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| import streamlit as st | |
| import cv2 | |
| import numpy as np | |
| from PIL import Image, ExifTags | |
| from mtcnn import MTCNN | |
| from deepface import DeepFace | |
| from transformers import BlipProcessor, BlipForConditionalGeneration | |
| # Initialize MTCNN for face detection | |
| detector = MTCNN() | |
| # Load BLIP model for scene description | |
| processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") | |
| blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base") | |
| # Function to auto-rotate the image based on EXIF data | |
| def auto_rotate_image(image): | |
| try: | |
| for orientation in ExifTags.TAGS.keys(): | |
| if ExifTags.TAGS[orientation] == 'Orientation': | |
| break | |
| exif = image._getexif() | |
| if exif is not None: | |
| orientation_value = exif.get(orientation) | |
| if orientation_value == 3: | |
| image = image.rotate(180, expand=True) | |
| elif orientation_value == 6: | |
| image = image.rotate(270, expand=True) | |
| elif orientation_value == 8: | |
| image = image.rotate(90, expand=True) | |
| except (AttributeError, KeyError, IndexError, TypeError): | |
| pass # No EXIF data or issues reading EXIF, skip rotation | |
| return image | |
| # Function to generate a scene description using BLIP | |
| def describe_scene(image): | |
| inputs = processor(image, return_tensors="pt") | |
| outputs = blip_model.generate(**inputs) | |
| caption = processor.decode(outputs[0], skip_special_tokens=True) | |
| return caption | |
| # Function for face analysis using DeepFace | |
| def analyze_face(face_image): | |
| face_array = np.array(face_image) | |
| try: | |
| result = DeepFace.analyze(face_array, actions=['gender', 'age', 'race', 'emotion'], enforce_detection=False) | |
| return result | |
| except Exception as e: | |
| return {"error": str(e)} | |
| # Main Streamlit application | |
| def main(): | |
| st.title("Comprehensive Image Analysis") | |
| st.write("Upload an image to get a scene description and detailed face analysis (gender, age, race, emotions).") | |
| # Upload image | |
| uploaded_image = st.file_uploader("Upload an image...", type=["jpg", "jpeg", "png", "webp"]) | |
| if uploaded_image is not None: | |
| image = Image.open(uploaded_image).convert("RGB") | |
| # Auto-rotate image if possible | |
| image = auto_rotate_image(image) | |
| st.image(image, caption="Uploaded Image", use_container_width=True) | |
| # Option for manual rotation | |
| rotation_angle = st.slider("Rotate Image (Degrees)", -180, 180, 0) | |
| if rotation_angle != 0: | |
| image = image.rotate(rotation_angle, expand=True) | |
| st.image(image, caption="Rotated Image", use_container_width=True) | |
| if st.button("Analyze"): | |
| with st.spinner("Analyzing..."): | |
| # Scene description | |
| scene_caption = describe_scene(image) | |
| st.subheader("Scene Description") | |
| st.write(scene_caption) | |
| # Detect faces using MTCNN | |
| faces = detector.detect_faces(np.array(image)) | |
| if faces: | |
| # Limit to only the first detected face | |
| st.write("**One Face Detected. Analyzing...**") | |
| # Get the first face | |
| face = faces[0] | |
| x, y, width, height = face['box'] | |
| face_crop = image.crop((x, y, x + width, y + height)) | |
| face_crop_resized = face_crop.resize((150, 150)) | |
| st.subheader("Face Analysis") | |
| st.image(face_crop_resized, caption="Detected Face", width=150) | |
| # Analyze face with DeepFace | |
| result = analyze_face(face_crop) | |
| if "error" in result: | |
| st.error(f"Face Analysis Error: {result['error']}") | |
| else: | |
| single_result = result[0] if isinstance(result, list) else result | |
| # Gender | |
| dominant_gender = single_result.get('dominant_gender', 'N/A') | |
| gender_confidence = int(round(single_result.get('gender', {}).get(dominant_gender, 0))) | |
| st.write(f"**Gender:** {dominant_gender.capitalize()} ({gender_confidence}%)") | |
| # Age | |
| age = single_result.get('age', 'N/A') | |
| st.write(f"**Age:** {age}") | |
| # Race | |
| race = single_result.get('race', {}) | |
| race_text = ", ".join([f"{r}: {int(round(p))}%" for r, p in race.items()]) | |
| st.write(f"**Race:** {race_text}") | |
| # Emotions | |
| emotions = single_result.get('emotion', {}) | |
| emotion_text = ", ".join([f"{e}: {int(round(p))}%" for e, p in emotions.items()]) | |
| st.write(f"**Emotions:** {emotion_text}") | |
| else: | |
| st.write("No faces detected.") | |
| if __name__ == "__main__": | |
| main() |