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import cv2
from transformers import ViTImageProcessor, ViTForImageClassification, AutoModelForImageClassification, AutoImageProcessor
import torch
import numpy as np
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 { '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] )
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 (x, y, w, h) in rawfaces:
# cv2.rectangle(annotatedImage, (x, y), (x+w, y+h), (255, 0, 0), 2)
# return Image.fromarray(annotatedImage, mode = 'RGB'), 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))
image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
image = np.asarray( image )
rawfaces = postProcessed( rawfaces, image.shape[:2] )
# 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 (x, y, w, h) in rawfaces:
face = image[x:w+x, y:h+y].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 = 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() |