Create app.py

app.py

@@ -0,0 +1,76 @@
+import os
+import cv2
+import mediapipe as mp
+import streamlit as st
+from PIL import Image
+import numpy as np
+
+# Initialize the MediaPipe Face Detection and Drawing utilities
+mp_face_detection = mp.solutions.face_detection
+mp_drawing = mp.solutions.drawing_utils
+
+# Function to detect faces and save each detected face as an image
+def detect_and_save_faces(image, output_folder="output"):
+    if not os.path.exists(output_folder):
+        os.makedirs(output_folder)
+
+    # Convert image to RGB for MediaPipe
+    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+    # Detect faces
+    with mp_face_detection.FaceDetection(min_detection_confidence=0.5) as face_detection:
+        results = face_detection.process(image_rgb)
+
+        face_images = []
+        # Draw face detections and save each face
+        if results.detections:
+            for idx, detection in enumerate(results.detections):
+                bboxC = detection.location_data.relative_bounding_box
+                h, w, _ = image.shape
+
+                # Get the bounding box coordinates
+                x_min = int(bboxC.xmin * w)
+                y_min = int(bboxC.ymin * h)
+                box_width = int(bboxC.width * w)
+                box_height = int(bboxC.height * h)
+
+                # Extract the face from the image
+                face_image = image[y_min:y_min + box_height, x_min:x_min + box_width]
+                face_images.append(face_image)
+
+                # Save the detected face
+                face_output_path = os.path.join(output_folder, f"face_{idx+1}.jpg")
+                cv2.imwrite(face_output_path, face_image)
+
+        # Draw bounding boxes on the original image
+        for detection in results.detections:
+            mp_drawing.draw_detection(image, detection)
+
+    return image, face_images
+
+# Streamlit UI for face detection
+st.title("Face Detection and Extraction")
+
+# File uploader
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Convert the uploaded file to an OpenCV image
+    file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
+    image = cv2.imdecode(file_bytes, 1)
+
+    # Run face detection and save faces
+    output_folder = "output"
+    detected_image, face_images = detect_and_save_faces(image, output_folder)
+
+    # Display the original image with detected faces
+    st.subheader("Original Image with Detected Faces")
+    st.image(cv2.cvtColor(detected_image, cv2.COLOR_BGR2RGB), caption="Detected Faces", use_column_width=True)
+
+    # Display the extracted faces
+    st.subheader("Extracted Faces")
+    if face_images:
+        for idx, face in enumerate(face_images):
+            st.image(cv2.cvtColor(face, cv2.COLOR_BGR2RGB), caption=f"Face {idx+1}", use_column_width=False)
+    else:
+        st.write("No faces detected.")