app.py

import gradio as gr
import cv2
import numpy as np
import torch
from torchvision import models, transforms
from torchvision.models.detection import FasterRCNN_ResNet50_FPN_Weights
from PIL import Image
import mediapipe as mp
from fer import FER  # Facial emotion recognition
from transformers import AutoFeatureExtractor, AutoModel

# -----------------------------
# Configuration
# -----------------------------
SKIP_RATE = 1  # For image processing, always run the analysis
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
DESIRED_SIZE = (640, 480)

# -----------------------------
# Global caches for overlay info and frame counters
# -----------------------------
posture_cache = {"landmarks": None, "text": "Initializing...", "counter": 0}
emotion_cache = {"text": "Initializing...", "counter": 0}
objects_cache = {"boxes": None, "text": "Initializing...", "object_list_text": "", "counter": 0}
faces_cache = {"boxes": None, "text": "Initializing...", "counter": 0}

# -----------------------------
# Initialize Models and Helpers
# -----------------------------
# MediaPipe Pose and Face Detection
mp_pose = mp.solutions.pose
pose = mp_pose.Pose()
mp_drawing = mp.solutions.drawing_utils

mp_face_detection = mp.solutions.face_detection
face_detection = mp_face_detection.FaceDetection(min_detection_confidence=0.5)

# Object Detection using Faster R-CNN
object_detection_model = models.detection.fasterrcnn_resnet50_fpn(
    weights=FasterRCNN_ResNet50_FPN_Weights.DEFAULT
)
object_detection_model.eval().to(device)
obj_transform = transforms.Compose([transforms.ToTensor()])

# Initialize the FER emotion detector (using the FER package)
emotion_detector = FER(mtcnn=True)

# Retrieve object categories from model weights metadata
object_categories = FasterRCNN_ResNet50_FPN_Weights.DEFAULT.meta["categories"]

# -----------------------------
# Facial Recognition Model (DINO-ViT)
# -----------------------------
facial_recognition_extractor = AutoFeatureExtractor.from_pretrained("facebook/dino-vitb16")
facial_recognition_model = AutoModel.from_pretrained("facebook/dino-vitb16")
facial_recognition_model.to(device)
facial_recognition_model.eval()

# -----------------------------
# Overlay Drawing Functions
# -----------------------------
def draw_posture_overlay(raw_frame, landmarks):
    # Draw connector lines using MediaPipe's POSE_CONNECTIONS
    for connection in mp_pose.POSE_CONNECTIONS:
        start_idx, end_idx = connection
        if start_idx < len(landmarks) and end_idx < len(landmarks):
            start_point = landmarks[start_idx]
            end_point = landmarks[end_idx]
            cv2.line(raw_frame, start_point, end_point, (50, 205, 50), 2)
    # Draw landmark points in lime green (BGR: (50,205,50))
    for (x, y) in landmarks:
        cv2.circle(raw_frame, (x, y), 4, (50, 205, 50), -1)
    return raw_frame

def draw_boxes_overlay(raw_frame, boxes, color):
    for (x1, y1, x2, y2) in boxes:
        cv2.rectangle(raw_frame, (x1, y1), (x2, y2), color, 2)
    return raw_frame

# -----------------------------
# Heavy (Synchronous) Detection Functions
# -----------------------------
def compute_posture_overlay(image):
    frame_bgr = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
    h, w, _ = frame_bgr.shape
    frame_bgr_small = cv2.resize(frame_bgr, DESIRED_SIZE)
    small_h, small_w, _ = frame_bgr_small.shape
    frame_rgb_small = cv2.cvtColor(frame_bgr_small, cv2.COLOR_BGR2RGB)
    pose_results = pose.process(frame_rgb_small)
    if pose_results.pose_landmarks:
        landmarks = []
        for lm in pose_results.pose_landmarks.landmark:
            # Scale landmarks back to the original image size
            x = int(lm.x * small_w * (w / small_w))
            y = int(lm.y * small_h * (h / small_h))
            landmarks.append((x, y))
        text = "Posture detected"
    else:
        landmarks = []
        text = "No posture detected"
    return landmarks, text

def compute_emotion_overlay(image):
    # Use the FER package (exactly as in your provided code)
    frame_bgr = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
    frame_bgr_small = cv2.resize(frame_bgr, DESIRED_SIZE)
    frame_rgb_small = cv2.cvtColor(frame_bgr_small, cv2.COLOR_BGR2RGB)
    emotions = emotion_detector.detect_emotions(frame_rgb_small)
    if emotions:
        top_emotion, score = max(emotions[0]["emotions"].items(), key=lambda x: x[1])
        text = f"{top_emotion} ({score:.2f})"
    else:
        text = "No face detected"
    return text

def compute_objects_overlay(image):
    frame_bgr = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
    frame_bgr_small = cv2.resize(frame_bgr, DESIRED_SIZE)
    frame_rgb_small = cv2.cvtColor(frame_bgr_small, cv2.COLOR_BGR2RGB)
    image_pil = Image.fromarray(frame_rgb_small)
    img_tensor = obj_transform(image_pil).to(device)
    with torch.no_grad():
        detections = object_detection_model([img_tensor])[0]
    threshold = 0.8
    boxes = []
    object_list = []
    for box, score, label in zip(detections["boxes"], detections["scores"], detections["labels"]):
        if score > threshold:
            boxes.append(tuple(box.int().cpu().numpy()))
            label_idx = int(label)
            label_name = object_categories[label_idx] if label_idx < len(object_categories) else "Unknown"
            object_list.append(f"{label_name} ({score:.2f})")
    text = f"Detected {len(boxes)} object(s)" if boxes else "No objects detected"
    object_list_text = " | ".join(object_list) if object_list else "None"
    return boxes, text, object_list_text

def compute_faces_overlay(image):
    frame_bgr = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
    h, w, _ = frame_bgr.shape
    frame_bgr_small = cv2.resize(frame_bgr, DESIRED_SIZE)
    small_h, small_w, _ = frame_bgr_small.shape
    frame_rgb_small = cv2.cvtColor(frame_bgr_small, cv2.COLOR_BGR2RGB)
    face_results = face_detection.process(frame_rgb_small)
    boxes = []
    if face_results.detections:
        for detection in face_results.detections:
            bbox = detection.location_data.relative_bounding_box
            x = int(bbox.xmin * small_w)
            y = int(bbox.ymin * small_h)
            box_w = int(bbox.width * small_w)
            box_h = int(bbox.height * small_h)
            boxes.append((x, y, x + box_w, y + box_h))
        text = f"Detected {len(boxes)} face(s)"
    else:
        text = "No faces detected"
    return boxes, text

def compute_facial_recognition_vector(image):
    """
    Detects a face using MediaPipe, crops it, and computes its embedding vector
    using facebook/dino-vitb16. The raw vector is returned as a string.
    """
    frame_bgr = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
    frame_bgr_small = cv2.resize(frame_bgr, DESIRED_SIZE)
    frame_rgb_small = cv2.cvtColor(frame_bgr_small, cv2.COLOR_BGR2RGB)
    face_results = face_detection.process(frame_rgb_small)
    if face_results.detections:
        detection = face_results.detections[0]
        bbox = detection.location_data.relative_bounding_box
        h, w, _ = frame_rgb_small.shape
        x = int(bbox.xmin * w)
        y = int(bbox.ymin * h)
        box_w = int(bbox.width * w)
        box_h = int(bbox.height * h)
        face_crop = frame_rgb_small[y:y+box_h, x:x+box_w]
        face_image = Image.fromarray(face_crop)
        inputs = facial_recognition_extractor(face_image, return_tensors="pt").to(device)
        with torch.no_grad():
            outputs = facial_recognition_model(**inputs)
        # Mean pooling of the last hidden state to obtain a vector representation
        vector = outputs.last_hidden_state.mean(dim=1).squeeze()
        vector_np = vector.cpu().numpy()
        # Format vector as a string with limited decimal places
        vector_str = np.array2string(vector_np, precision=2, separator=',')
        return face_crop, vector_str
    else:
        return np.array(image), "No face detected"

# -----------------------------
# Main Analysis Functions for Single Image
# -----------------------------
def analyze_posture_current(image):
    global posture_cache
    posture_cache["counter"] += 1
    current_frame = np.array(image)
    if posture_cache["counter"] % SKIP_RATE == 0 or posture_cache["landmarks"] is None:
        landmarks, text = compute_posture_overlay(image)
        posture_cache["landmarks"] = landmarks
        posture_cache["text"] = text
    output = current_frame.copy()
    if posture_cache["landmarks"]:
        output = draw_posture_overlay(output, posture_cache["landmarks"])
    return output, f"<div style='color: lime !important;'>Posture Analysis: {posture_cache['text']}</div>"

def analyze_emotion_current(image):
    global emotion_cache
    emotion_cache["counter"] += 1
    current_frame = np.array(image)
    if emotion_cache["counter"] % SKIP_RATE == 0 or emotion_cache["text"] is None:
        text = compute_emotion_overlay(image)
        emotion_cache["text"] = text
    return current_frame, f"<div style='color: lime !important;'>Emotion Analysis: {emotion_cache['text']}</div>"

def analyze_objects_current(image):
    global objects_cache
    objects_cache["counter"] += 1
    current_frame = np.array(image)
    if objects_cache["counter"] % SKIP_RATE == 0 or objects_cache["boxes"] is None:
        boxes, text, object_list_text = compute_objects_overlay(image)
        objects_cache["boxes"] = boxes
        objects_cache["text"] = text
        objects_cache["object_list_text"] = object_list_text
    output = current_frame.copy()
    if objects_cache["boxes"]:
        output = draw_boxes_overlay(output, objects_cache["boxes"], (255, 255, 0))
    combined_text = f"Object Detection: {objects_cache['text']}<br>Details: {objects_cache['object_list_text']}"
    return output, f"<div style='color: lime !important;'>{combined_text}</div>"

def analyze_faces_current(image):
    global faces_cache
    faces_cache["counter"] += 1
    current_frame = np.array(image)
    if faces_cache["counter"] % SKIP_RATE == 0 or faces_cache["boxes"] is None:
        boxes, text = compute_faces_overlay(image)
        faces_cache["boxes"] = boxes
        faces_cache["text"] = text
    output = current_frame.copy()
    if faces_cache["boxes"]:
        output = draw_boxes_overlay(output, faces_cache["boxes"], (0, 0, 255))
    return output, f"<div style='color: lime !important;'>Face Detection: {faces_cache['text']}</div>"

def analyze_facial_recognition(image):
    # Compute and return the facial vector (and the cropped face)
    face_crop, vector_str = compute_facial_recognition_vector(image)
    return face_crop, f"<div style='color: lime !important;'>Facial Vector: {vector_str}</div>"

def analyze_all(image):
    current_frame = np.array(image).copy()
    # Posture Analysis
    landmarks, posture_text = compute_posture_overlay(image)
    if landmarks:
        current_frame = draw_posture_overlay(current_frame, landmarks)
    # Emotion Analysis
    emotion_text = compute_emotion_overlay(image)
    # Object Detection
    boxes_obj, objects_text, object_list_text = compute_objects_overlay(image)
    if boxes_obj:
        current_frame = draw_boxes_overlay(current_frame, boxes_obj, (255, 255, 0))
    # Face Detection
    boxes_face, faces_text = compute_faces_overlay(image)
    if boxes_face:
        current_frame = draw_boxes_overlay(current_frame, boxes_face, (0, 0, 255))
    # Combined Analysis Text
    combined_text = (
        f"<b>Posture Analysis:</b> {posture_text}<br>"
        f"<b>Emotion Analysis:</b> {emotion_text}<br>"
        f"<b>Object Detection:</b> {objects_text}<br>"
        f"<b>Detected Objects:</b> {object_list_text}<br>"
        f"<b>Face Detection:</b> {faces_text}"
    )
    if object_list_text and object_list_text != "None":
        description_text = f"Image Description: The scene features {object_list_text}."
    else:
        description_text = "Image Description: No prominent objects detected."
    combined_text += f"<br><br><div style='border:1px solid lime; padding:10px; box-shadow: 0 0 10px lime;'><b>{description_text}</b></div>"
    combined_text_html = f"<div style='color: lime !important;'>{combined_text}</div>"
    return current_frame, combined_text_html

# -----------------------------
# Custom CSS (High-Tech Neon Theme)
# -----------------------------
custom_css = """
@import url('https://fonts.googleapis.com/css2?family=Orbitron:wght@400;700&display=swap');
body {
    background-color: #0e0e0e;
    font-family: 'Orbitron', sans-serif;
    color: #32CD32;
}
.gradio-container {
    background: linear-gradient(135deg, #1a1a1a, #333333);
    border: 2px solid #32CD32;
    box-shadow: 0 0 15px #32CD32;
    border-radius: 10px;
    padding: 20px;
    max-width: 1200px;
    margin: auto;
}
.gradio-title, .gradio-description, .tab-item, .tab-item * {
    color: #32CD32 !important;
    text-shadow: 0 0 10px #32CD32;
}
input, button, .output {
    border: 1px solid #32CD32;
    box-shadow: 0 0 8px #32CD32;
    color: #32CD32;
}
"""

# -----------------------------
# Create Individual Interfaces for Image Processing
# -----------------------------
posture_interface = gr.Interface(
    fn=analyze_posture_current,
    inputs=gr.Image(label="Upload an Image for Posture Analysis"),
    outputs=[gr.Image(type="numpy", label="Annotated Output"), gr.HTML(label="Posture Analysis")],
    title="Posture",
    description="Detects your posture using MediaPipe with connector lines.",
    live=False
)

emotion_interface = gr.Interface(
    fn=analyze_emotion_current,
    inputs=gr.Image(label="Upload an Image for Emotion Analysis"),
    outputs=[gr.Image(type="numpy", label="Annotated Output"), gr.HTML(label="Emotion Analysis")],
    title="Emotion",
    description="Detects facial emotions using FER.",
    live=False
)

objects_interface = gr.Interface(
    fn=analyze_objects_current,
    inputs=gr.Image(label="Upload an Image for Object Detection"),
    outputs=[gr.Image(type="numpy", label="Annotated Output"), gr.HTML(label="Object Detection")],
    title="Objects",
    description="Detects objects using a pretrained Faster R-CNN.",
    live=False
)

faces_interface = gr.Interface(
    fn=analyze_faces_current,
    inputs=gr.Image(label="Upload an Image for Face Detection"),
    outputs=[gr.Image(type="numpy", label="Annotated Output"), gr.HTML(label="Face Detection")],
    title="Faces",
    description="Detects faces using MediaPipe.",
    live=False
)

facial_recognition_interface = gr.Interface(
    fn=analyze_facial_recognition,
    inputs=gr.Image(label="Upload a Face Image for Facial Recognition"),
    outputs=[gr.Image(type="numpy", label="Cropped Face"), gr.HTML(label="Facial Recognition")],
    title="Facial Recognition",
    description="Extracts and outputs the facial vector using facebook/dino-vitb16.",
    live=False
)

all_interface = gr.Interface(
    fn=analyze_all,
    inputs=gr.Image(label="Upload an Image for All Inferences"),
    outputs=[gr.Image(type="numpy", label="Annotated Output"), gr.HTML(label="Combined Analysis")],
    title="All Inferences",
    description="Runs posture, emotion, object, and face detection all at once.",
    live=False
)

tabbed_interface = gr.TabbedInterface(
    interface_list=[
        posture_interface,
        emotion_interface,
        objects_interface,
        faces_interface,
        facial_recognition_interface,
        all_interface
    ],
    tab_names=[
        "Posture",
        "Emotion",
        "Objects",
        "Faces",
        "Facial Recognition",
        "All Inferences"
    ]
)

# -----------------------------
# Wrap in a Blocks Layout and Launch
# -----------------------------
demo = gr.Blocks(css=custom_css)
with demo:
    gr.Markdown("<h1 class='gradio-title' style='color: #32CD32;'>Multi-Analysis Image App</h1>")
    gr.Markdown("<p class='gradio-description' style='color: #32CD32;'>Upload an image to run high-tech analysis for posture, emotions, objects, faces, and facial embeddings.</p>")
    tabbed_interface.render()

if __name__ == "__main__":
    demo.launch()