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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
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