File size: 9,419 Bytes
e586088 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 |
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
)
|