Create 3_Skin_Tone_Bias.py

pages/3_Skin_Tone_Bias.py

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+import streamlit as st
+from PIL import Image, ImageDraw, ImageOps
+import cv2
+import numpy as np
+import pandas as pd
+import dlib
+import requests
+import os
+from io import BytesIO
+from facenet_pytorch import MTCNN
+
+# ---------------------------------------------------------------
+# Cache Models
+@st.cache_resource
+def load_mtcnn_model():
+    return MTCNN(keep_all=True)
+
+@st.cache_resource
+def load_dlib_detector():
+    return dlib.get_frontal_face_detector()
+
+# ---------------------------------------------------------------
+# Face Detection Functions
+def detect_faces_opencv(image):
+    cv_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+    gray = cv2.cvtColor(cv_image, cv2.COLOR_BGR2GRAY)
+    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
+    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)
+
+    result = []
+    for idx, (x, y, w, h) in enumerate(faces):
+        cv2.rectangle(cv_image, (x, y), (x+w, y+h), (0, 255, 0), 2)
+        cv2.putText(cv_image, f"Face {idx+1}", (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
+        result.append({
+            "Face ID": f"Face {idx+1}",
+            "X": x,
+            "Y": y,
+            "W": w,
+            "H": h,
+            "Confidence": "N/A"
+        })
+
+    cv_image = cv2.cvtColor(cv_image, cv2.COLOR_BGR2RGB)
+    return cv_image, result
+
+def detect_faces_mtcnn(image):
+    mtcnn = load_mtcnn_model()
+    boxes, probs = mtcnn.detect(image, landmarks=False)
+    result = []
+    draw_image = image.copy()
+    draw = ImageDraw.Draw(draw_image)
+
+    if boxes is not None:
+        for idx, (box, prob) in enumerate(zip(boxes, probs)):
+            x1, y1, x2, y2 = [int(v) for v in box]
+            draw.rectangle([x1, y1, x2, y2], outline="blue", width=2)
+            draw.text((x1, y1 - 15), f"Face {idx+1}", fill="blue")
+            result.append({
+                "Face ID": f"Face {idx+1}",
+                "X": x1,
+                "Y": y1,
+                "W": x2 - x1,
+                "H": y2 - y1,
+                "Confidence": f"{prob:.2f}"
+            })
+    return draw_image, result
+
+def detect_faces_dlib(image):
+    dlib_detector = load_dlib_detector()
+    cv_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2GRAY)
+    dets, scores, _ = dlib_detector.run(cv_image, 1, -1)
+
+    result = []
+    draw_image = image.copy()
+    draw = ImageDraw.Draw(draw_image)
+
+    for idx, (d, score) in enumerate(zip(dets, scores)):
+        if score > 0.0:
+            x1, y1, x2, y2 = d.left(), d.top(), d.right(), d.bottom()
+            draw.rectangle([x1, y1, x2, y2], outline="red", width=2)
+            draw.text((x1, y1 - 15), f"Face {idx+1}", fill="red")
+            result.append({
+                "Face ID": f"Face {idx+1}",
+                "X": x1,
+                "Y": y1,
+                "W": x2 - x1,
+                "H": y2 - y1,
+                "Confidence": f"{score:.2f}"
+            })
+    return draw_image, result
+
+# ---------------------------------------------------------------
+# Face Cropping
+def get_face_crops(image, boxes):
+    faces = []
+    for box in boxes:
+        x, y, w, h = box["X"], box["Y"], box["W"], box["H"]
+        cropped_face = image.crop((x, y, x + w, y + h))
+        cropped_face = ImageOps.fit(cropped_face, (80, 80))
+        faces.append(cropped_face)
+    return faces
+
+# ---------------------------------------------------------------
+# Main Page UI
+st.header("👤 Skin Tone Bias in Face Detection")
+
+st.markdown(
+    """
+    This page explores potential **skin tone bias** in face detection models like **Dlib**, **MTCNN**, and **OpenCV**.
+    Upload group photos with varying skin tones to observe detection differences.
+    """
+)
+
+model_choice = st.selectbox("Select a Face Detection Model", ["Dlib", "MTCNN", "OpenCV"])
+input_method = st.selectbox("Select Input Method", ["Default Images", "Upload Image", "Use Image URL"])
+image = None
+
+# Image Loading
+if input_method == "Upload Image":
+    uploaded_file = st.file_uploader("Upload a group image", type=["jpg", "jpeg", "png"])
+    if uploaded_file:
+        image = Image.open(uploaded_file).convert("RGB")
+
+elif input_method == "Use Image URL":
+    image_url = st.text_input("Paste an image URL")
+    if image_url:
+        try:
+            response = requests.get(image_url)
+            image = Image.open(BytesIO(response.content)).convert("RGB")
+        except Exception:
+            st.error("Couldn't load image from the provided URL.")
+
+elif input_method == "Default Images":
+    default_path = "default_images/skin_tone_bias"
+    if os.path.exists(default_path):
+        default_images = sorted([f for f in os.listdir(default_path) if f.lower().endswith((".jpg", ".jpeg", ".png"))])
+        if default_images:
+            selected = st.selectbox("Choose a default image", default_images)
+            image = Image.open(os.path.join(default_path, selected)).convert("RGB")
+        else:
+            st.warning("No images found in 'default_images/skin_tone_bias'.")
+    else:
+        st.warning("Folder 'default_images/skin_tone_bias' does not exist.")
+
+# Image Preview
+if image is not None:
+    st.image(image, caption="Input Image", width=400)
+
+# Detection
+if st.button("🔍 Detect Faces"):
+    if image is None:
+        st.warning("⚠️ Please provide an image before detection.")
+    else:
+        with st.spinner(f"Detecting faces using {model_choice}..."):
+            if model_choice == "OpenCV":
+                draw_image, result = detect_faces_opencv(image)
+            elif model_choice == "MTCNN":
+                draw_image, result = detect_faces_mtcnn(image)
+            elif model_choice == "Dlib":
+                draw_image, result = detect_faces_dlib(image)
+
+            if result:
+                st.success(f"✅ Detected {len(result)} face(s) with {model_choice}")
+                st.image(draw_image, caption=f"{model_choice} Detection Output", use_container_width=True)
+
+                face_images = get_face_crops(image, result)
+
+                st.markdown("### 👤 Cropped Face Previews")
+                num_faces = len(face_images)
+                cols_per_row = 3
+
+                for i in range(0, num_faces, cols_per_row):
+                    cols = st.columns(cols_per_row, gap="large")
+                    for j in range(cols_per_row):
+                        if i + j < num_faces:
+                            with cols[j]:
+                                st.image(face_images[i + j], use_container_width=True)
+                                st.markdown(
+                                    f"<div style='text-align: center;'>"
+                                    f"<b>{result[i + j]['Face ID']}</b><br>"
+                                    f"Confidence: <code>{result[i + j]['Confidence']}</code>"
+                                    f"</div>",
+                                    unsafe_allow_html=True
+                                )
+            else:
+                st.warning(f"⚠️ No faces detected by {model_choice}. Try a different model or image.")
+