app.py

from deepface import DeepFace
import gradio as gr

from PIL import Image, ImageColor
import cv2
import numpy as np
import math

FONT_SCALE = 8e-4  # Adjust for larger font size in all images
THICKNESS_SCALE = 4e-4  # Adjust for larger thickness in all images

import torch
from utils.facial_makeup import *
import torchvision.transforms as transforms

title = "DEEP FACE DEMO"
distance_metric = ["cosine", "euclidean", "euclidean_l2",]
detection_model = ["opencv", "retinaface", "mtcnn", "ssd", "dlib",]
recognition_model = ["VGG-Face", "Facenet", "OpenFace", "DeepFace", "DeepID", "ArcFace", "Dlib", "SFace",]
facial_recognition_example=[['./images/blackpink.jpg', './images/jennie.jpg'], ['./images/blackpink.jpg', './images/lisa.jpg'],\
                            ['./images/blackpink.jpg', './images/jisoo.jpg'], ['./images/blackpink.jpg', './images/rose.jpg']]
facial_analysis_example=[['./images/noone01.jpg'], ['./images/noone02.jpg'], ['./images/midu.jpg']]
facial_makeup_example=[['./images/noone01.jpg'], ['./images/noone02.jpg'], ['./images/midu.jpg']]
table = {
    'hair': 17,
    'upper_lip': 12,
    'lower_lip': 13
}

def facial_recognition(img1, img2, metric, detection, recognition):
	output = "One of the two photos does not have face."
	min_height = min(img1.shape[0],img2.shape[1])
	try:
		result = DeepFace.verify(img1_path = img1, img2_path = img2, detector_backend = detection, model_name = recognition, distance_metric=metric)
	except:
		img1 = cv2.resize(img1, (img1.shape[1], min_height), interpolation = cv2.INTER_AREA)
		img2 = cv2.resize(img2, (img2.shape[1], min_height), interpolation = cv2.INTER_AREA)
		output_img = np.concatenate((img1, img2), axis=1)
		return Image.fromarray(output_img), output 

	x1,y1,w1,h1 = result["facial_areas"]["img1"]["x"], result["facial_areas"]["img1"]["y"], result["facial_areas"]["img1"]["w"], result["facial_areas"]["img1"]["h"]
	cv2.rectangle(img1, (x1, y1), (x1 + w1, y1 + h1), (255,0,0), 4)
	img1 = cv2.resize(img1, (img1.shape[1], min_height), interpolation = cv2.INTER_AREA)
	x2,y2,w2,h2 = result["facial_areas"]["img2"]["x"], result["facial_areas"]["img2"]["y"], result["facial_areas"]["img2"]["w"], result["facial_areas"]["img2"]["h"]
	cv2.rectangle(img2, (x2, y2), (x2 + w2, y2 + h2), (255,0,0), 4)
	img2 = cv2.resize(img2, (img2.shape[1], min_height), interpolation = cv2.INTER_AREA)
	output_img = np.concatenate((img1, img2), axis=1)
	similarity = result["distance"]
	if result["verified"] is True:
		output = f"Two faces belong to the same person with a {metric} similarity of {similarity:.2f}."
	else:
		output = f"Two faces do not belong to the same person."

	return Image.fromarray(output_img), output 


def facial_analysis(img, detection):

	height, width, _ = img.shape
	font_scale = min(width, height) * FONT_SCALE
	thickness = math.ceil(min(width, height) * THICKNESS_SCALE)
	try:
		objs = DeepFace.analyze(img_path = img, actions = ['age', 'gender', 'race', 'emotion'], detector_backend = detection)
	except:
		return Image.fromarray(img)
	for obj in objs:
		x,y,w,h = obj["region"]["x"], obj["region"]["y"], obj["region"]["w"], obj["region"]["h"]
		age = obj["age"]
		gender = obj["dominant_gender"]
		race = obj["dominant_race"]
		emotion = obj["dominant_emotion"]
		cv2.rectangle(img, (x, y), (x + w, y + h), (255,0,0), 4)
		text =f"{age},{gender},{emotion}"

		cv2.putText(
			img,
			text,
			(int(x), int(y) - 10),
			fontFace = cv2.FONT_HERSHEY_SIMPLEX,
			fontScale = font_scale,
			color = (255, 0, 0),
			thickness=thickness
		)

	return Image.fromarray(img)


def facial_makeup(img_path,hair_color,lips_color):
	hair_rgb = ImageColor.getcolor(hair_color, "RGB")
	lips_rgb = ImageColor.getcolor(lips_color, "RGB")
	image = cv2.imread(img_path)
	parsing = evaluate(img_path)
	parsing = cv2.resize(parsing, (image.shape[1], image.shape[0]), interpolation=cv2.INTER_NEAREST)
	parts = [table['hair'], table['upper_lip'], table['lower_lip']]

	image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
	colors = [list(hair_rgb), list(lips_rgb), list(lips_rgb)]
	for part, color in zip(parts, colors):
		image = hair(image, parsing, part, color)	
	return Image.fromarray(image)

def main():
	demo = gr.Blocks()
	with demo:
		gr.Markdown(title)
		inputs_metric = gr.Radio(choices=distance_metric,label='Distance Metric', value="cosine",show_label=True)
		inputs_detection = gr.Dropdown(choices=detection_model,label='Detection Model',value="retinaface",show_label=True)
		inputs_recognition = gr.Dropdown(choices=recognition_model,label='Recognition Model',value="ArcFace",show_label=True)    
		with gr.Tabs():
			with gr.TabItem('Facial Recognition'):
				with gr.Row():
					gr.Markdown("Input two images, the most similar faces between two images will be compared")
				with gr.Row():
					with gr.Column():
						Recognition_inputs_image1 = gr.Image(label='Image 1',interactive=True)
						Recognition_inputs_image2 = gr.Image(label='Image 2',interactive=True)
					with gr.Column():
						Recognition_outputs_image = gr.Image(type="pil", label="Output Image")
						# Recognition_outputs_text = gr.Textbox(label="Output")
						Recognition_outputs_text = gr.Label(label='Output')
				with gr.Row():
					Recognition_example_images = gr.Examples(examples=facial_recognition_example,inputs=[Recognition_inputs_image1,Recognition_inputs_image2])

				verify_but = gr.Button('Verify') 
			with gr.TabItem('Facial Analysis'):
				with gr.Row():
					gr.Markdown("Input image, return results including age, gender, race and emotion of all faces.")
				with gr.Row():
					with gr.Column():
						Analysis_inputs_image = gr.Image(label='Image',interactive=True)
					with gr.Column():
						Analysis_outputs_image = gr.Image(type="pil", label="Output Image")
				with gr.Row():
					Analysis_example_images = gr.Examples(examples=facial_analysis_example,inputs=[Analysis_inputs_image])
				analysis_but = gr.Button("Analysis")

			with gr.TabItem('Facial MakeUp'):
				with gr.Row():
					gr.Markdown("Input image, choose hair and lips color, return image with selected makeup.")
				with gr.Row():
					with gr.Column():
						MakeUp_inputs_image = gr.Image(label='Image',type='filepath',interactive=True)
						MakeUp_inputs_hair = gr.ColorPicker(label="Hair Color")
						MakeUp_inputs_lips = gr.ColorPicker(label="Lips Color")
					with gr.Column():
						MakeUp_outputs_image = gr.Image(type="pil", label="Output Image")
				with gr.Row():
					MakeUp_example_images = gr.Examples(examples=facial_makeup_example,inputs=[MakeUp_inputs_image])
				makeup_but = gr.Button("MakeUp")
		verify_but.click(facial_recognition,inputs=[Recognition_inputs_image1,Recognition_inputs_image2,inputs_metric,inputs_detection,inputs_recognition],\
						outputs=[Recognition_outputs_image,Recognition_outputs_text],queue=True)
		analysis_but.click(facial_analysis,inputs=[Analysis_inputs_image,inputs_detection],outputs=[Analysis_outputs_image],queue=True)
		makeup_but.click(facial_makeup,inputs=[MakeUp_inputs_image,MakeUp_inputs_hair,MakeUp_inputs_lips],outputs=[MakeUp_outputs_image],queue=True)

	demo.launch(debug=True,enable_queue=True,server_name="0.0.0.0")



if __name__ == "__main__":
	main()