Update app.py

app.py

@@ -1,8 +1,9 @@
 from deepface import DeepFace
 import gradio as gr
-from PIL import Image
+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
@@ -19,6 +20,7 @@ recognition_model = ["VGG-Face", "Facenet", "OpenFace", "DeepFace", "DeepID", "A
 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/jennie.jpg'], ['./images/lisa.jpg'], ['./images/jisoo.jpg'], ['./images/rose.jpg'], ['./images/midu.jpg']]
+facial_makeup_example=[['./images/jennie.jpg'], ['./images/lisa.jpg'], ['./images/jisoo.jpg'], ['./images/rose.jpg'], ['./images/midu.jpg']]
 
 def facial_recognition(img1, img2, metric, detection, recognition):
 	output = "One of the two photos does not have face."
@@ -77,6 +79,25 @@ def facial_analysis(img, detection):
 
 	return Image.fromarray(img)
 
+table = {
+    'hair': 17,
+    'upper_lip': 12,
+    'lower_lip': 13
+}
+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:
@@ -111,9 +132,22 @@ def main():
 				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():
+					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")