized_img = cv2.resize(cropped, (size, size), interpolation=cv2.INTER_AREA)
        resized_img = np.array(resized_img)
        return resized_img, (x_a, y_a, x_b - x_a, y_b - y_a)


def draw_label(cls, image, point, label, font=cv2.FONT_HERSHEY_SIMPLEX,
               font_scale=1, thickness=2):
    size = cv2.getTextSize(label, font, font_scale, thickness)[0]
    x, y = point
    cv2.rectangle(image, (x, y - size[1]), (x + size[0], y), (255, 0, 0), cv2.FILLED)
    cv2.putText(image, label, point, font, font_scale, (255, 255, 255), thickness)