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requirements.txt

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+streamlit==1.19.0
+Pillow==9.3.0
+deepface==0.0.75
+opencv-python-headless==4.6.0.66
+pandas==1.5.3

run.sh

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+streamlit run test.py

test.py

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+import streamlit as st
+from PIL import Image
+from deepface import DeepFace
+import tempfile
+import pandas as pd
+import cv2 as cv
+import threading
+from time import sleep
+
+st.title('Image Upload and Verification App')
+
+st.write('Please upload two images for facial verification.')
+
+# Upload two images
+uploaded_file1 = st.file_uploader("Choose the first image...", type=["jpg", "png", "jpeg"], key="1")
+uploaded_file2 = st.file_uploader("Choose the second image...", type=["jpg", "png", "jpeg"], key="2")
+
+# Define the global variables
+df = None
+analyze_img1 = None
+analyze_img2 = None
+
+def verify(img1_path, img2_path):
+    global df
+    model_name = 'VGG-Face'  # You can change this to other models like "Facenet", "OpenFace", "DeepFace", etc.
+    result = DeepFace.verify(img1_path=img1_path, img2_path=img2_path, model_name=model_name)
+    result["img1_facial_areas"] = result["facial_areas"]["img1"]
+    result["img2_facial_areas"] = result["facial_areas"]["img2"]
+    del result["facial_areas"]
+    df = pd.DataFrame([result])
+
+def analyze_image1(img1_path):
+    global analyze_img1
+    analyze_img1 = DeepFace.analyze(img_path=img1_path)[0]
+
+def analyze_image2(img2_path):
+    global analyze_img2
+    analyze_img2 = DeepFace.analyze(img_path=img2_path)[0]
+
+def generate_analysis_sentence(analysis):
+    age = analysis['age']
+    gender = [i for i in analysis['gender'].keys()][-1]
+    dominant_emotion = analysis['dominant_emotion']
+    dominant_race = analysis['dominant_race']
+    
+    # Highlight specific words in blue
+    age_html = f"<span style='color:blue'>{age}</span>"
+    gender_html = f"<span style='color:blue'>{gender}</span>"
+    dominant_emotion_html = f"<span style='color:blue'>{dominant_emotion}</span>"
+    dominant_race_html = f"<span style='color:blue'>{dominant_race}</span>"
+    
+    return f"""The person in the image appears to be {age_html} years old, identified as '{gender_html}'.
+    The dominant emotion detected is {dominant_emotion_html}. 
+    Ethnicity prediction indicates {dominant_race_html}."""
+
+def display_image_with_analysis(image, analysis):
+    # Display the image
+    st.image(image, caption='Image', use_column_width=True)
+
+    # Display the analysis results
+    st.write("Analysis:")
+    st.markdown(generate_analysis_sentence(analysis), unsafe_allow_html=True)
+
+def drow_rectangle():
+    # Load images with OpenCV
+    img1 = cv.imread(img1_path)
+    img2 = cv.imread(img2_path)
+
+    # Get facial areas and draw rectangles
+    face_area1 = df.iloc[0]["img1_facial_areas"]
+    p1_1 = (face_area1["x"], face_area1["y"])
+    p2_1 = (face_area1["x"] + face_area1["w"], face_area1["y"] + face_area1["h"])
+    rect_img1 = cv.rectangle(img1.copy(), p1_1, p2_1, (0, 255, 0), 2)
+
+    face_area2 = df.iloc[0]["img2_facial_areas"]
+    p1_2 = (face_area2["x"], face_area2["y"])
+    p2_2 = (face_area2["x"] + face_area2["w"], face_area2["y"] + face_area2["h"])
+    rect_img2 = cv.rectangle(img2.copy(), p1_2, p2_2, (0, 255, 0), 2)
+
+    # Resize images with a better interpolation method
+    rect_img1 = cv.cvtColor(rect_img1, cv.COLOR_BGR2RGB)
+    rect_img1 = cv.resize(rect_img1, (200, 250), interpolation=cv.INTER_AREA)
+
+    rect_img2 = cv.cvtColor(rect_img2, cv.COLOR_BGR2RGB)
+    rect_img2 = cv.resize(rect_img2, (200, 250), interpolation=cv.INTER_AREA)
+
+    #st.dataframe(df)
+
+    # Display the results
+    if df["verified"].iloc[0]:
+        message = "The faces in the images match!"
+    else:
+        message = "The faces in the images do not match!"
+
+    st.title(message)
+
+    col1, col2 = st.columns(2)
+    col1.image(rect_img1, caption='Verified Image 1', use_column_width=True)
+    col2.image(rect_img2, caption='Verified Image 2', use_column_width=True)
+
+def get_analyze():
+    # Display the analysis results
+    st.write("Analysis for Image 1:")
+    try:
+        st.markdown(generate_analysis_sentence(analyze_img1), unsafe_allow_html=True)
+    except: 
+        st.warning("can't detect image 1")
+
+    st.write("Analysis for Image 2:")
+    try:
+        st.markdown(generate_analysis_sentence(analyze_img2), unsafe_allow_html=True)
+    except: 
+        st.warning("can't detect image 2")
+
+
+col1, col2 = st.columns(2)
+with col1:
+    st.text("Check if the faces in the images match!")
+    check = st.button("Check")
+with col2:
+    st.text("Analyze the faces in each image!")
+    analyze = st.button("Analyze")
+
+if uploaded_file1 is not None and uploaded_file2 is not None:
+    # Open the images with PIL
+    image1 = Image.open(uploaded_file1)
+    image2 = Image.open(uploaded_file2)
+
+    st.write("Here are your images:")
+
+    # Convert images to RGB if they are in RGBA mode
+    if image1.mode == 'RGBA':
+        image1 = image1.convert('RGB')
+    if image2.mode == 'RGBA':
+        image2 = image2.convert('RGB')
+
+    # Save the uploaded images to a temporary directory
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp_file1:
+        image1.save(tmp_file1.name)
+        img1_path = tmp_file1.name
+
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp_file2:
+        image2.save(tmp_file2.name)
+        img2_path = tmp_file2.name
+
+    t1 = threading.Thread(target=verify, args=(img1_path, img2_path))
+    t2 = threading.Thread(target=analyze_image1, args=(img1_path,))
+    t3 = threading.Thread(target=analyze_image2, args=(img2_path,))
+    t1.start()
+    t2.start()
+    t3.start()
+    t1.join()
+
+
+    if check and not t1.is_alive():
+        n = 0
+        while True:
+            try:
+                drow_rectangle()
+                sleep(2)
+                break
+            except:
+                n = n + 1
+                print(f"Try : {n}")
+                if n == 4:
+                    st.warning("Please make sure there are people's faces in each of the two photos or try again")
+                    break
+
+    t2.join()
+    t3.join()
+    if analyze:
+        n = 0
+
+        while t2.is_alive() or t3.is_alive():
+            sleep(2)
+        while True:
+            try:
+                get_analyze()
+                sleep(2)
+                break
+            except:
+                n = n + 1
+                print(f"Try : {n}")
+                if n == 4:
+                    st.warning("Please make sure there are people's faces in each of the two photos or try again")
+                    break
+
+
+else:
+    st.write("Please upload both images to proceed.")