modnet.py

import os
import cv2
import argparse
import numpy as np
from PIL import Image

import onnx
import onnxruntime


class ModNet:

    def __init__(self, model_path):
        # Initialize session and get prediction
        self.session = onnxruntime.InferenceSession(model_path, None)

    # Get x_scale_factor & y_scale_factor to resize image
    def get_scale_factor(self, im_h, im_w, ref_size):

        if max(im_h, im_w) < ref_size or min(im_h, im_w) > ref_size:
            if im_w >= im_h:
                im_rh = ref_size
                im_rw = int(im_w / im_h * ref_size)
            elif im_w < im_h:
                im_rw = ref_size
                im_rh = int(im_h / im_w * ref_size)
        else:
            im_rh = im_h
            im_rw = im_w

        im_rw = im_rw - im_rw % 32
        im_rh = im_rh - im_rh % 32

        x_scale_factor = im_rw / im_w
        y_scale_factor = im_rh / im_h

        return x_scale_factor, y_scale_factor

    def segment(self, image_path):
        ref_size = 512
        ##############################################
        #  Main Inference part
        ##############################################

        # read image
        im = cv2.imread(image_path)
        im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)

        # unify image channels to 3
        if len(im.shape) == 2:
            im = im[:, :, None]
        if im.shape[2] == 1:
            im = np.repeat(im, 3, axis=2)
        elif im.shape[2] == 4:
            im = im[:, :, 0:3]

        # normalize values to scale it between -1 to 1
        im = (im - 127.5) / 127.5

        im_h, im_w, im_c = im.shape
        x, y = self.get_scale_factor(im_h, im_w, ref_size)

        # resize image
        im = cv2.resize(im, None, fx=x, fy=y, interpolation=cv2.INTER_AREA)

        # prepare input shape
        im = np.transpose(im)
        im = np.swapaxes(im, 1, 2)
        im = np.expand_dims(im, axis=0).astype('float32')

        input_name = self.session.get_inputs()[0].name
        output_name = self.session.get_outputs()[0].name
        result = self.session.run([output_name], {input_name: im})

        # refine matte
        matte = (np.squeeze(result[0]) * 255).astype('uint8')
        matte = cv2.resize(matte, dsize=(im_w, im_h), interpolation=cv2.INTER_AREA)

        # obtain predicted foreground
        image = cv2.imread(image_path)
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

        if len(image.shape) == 2:
            image = image[:, :, None]
        if image.shape[2] == 1:
            image = np.repeat(image, 3, axis=2)
        elif image.shape[2] == 4:
            image = image[:, :, 0:3]
        matte = np.repeat(np.asarray(matte)[:, :, None], 3, axis=2) / 255
        foreground = image * matte + np.full(image.shape, 255) * (1 - matte)

        return foreground