app.py

import cv2
from transformers import ViTImageProcessor, ViTForImageClassification, AutoModelForImageClassification, AutoImageProcessor
import torch 
import numpy as np
# import face_recognition
import subprocess
import sys
# subprocess.check_call([sys.executable, "-m", "pip", "install", 'git+https://github.com/bit-guber/retinaface.git', "--force-reinstall"])

# from retinaface import RetinaFace

from deepface import DeepFace

torch.backends.cudnn.benchmark = True

import urllib.request
path = 'https://raw.githubusercontent.com/opencv/opencv/master/data/haarcascades/haarcascade_frontalface_default.xml'
urllib.request.urlretrieve(path, path.split('/')[-1])

face_cascade = cv2.CascadeClassifier('./haarcascade_frontalface_default.xml')

class Base:
    size = 224
    scale = 1. / 255.
    mean = np.array( [ .5 ] * 3 ).reshape( 1, 1, 1, -1)
    std  = np.array( [ .5 ] * 3 ).reshape( 1, 1, 1, -1)
    resample = 2
    
class ethnicityConfig(Base):
    size = 384
    
class maskConfig(Base):
    resample = 3
    mean = np.array( [ .485 ] * 3 ).reshape( 1, 1, 1, -1)
    std  = np.array( [ .229 ] * 3 ).reshape( 1, 1, 1, -1)


AGE = "nateraw/vit-age-classifier"
GENDER = 'rizvandwiki/gender-classification-2'
ETHNICITY = 'cledoux42/Ethnicity_Test_v003'
MASK = 'DamarJati/Face-Mask-Detection'
BLUR = 'WT-MM/vit-base-blur'
BEARD = 'dima806/beard_face_image_detection'


device = 'cuda' if torch.cuda.is_available() else 'cpu'
# base_processor = ViTImageProcessor.from_pretrained( global_path + 'base_processor' )
age_model      = ViTForImageClassification.from_pretrained( AGE ).to(device)
gender_model   = ViTForImageClassification.from_pretrained( GENDER ).to(device)
beard_model    = ViTForImageClassification.from_pretrained( BEARD ).to(device)
blur_model     = ViTForImageClassification.from_pretrained( BLUR ).to(device)

# ethnicity_precessor = ViTImageProcessor.from_pretrained( global_path + 'ethnicity' )
ethnicity_model= ViTForImageClassification.from_pretrained( ETHNICITY ).to(device)

# mask_processor = ViTImageProcessor.from_pretrained( global_path + 'mask' )
mask_model     = AutoModelForImageClassification.from_pretrained( MASK ).to(device)


from PIL import Image
def normalize( data, mean, std ): # (batchs, nchannels, height, width)
    data =  (data - mean  ) / std
    return data.astype(np.float32)

def resize( image, size = 224, resample = 2  ):
#     if isinstance(iamge, np.ndarray):
#         image = Image.fromarray( image, mode = 'RGB' )
    
    image = image.resize( (size, size), resample = resample )
    
    return np.array( image )

def rescale( data, scale = Base.scale ):
    return data * scale

# resize 
# rescale
# normalize

def ParallelBatchsPredict( data, MODELS, nbatchs = 16 ):
    
    total = data.shape[0]
    # for change channel axis to first format
    data = np.transpose( data, ( 0, 3, 1, 2 ) )
    count = 0
    batchs = [ [] for i in range(len(MODELS)) ]
    for i in range( 0, total, nbatchs ):
        batch = data[i:i+nbatchs]
        count += batch.shape[0]
        with torch.no_grad():
            batch = torch.from_numpy( batch ).to(device)
            for _, model in enumerate(MODELS):
                logits = model( batch ).logits.softmax(1).argmax(1).tolist()
                for x in logits:
                    batchs[_].append( model.config.id2label[ x ] )

    assert count == total
    return batchs
# model arrange
# age 
# gender
# blur
# beard
# changle processor
# Ethnicity
# change processor
# Mask
def AnalysisFeatures(rawFaces): # list[ PIL.Image ]
    
    if len(rawFaces) == 0:
        return [ [] ]* 6
    baseProcessed = np.array([ resize(x, size = Base.size, resample = Base.resample ) for x in  rawFaces])
    baseProcessed = rescale( baseProcessed )
    baseProcessed = normalize( baseProcessed, Base.mean, Base.std )
    
    ages, genders, beards, blurs = ParallelBatchsPredict(baseProcessed,  [age_model, gender_model, beard_model, blur_model]  )
    
    EthncityProcessed = np.array([ resize(x, size = ethnicityConfig.size, resample = ethnicityConfig.resample ) for x in  rawFaces])
    EthncityProcessed = rescale( EthncityProcessed )
    EthncityProcessed = normalize( EthncityProcessed, ethnicityConfig.mean, ethnicityConfig.std )
    
    ethncities = ParallelBatchsPredict(EthncityProcessed, [ethnicity_model])[0]
    
    
    MaskProcessed = np.array([ resize(x, size = maskConfig.size, resample = maskConfig.resample ) for x in  rawFaces])
    MaskProcessed = rescale( MaskProcessed )
    MaskProcessed = normalize( MaskProcessed, maskConfig.mean, maskConfig.std )
    
    masks = ParallelBatchsPredict(MaskProcessed, [mask_model])[0] 
    
    beards = [True if beard == 'Beard' else False for beard in beards]
    blurs  = [True if blur == 'blurry' else False for blur in blurs]
    masks  = [True if mask == 'WithMask' else False for mask in masks]
    
    return ages, genders, beards, blurs, ethncities, masks


import gradio as gr

def frameWrapper( facesCo, ages, genders, beards, blurs, ethncities, masks ):
    return { 'identifiedPersonCount': len(facesCo), 'value': [ { 'coordinate': { 'x': x, 'y': y, 'h': h, 'w':w }, 'ageGroup': age, 'gender': gender, 'beardPresent':beard, 'blurOccur': blur, 'ethncity': ethncity, 'maskPresent': mask } for (x, y, w, h), age, gender, beard, blur, ethncity, mask in zip( facesCo, ages, genders, beards, blurs, ethncities, masks ) ] }

def postProcessed( rawfaces, maximunSize, minSize = 30 ):
    faces = []
    for (x, y, w, h) in rawfaces:
        x1 = x if x<maximunSize[0] else maximunSize[0]
        y1 = y if y<maximunSize[1] else maximunSize[1]
        x2 = w+x if w+x<maximunSize[0] else maximunSize[0]
        y2 = h+y if h+y<maximunSize[1] else maximunSize[1]
        
        if x2-x1 > minSize and y2-y1 >minSize:
            faces.append( (x, y, w, h) )
    return faces
def image_inference(image):

    
    if sum(image.shape) == 0:
        return image, { 'ErrorFound': 'ImageNotFound' }
    # Convert into grayscale
    # gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # Detect faces
    # rawfaces = face_cascade.detectMultiScale(gray, 1.05, 5, minSize = (30, 30))
    # image = np.asarray( image )
    # Draw rectangle around the faces
    # rawfaces = postProcessed( rawfaces, image.shape[:2] )
    
    # rawfaces = face_recognition.face_locations( image, number_of_times_to_upsample = 1 , model="hog")
    # rawfaces = []
    # for name, keys in RetinaFace.detect_faces( image ).items():
    #     rawfaces.append( keys['facial_area']  )
    # faces = [ image[top:bottom, left:right].copy() for (top, left, bottom, right) in rawfaces ]
    # faces = RetinaFace.extract_faces( image, align = True)
    # faces_mean = [ x.mean() for x in faces ]
    faces = [ x['face'] for x in  DeepFace.extract_faces( image )]
    # faces = [ image[x:w+x, y:h+y].copy() for (x, y, w, h) in rawfaces ]
    faces = [ Image.fromarray(x, mode = 'RGB') for x in faces ]
    ages, genders, beards, blurs, ethncities, masks = AnalysisFeatures( faces )

    annotatedImage = image.copy()
    for (top, left, bottom, right) in rawfaces:
        cv2.rectangle(annotatedImage, (top, left), (left, right), (255, 0, 0), 5)

    return Image.fromarray(annotatedImage, mode = 'RGB'), {'facesLength':len(faces), 'faceMean':faces_mean }#frameWrapper( rawfaces, ages, genders, beards, blurs, ethncities, masks )
    # return frameWrapper( rawfaces, ages, genders, beards, blurs, ethncities, masks )
def video_inference(video_path):
    
    global_facesCo = []
    global_faces = []
    cap = cv2.VideoCapture(video_path)
    frameCount = 0
    while(cap.isOpened()):
        _, img = cap.read()
        
        # try:
        # Convert into grayscale
            # gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        # except:
            # break
        # Detect faces
        # rawfaces = face_cascade.detectMultiScale(gray, 1.05, 6, minSize = (30, 30))
        try:
            image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
            image = np.asarray( image )
        except:
            break
        # rawfaces = postProcessed( rawfaces, image.shape[:2] )
        rawfaces = []
        for name, keys in RetinaFace.detect_faces( image ).items():
            rawfaces.append( keys['facial_area']  )
        
        # rawfaces = face_recognition.face_locations( image, number_of_times_to_upsample = 1 , model="hog")
        # Draw rectangle around the faces
        # https://stackoverflow.com/questions/15589517/how-to-crop-an-image-in-opencv-using-python for fliping axis 
        global_facesCo.append( rawfaces )
        for (top, left, bottom, right) in rawfaces:
            # face = image[x:w+x, y:h+y].copy()
            face = image[top:bottom, left:right].copy()
            global_faces.append(Image.fromarray( face , mode = 'RGB') ) 
    
    ages, genders, beards, blurs, ethncities, masks = AnalysisFeatures( global_faces )
    
    total_extraction = []
    for facesCo in global_facedsCo:
        length = len(facesCo)
        
        total_extraction.append( frameWrapper( facesCo, ages[:length], genders[:length], beards[:length], blurs[:length], ethncities[:length], masks[:length]  ) )
        
        ages, genders, beards, blurs, ethncities, masks = ages[length:], genders[length:], beards[length:], blurs[length:], ethncities[length:], masks[length:]
    return total_extraction

css = """
    .outputJSON{
        overflow: scroll;
    }
    """
imageHander = gr.Interface( fn = image_inference, inputs = gr.Image(type="numpy", sources = 'upload'), outputs = ['image', gr.JSON(elem_classes = 'outputJSON')], css = css )
videoHander = gr.Interface( fn = video_inference, inputs = gr.Video(sources = 'upload', max_length = 30, include_audio = False), outputs = 'json' )
demo = gr.TabbedInterface( [imageHander, videoHander], tab_names = [ 'Image-to-Features', 'Video-to-Features' ], title = 'Facial Feature Extraction' )

demo.launch()