app.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import cv2
import sys
import numpy as np
import mxnet as mx
import os


from scipy import misc
import random
import sklearn
from sklearn.decomposition import PCA
from time import sleep
from easydict import EasyDict as edict
from mtcnn_detector import MtcnnDetector
from skimage import transform as trans
import matplotlib.pyplot as plt
from mxnet.contrib.onnx.onnx2mx.import_model import import_model

import gradio as gr


def get_model(ctx, model):
    image_size = (112,112)
    # Import ONNX model
    sym, arg_params, aux_params = import_model(model)
    # Define and binds parameters to the network
    model = mx.mod.Module(symbol=sym, context=ctx, label_names = None)
    model.bind(data_shapes=[('data', (1, 3, image_size[0], image_size[1]))])
    model.set_params(arg_params, aux_params)
    return model
    
for i in range(4):
    mx.test_utils.download(dirname='mtcnn-model', url='https://s3.amazonaws.com/onnx-model-zoo/arcface/mtcnn-model/det{}-0001.params'.format(i+1))
    mx.test_utils.download(dirname='mtcnn-model', url='https://s3.amazonaws.com/onnx-model-zoo/arcface/mtcnn-model/det{}-symbol.json'.format(i+1))
    mx.test_utils.download(dirname='mtcnn-model', url='https://s3.amazonaws.com/onnx-model-zoo/arcface/mtcnn-model/det{}.caffemodel'.format(i+1))
    mx.test_utils.download(dirname='mtcnn-model', url='https://s3.amazonaws.com/onnx-model-zoo/arcface/mtcnn-model/det{}.prototxt'.format(i+1))

# Determine and set context
if len(mx.test_utils.list_gpus())==0:
    ctx = mx.cpu()
else:
    ctx = mx.gpu(0)
# Configure face detector
det_threshold = [0.6,0.7,0.8]
mtcnn_path = os.path.join(os.path.dirname('__file__'), 'mtcnn-model')
detector = MtcnnDetector(model_folder=mtcnn_path, ctx=ctx, num_worker=1, accurate_landmark = True, threshold=det_threshold)

def preprocess(img, bbox=None, landmark=None, **kwargs):
    M = None
    image_size = []
    str_image_size = kwargs.get('image_size', '')
    # Assert input shape
    if len(str_image_size)>0:
        image_size = [int(x) for x in str_image_size.split(',')]
        if len(image_size)==1:
            image_size = [image_size[0], image_size[0]]
        assert len(image_size)==2
        assert image_size[0]==112
        assert image_size[0]==112 or image_size[1]==96
    
    # Do alignment using landmark points
    if landmark is not None:
        assert len(image_size)==2
        src = np.array([
          [30.2946, 51.6963],
          [65.5318, 51.5014],
          [48.0252, 71.7366],
          [33.5493, 92.3655],
          [62.7299, 92.2041] ], dtype=np.float32 )
        if image_size[1]==112:
            src[:,0] += 8.0
        dst = landmark.astype(np.float32)
        tform = trans.SimilarityTransform()
        tform.estimate(dst, src)
        M = tform.params[0:2,:]
        assert len(image_size)==2
        warped = cv2.warpAffine(img,M,(image_size[1],image_size[0]), borderValue = 0.0)
        return warped
    
    # If no landmark points available, do alignment using bounding box. If no bounding box available use center crop
    if M is None:
        if bbox is None:
            det = np.zeros(4, dtype=np.int32)
            det[0] = int(img.shape[1]*0.0625)
            det[1] = int(img.shape[0]*0.0625)
            det[2] = img.shape[1] - det[0]
            det[3] = img.shape[0] - det[1]
        else:
            det = bbox
        margin = kwargs.get('margin', 44)
        bb = np.zeros(4, dtype=np.int32)
        bb[0] = np.maximum(det[0]-margin/2, 0)
        bb[1] = np.maximum(det[1]-margin/2, 0)
        bb[2] = np.minimum(det[2]+margin/2, img.shape[1])
        bb[3] = np.minimum(det[3]+margin/2, img.shape[0])
        ret = img[bb[1]:bb[3],bb[0]:bb[2],:]
        if len(image_size)>0:
            ret = cv2.resize(ret, (image_size[1], image_size[0]))
        return ret
    
def get_input(detector,face_img):
    # Pass input images through face detector
    ret = detector.detect_face(face_img, det_type = 0)
    if ret is None:
        return None
    bbox, points = ret
    if bbox.shape[0]==0:
        return None
    bbox = bbox[0,0:4]
    points = points[0,:].reshape((2,5)).T
    # Call preprocess() to generate aligned images
    nimg = preprocess(face_img, bbox, points, image_size='112,112')
    nimg = cv2.cvtColor(nimg, cv2.COLOR_BGR2RGB)
    aligned = np.transpose(nimg, (2,0,1))
    return aligned
    
def get_feature(model,aligned):
    input_blob = np.expand_dims(aligned, axis=0)
    data = mx.nd.array(input_blob)
    db = mx.io.DataBatch(data=(data,))
    model.forward(db, is_train=False)
    embedding = model.get_outputs()[0].asnumpy()
    embedding = sklearn.preprocessing.normalize(embedding).flatten()
    return embedding
    
# Download first image
mx.test_utils.download('https://s3.amazonaws.com/onnx-model-zoo/arcface/player1.jpg')
# Download second image
mx.test_utils.download('https://s3.amazonaws.com/onnx-model-zoo/arcface/player2.jpg')
# Download onnx model
mx.test_utils.download('https://s3.amazonaws.com/onnx-model-zoo/arcface/resnet100.onnx')
# Path to ONNX model
model_name = 'resnet100.onnx'

# Load ONNX model
model = get_model(ctx , model_name)

def inference(first,second):
  # Load first image
  img1 = cv2.imread(first)
  
  # Preprocess first image
  pre1 = get_input(detector,img1)
  
  # Get embedding of first image
  out1 = get_feature(model,pre1)
  
  # Load second image
  img2 = cv2.imread(second)
  
  # Preprocess second image
  pre2 = get_input(detector,img2)
  
  # Get embedding of second image
  out2 = get_feature(model,pre2)
  
  # Compute squared distance between embeddings
  dist = np.sum(np.square(out1-out2))
  # Compute cosine similarity between embedddings
  sim = np.dot(out1, out2.T)
  # Print predictions
  return 'Distance = %f' %(dist),'Similarity = %f' %(sim)
  
title="ArcFace"
description="ArcFace is a CNN based model for face recognition which learns discriminative features of faces and produces embeddings for input face images. To enhance the discriminative power of softmax loss, a novel supervisor signal called additive angular margin (ArcFace) is used here as an additive term in the softmax loss. ArcFace can use a variety of CNN networks as its backend, each having different accuracy and performance."
gr.Interface(inference,[gr.inputs.Image(type="filepath"),gr.inputs.Image(type="filepath")],["text","text"],title=title,description=description).launch(enable_queue=True)