app_simple.py

import os
import cv2
import torch
import numpy as np
import gradio as gr
import base64
from typing import List, Dict, Any
import tempfile
import time
from PIL import Image, ImageDraw
import json
import io

# Import RetinaFace model components
from models.retinaface import RetinaFace
from utils.prior_box import PriorBox
from utils.py_cpu_nms import py_cpu_nms
from utils.box_utils import decode, decode_landm

# Global variables for models
mobilenet_model = None
resnet_model = None
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

def load_models():
    """Load both MobileNet and ResNet RetinaFace models"""
    global mobilenet_model, resnet_model
    
    try:
        # Model configurations
        mobilenet_cfg = {
            'name': 'mobilenet0.25',
            'min_sizes': [[16, 32], [64, 128], [256, 512]],
            'steps': [8, 16, 32],
            'variance': [0.1, 0.2],
            'clip': False,
            'loc_weight': 2.0,
            'gpu_train': True,
            'batch_size': 32,
            'ngpu': 1,
            'epoch': 250,
            'decay1': 190,
            'decay2': 220,
            'image_size': 640,
            'pretrain': False,
            'return_layers': {'stage1': 1, 'stage2': 2, 'stage3': 3},
            'in_channel': 32,
            'out_channel': 64
        }

        resnet_cfg = {
            'name': 'Resnet50',
            'min_sizes': [[16, 32], [64, 128], [256, 512]],
            'steps': [8, 16, 32],
            'variance': [0.1, 0.2],
            'clip': False,
            'loc_weight': 2.0,
            'gpu_train': True,
            'batch_size': 24,
            'ngpu': 4,
            'epoch': 100,
            'decay1': 70,
            'decay2': 90,
            'image_size': 840,
            'pretrain': False,
            'return_layers': {'layer2': 1, 'layer3': 2, 'layer4': 3},
            'in_channel': 256,
            'out_channel': 256
        }
        
        # Load MobileNet model
        mobilenet_model = RetinaFace(cfg=mobilenet_cfg, phase='test')
        mobilenet_model.load_state_dict(torch.load('mobilenet0.25_Final.pth', map_location=device))
        mobilenet_model.eval()
        mobilenet_model = mobilenet_model.to(device)
        
        # Load ResNet model
        resnet_model = RetinaFace(cfg=resnet_cfg, phase='test')
        resnet_model.load_state_dict(torch.load('Resnet50_Final.pth', map_location=device))
        resnet_model.eval()
        resnet_model = resnet_model.to(device)
        
        print("✅ Models loaded successfully!")
        return True
        
    except Exception as e:
        print(f"❌ Error loading models: {e}")
        return False

def detect_faces(image, model_type="mobilenet", confidence_threshold=0.5, nms_threshold=0.4):
    """Core face detection function"""
    try:
        start_time = time.time()
        
        # Choose model
        if model_type == "resnet":
            model = resnet_model
            cfg = {
                'min_sizes': [[16, 32], [64, 128], [256, 512]],
                'steps': [8, 16, 32],
                'variance': [0.1, 0.2],
                'clip': False,
                'image_size': 840
            }
        else:
            model = mobilenet_model
            cfg = {
                'min_sizes': [[16, 32], [64, 128], [256, 512]],
                'steps': [8, 16, 32],
                'variance': [0.1, 0.2],
                'clip': False,
                'image_size': 640
            }
        
        if model is None:
            return None, "Models not loaded"
            
        # Convert PIL to numpy array
        if isinstance(image, Image.Image):
            image = np.array(image)
        
        # Preprocessing
        img = np.float32(image)
        im_height, im_width, _ = img.shape
        scale = torch.Tensor([img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
        img -= (104, 117, 123)
        img = img.transpose(2, 0, 1)
        img = torch.from_numpy(img).unsqueeze(0)
        img = img.to(device)
        scale = scale.to(device)

        # Forward pass
        with torch.no_grad():
            loc, conf, landms = model(img)

        # Generate priors
        priorbox = PriorBox(cfg, image_size=(im_height, im_width))
        priors = priorbox.forward()
        priors = priors.to(device)
        prior_data = priors.data
        boxes = decode(loc.data.squeeze(0), prior_data, cfg['variance'])
        boxes = boxes * scale
        boxes = boxes.cpu().numpy()
        scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
        landms = decode_landm(landms.data.squeeze(0), prior_data, cfg['variance'])
        scale1 = torch.Tensor([img.shape[3], img.shape[2], img.shape[3], img.shape[2],
                               img.shape[3], img.shape[2], img.shape[3], img.shape[2],
                               img.shape[3], img.shape[2]])
        scale1 = scale1.to(device)
        landms = landms * scale1
        landms = landms.cpu().numpy()

        # Ignore low scores
        inds = np.where(scores > confidence_threshold)[0]
        boxes = boxes[inds]
        landms = landms[inds]
        scores = scores[inds]

        # Keep top-K before NMS
        order = scores.argsort()[::-1][:5000]
        boxes = boxes[order]
        landms = landms[order]
        scores = scores[order]

        # Apply NMS
        dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
        keep = py_cpu_nms(dets, nms_threshold)
        dets = dets[keep, :]
        landms = landms[keep]

        # Draw results
        result_image = Image.fromarray(image)
        draw = ImageDraw.Draw(result_image)
        
        faces = []
        for b, landmarks in zip(dets, landms):
            if b[4] < confidence_threshold:
                continue
                
            # Draw bounding box
            draw.rectangle([b[0], b[1], b[2], b[3]], outline="red", width=2)
            
            # Draw confidence score
            draw.text((b[0], b[1] - 15), f'{b[4]:.2f}', fill="red")
            
            # Draw landmarks
            for i in range(0, 10, 2):
                draw.ellipse([landmarks[i]-2, landmarks[i+1]-2, landmarks[i]+2, landmarks[i+1]+2], fill="blue")
            
            faces.append({
                "bbox": {"x1": float(b[0]), "y1": float(b[1]), "x2": float(b[2]), "y2": float(b[3])},
                "confidence": float(b[4]),
                "landmarks": {
                    "left_eye": [float(landmarks[0]), float(landmarks[1])],
                    "right_eye": [float(landmarks[2]), float(landmarks[3])],
                    "nose": [float(landmarks[4]), float(landmarks[5])],
                    "left_mouth": [float(landmarks[6]), float(landmarks[7])],
                    "right_mouth": [float(landmarks[8]), float(landmarks[9])]
                }
            })
        
        processing_time = time.time() - start_time
        
        result_text = f"""
        **Detection Results:**
        - **Faces Detected:** {len(faces)}
        - **Model Used:** {model_type}
        - **Processing Time:** {processing_time:.3f}s
        - **Confidence Threshold:** {confidence_threshold}
        - **NMS Threshold:** {nms_threshold}
        """
        
        return result_image, result_text
        
    except Exception as e:
        return None, f"Error: {str(e)}"

# Load models on startup
print("Loading RetinaFace models...")
model_loaded = load_models()

# Create simple Gradio interface
def create_interface():
    with gr.Blocks(title="RetinaFace Face Detection") as demo:
        gr.Markdown("# 🔥 RetinaFace Face Detection API")
        gr.Markdown("Real-time face detection using RetinaFace with MobileNet and ResNet backbones")
        
        if model_loaded:
            gr.Markdown("✅ **Status**: Models loaded successfully!")
        else:
            gr.Markdown("❌ **Status**: Error loading models")
        
        with gr.Row():
            with gr.Column():
                input_image = gr.Image(type="pil", label="Upload Image")
                model_choice = gr.Dropdown(
                    choices=["mobilenet", "resnet"], 
                    value="mobilenet", 
                    label="Model"
                )
                confidence = gr.Slider(
                    minimum=0.1, maximum=1.0, value=0.5, step=0.1,
                    label="Confidence"
                )
                nms = gr.Slider(
                    minimum=0.1, maximum=1.0, value=0.4, step=0.1,
                    label="NMS Threshold"
                )
                detect_btn = gr.Button("🔍 Detect Faces", variant="primary")
            
            with gr.Column():
                output_image = gr.Image(label="Results")
                output_text = gr.Markdown()
        
        detect_btn.click(
            fn=detect_faces,
            inputs=[input_image, model_choice, confidence, nms],
            outputs=[output_image, output_text]
        )
        
        gr.Markdown("""
        ## API Usage
        Use `/api/predict` endpoint with:
        ```json
        {
            "data": [image, "mobilenet", 0.5, 0.4]
        }
        ```
        """)
    
    return demo

# Create and launch the interface
demo = create_interface()

if __name__ == "__main__":
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=True
    )