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app.py

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+import io
+from deepface import DeepFace
+import pandas as pd
+import gradio as gr
+import matplotlib.pyplot as plt
+import requests, validators
+import torch
+import pathlib
+from PIL import Image
+import os
+
+
+os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
+
+def get_original_image(url_input):
+    """Extract image from URL"""
+    
+    if validators.url(url_input):
+        image = Image.open(requests.get(url_input, stream=True).raw)
+        
+        return image
+    
+def face_verification(img1, img2, dist,model,detector):
+    """Check the similarity of 2 images"""
+    
+    try:
+        result = DeepFace.verify(img1_path=img1,img2_path=img2,distance_metric=dist,model_name=model,detector_backend=detector)
+    except:
+        result = DeepFace.verify(img1_path=img1,img2_path=img2,distance_metric=dist,model_name=model,detector_backend=detector,\
+                                enforce_detection=False)
+        
+    return result['verified'],round(result['distance'],2),result['threshold'],result['model'],result['similarity_metric']
+
+def facial_analysis(img1, detector):
+    """Determine emotion, race, gender and age from models"""
+    
+    try:
+        #facial analysis
+        obj = DeepFace.analyze(img_path = img1, actions = ['age', 'gender', 'race', 'emotion'],detector_backend=detector)
+    except:
+        obj = DeepFace.analyze(img_path = img1, actions = ['age', 'gender', 'race', 'emotion'],detector_backend=detector,\
+                              enforce_detection=False)
+    
+    return obj['age'],obj['gender'],obj['dominant_race'],obj['dominant_emotion']
+
+def face_recognition(img1,dir_loc,model,dist,detector):
+    """Facial recognition given a database or folder location with images"""
+    
+    #face recognition
+    rec = DeepFace.find(img_path = img_1, db_path = dir_loc,distance_metric=dist,model_name=model,detector_backend=detector)
+    
+    return rec
+            
+        
+def set_example_image(example: list) -> dict:
+    return gr.Image.update(value=example[0])
+
+def set_example_url(example: list) -> dict:
+    return gr.Textbox.update(value=example[0]), gr.Image.update(value=get_original_image(example[0]))
+
+
+title = """<h1 id="title">DeepFace for Facial Recognition and Analysis</h1>"""
+
+description = """
+Deepface is a lightweight face recognition and facial attribute analysis (age, gender, emotion and race) framework for python. It is a hybrid face recognition framework wrapping state-of-the-art models: VGG-Face, Google FaceNet, OpenFace, Facebook DeepFace, DeepID, ArcFace, Dlib and SFace.
+
+Experiments show that human beings have 97.53% accuracy on facial recognition tasks whereas those models already reached and passed that accuracy level.
+Please click on the Github link for more information: [DeepFace](https://github.com/serengil/deepface)
+
+This space captures facial verification which determines if 2 facial images are the same person and the facial attribute analysis which predicts age,gender, emotion and race. The attribute analysis for age and race is a hit and miss based on my personal experience and the reported test accuracy is 68% for race prediction
+The age prediction model got ± 4.65 MAE.
+"""
+
+models = ["VGG-Face", "Facenet", "Facenet512", "OpenFace", "DeepFace", "DeepID", "ArcFace", "Dlib", "SFace"]
+metrics = ["cosine", "euclidean", "euclidean_l2"]
+backends = ['opencv', 'ssd', 'dlib', 'mtcnn', 'retinaface', 'mediapipe']
+
+urls = [["https://media.vanityfair.com/photos/6036a15657f37ea4415256d2/master/w_2560%2Cc_limit/1225292516",\
+        "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQSuPVx0JaEW2yp4mT8ZwqFANMT3RhoxcwuuGLhnKgxsA&s"]]
+
+url = [['https://media.vanityfair.com/photos/6036a15657f37ea4415256d2/master/w_2560%2Cc_limit/1225292516']]
+
+twitter_link = """
+[![](https://img.shields.io/twitter/follow/nickmuchi?label=@nickmuchi&style=social)](https://twitter.com/nickmuchi)
+"""
+
+css = '''
+h1#title {
+  text-align: center;
+}
+'''
+demo = gr.Blocks(css=css)
+
+with demo:
+    gr.Markdown(title)
+    gr.Markdown(description)
+    gr.Markdown(twitter_link)
+    model_options = gr.Dropdown(choices=models,label='Facial Recognition Models',value=models[1],show_label=True)
+    metric_options = gr.Radio(choices=metrics,label='Distance Metric', value=metrics[0],show_label=True)
+    backends_options = gr.Dropdown(choices=backends,label='Face Detector',value=backends[-2],show_label=True)
+    
+    with gr.Tabs():
+        with gr.TabItem('Facial Recognition'):
+            with gr.Tabs():
+                with gr.TabItem("URL Images"):
+                    
+                    with gr.Row():
+                        with gr.Column():
+                            url_input_1_fr = gr.Textbox(lines=2,label='Image URL 1')
+                            url_image_1_fr = gr.Image(label='Image 1',shape=(550,550),interactive=False)
+                            url_input_1_fr.change(get_original_image, url_input_1_fr, url_image_1_fr)
+                            url_input_2 = gr.Textbox(lines=2,label='Image URL 2')
+                            url_image_2 = gr.Image(label='Image 2',shape=(550,550),interactive=False)
+                            url_input_2.change(get_original_image, url_input_2, url_image_2)
+                            
+                        with gr.Column():
+                            sim_from_url = gr.Label(label='Same Person')
+                            dist_from_url = gr.Label(label = 'Distance')
+                            thresh_from_url = gr.Label(label = 'Threshold to Verify')
+                            model_from_url = gr.Label(label = 'Model Name')
+                            metric_from_url = gr.Label(label = 'Similarity Metric')
+                            
+                            
+                    with gr.Row():
+                        example_url = gr.Examples(examples=urls,inputs=[url_input_1_fr,url_input_2])
+                        
+                    url_but_fr = gr.Button('Verify')
+                            
+                with gr.TabItem("Upload Images"):
+                    
+                    with gr.Row():
+                        with gr.Column():
+                            upload_image_1_fr = gr.Image(label='Image 1',shape=(550,550),interactive=True)
+                            upload_image_2 = gr.Image(label='Image 2',shape=(550,550),interactive=True)
+                        with gr.Column():
+                            sim_from_upload = gr.Label(label='Same Person')
+                            dist_from_upload = gr.Label(label = 'Distance')
+                            thresh_from_upload = gr.Label(label = 'Threshold to Verify')
+                            model_from_upload = gr.Label(label = 'Model Name')
+                            metric_from_upload = gr.Label(label = 'Similarity Metric')
+                            
+                    with gr.Row():
+                        example_images = gr.Dataset(components=[upload_image_1_fr],
+                                        samples=[[path.as_posix()] for path in sorted(pathlib.Path('images').rglob('*.j*g'))])
+                            
+                    up_but_fr = gr.Button('Verify')          
+                
+     
+        with gr.TabItem('Facial Analysis'):
+                
+            with gr.Tabs():
+                with gr.TabItem("URL Image"):
+                    
+                    with gr.Row():
+                        with gr.Column():
+                            url_input_1_fa = gr.Textbox(lines=2,label='Enter valid image URL here..')
+                            url_image_1_fa = gr.Image(label='Image 1',shape=(550,550))
+                            url_input_1_fa.change(get_original_image, url_input_1_fa, url_image_1_fa)
+                            
+                            
+                        with gr.Column():
+                            age_from_url = gr.Label(label='Age')
+                            gender_from_url = gr.Label(label = 'Gender')
+                            emo_from_url = gr.Label(label = 'Emotion')
+                            race_from_url = gr.Label(label = 'Race')
+                                     
+                    with gr.Row():
+                        example_url = gr.Examples(examples=url,inputs=[url_input_1_fa])
+                        
+                    url_but_fa = gr.Button('Analyze')
+                            
+                with gr.TabItem("Upload Image"):
+                    
+                    with gr.Row():
+                        with gr.Column():
+                            upload_image_1_fa = gr.Image(label='Image 1',shape=(550,550))
+                            
+                        with gr.Column():
+                            age_from_upload = gr.Label(label='Age')
+                            gender_from_upload = gr.Label(label = 'Gender')
+                            emo_from_upload = gr.Label(label = 'Emotion')
+                            race_from_upload = gr.Label(label = 'Race')
+                            
+                    with gr.Row():
+                        example_images = gr.Dataset(components=[upload_image_1_fa],
+                                        samples=[[path.as_posix()] for path in sorted(pathlib.Path('images').rglob('*.j*g'))])
+                            
+                    up_but_fa = gr.Button('Analyze')
+                    
+                    
+                with gr.TabItem("WebCam Image"):
+                    
+                    with gr.Row():
+                        with gr.Column():
+                            web_image = gr.Image(label='WebCam Image',source='webcam',shape=(550,550),streaming=True)
+                            
+                        with gr.Column():
+                            age_from_web = gr.Label(label='Age')
+                            gender_from_web = gr.Label(label = 'Gender')
+                            emo_from_web = gr.Label(label = 'Emotion')
+                            race_from_web = gr.Label(label = 'Race')
+                            
+                    web_but_fa = gr.Button('Analyze')
+            
+    url_but_fr.click(face_verification,inputs=[url_image_1_fr,url_image_2,metric_options,model_options,backends_options],\
+                     outputs=[sim_from_url,dist_from_url,thresh_from_url,model_from_url,metric_from_url],queue=True)
+    up_but_fr.click(face_verification,inputs=[upload_image_1_fr,upload_image_2,metric_options,model_options,backends_options],\
+                    outputs=[sim_from_upload,dist_from_upload,thresh_from_upload,model_from_upload,metric_from_upload],queue=True)
+    url_but_fa.click(facial_analysis,inputs=[url_image_1_fa,backends_options],\
+                     outputs=[age_from_url,gender_from_url,race_from_url,emo_from_url],queue=True)
+    up_but_fa.click(facial_analysis,inputs=[upload_image_1_fa,backends_options],\
+                    outputs=[age_from_upload,gender_from_upload,race_from_upload,emo_from_upload])
+    web_but_fa.click(facial_analysis,inputs=[web_image,backends_options],\
+                     outputs=[age_from_web,gender_from_web,race_from_web,emo_from_web])
+    
+
+    gr.Markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=nickmuchi-deepface)")
+
+    
+demo.launch(debug=True,enable_queue=True)