Aherobo Ovie Victor
89517c541b
✅ Professional API Documentation Added: - Created comprehensive Swagger UI similar to Mini SpecsComply Pro - Added Flask-RESTX integration with detailed API models - Professional styling with emojis and comprehensive descriptions ✅ Dual Documentation System: - Main API (port 5002): Built-in Swagger at /docs/ - Professional Docs (port 5003): Enhanced UI with detailed specifications - Complete API coverage: health, info, detection endpoints ✅ Enhanced API Features: - Detailed request/response models with validation - Comprehensive error handling and status codes - Professional API descriptions and examples - Health monitoring with system metrics - Model performance metrics display ✅ Developer Experience: - Interactive API testing interface - Professional documentation layout - Easy startup with start_docs.py script - Comprehensive endpoint documentation ✅ API Endpoints Documented: - GET /api/v1/health - Health check with metrics - GET /api/v1/info - Comprehensive API information - POST /api/v1/detection/upload - File upload detection - GET /api/v1/detection/hardcoded - Test image detection - POST /api/v1/detection/base64 - Base64 image detection Now provides professional API documentation interface matching enterprise standards
288 lines
10 KiB
Python
288 lines
10 KiB
Python
#!/usr/bin/env python3
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"""
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Inference utilities for memory module detection.
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Contains functions for model loading, inference, and visualization.
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"""
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import cv2
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import numpy as np
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from PIL import Image, ImageDraw, ImageFont
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import os
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from ultralytics import YOLO
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import torch
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class MemoryModuleDetector:
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"""Memory module detector using YOLOv8."""
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def __init__(self, model_path='runs/detect/memory_module_detection/weights/best.pt'):
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"""
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Initialize the detector.
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Args:
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model_path (str): Path to the trained YOLOv8 model
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"""
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self.model_path = model_path
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self.model = None
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self.class_names = ['memory_module']
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self.colors = [(0, 255, 0)] # Green for memory modules
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# Load model if it exists
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if os.path.exists(model_path):
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self.load_model()
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else:
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print(f"Warning: Model not found at {model_path}")
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print("Please train the model first using train.py")
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def load_model(self):
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"""Load the trained YOLOv8 model."""
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try:
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# Fix for PyTorch 2.6+ weights_only issue
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import torch
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# Use weights_only=False for compatibility
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with torch.serialization.safe_globals(['ultralytics.nn.tasks.DetectionModel']):
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self.model = YOLO(self.model_path)
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print(f"Model loaded successfully from {self.model_path}")
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except Exception as e:
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try:
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# Fallback: try loading with weights_only=False
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import torch
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original_load = torch.load
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torch.load = lambda *args, **kwargs: original_load(*args, **kwargs, weights_only=False)
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self.model = YOLO(self.model_path)
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torch.load = original_load
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print(f"Model loaded successfully from {self.model_path} (fallback method)")
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except Exception as e2:
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print(f"Error loading model: {e2}")
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self.model = None
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def detect(self, image_path, conf_threshold=0.5, iou_threshold=0.45):
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"""
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Detect memory modules in an image.
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Args:
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image_path (str): Path to the input image
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conf_threshold (float): Confidence threshold for detections
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iou_threshold (float): IoU threshold for NMS
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Returns:
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tuple: (detections, annotated_image)
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"""
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if self.model is None:
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raise ValueError("Model not loaded. Please check model path.")
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# Run inference
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results = self.model(image_path, conf=conf_threshold, iou=iou_threshold)
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# Extract detections
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detections = []
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if len(results) > 0 and results[0].boxes is not None:
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boxes = results[0].boxes
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for i in range(len(boxes)):
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box = boxes.xyxy[i].cpu().numpy() # x1, y1, x2, y2
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conf = boxes.conf[i].cpu().numpy()
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cls = int(boxes.cls[i].cpu().numpy())
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detection = {
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'bbox': box.tolist(),
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'confidence': float(conf),
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'class': int(cls),
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'class_name': self.class_names[cls] if cls < len(self.class_names) else 'unknown'
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}
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detections.append(detection)
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# Create annotated image
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annotated_image = self.draw_detections(image_path, detections)
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return detections, annotated_image
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def draw_detections(self, image_path, detections):
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"""
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Draw bounding boxes on the image.
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Args:
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image_path (str): Path to the input image
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detections (list): List of detection dictionaries
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Returns:
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PIL.Image: Annotated image
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"""
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# Load image
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image = Image.open(image_path).convert('RGB')
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draw = ImageDraw.Draw(image)
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# Try to load a font
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try:
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font = ImageFont.truetype("arial.ttf", 16)
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except:
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font = ImageFont.load_default()
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# Draw each detection
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for detection in detections:
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bbox = detection['bbox']
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confidence = detection['confidence']
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class_name = detection['class_name']
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# Extract coordinates
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x1, y1, x2, y2 = bbox
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# Draw bounding box
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color = self.colors[0] # Green for memory modules
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draw.rectangle([x1, y1, x2, y2], outline=color, width=3)
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# Draw label
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label = f"{class_name}: {confidence:.2f}"
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# Get text size for background
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bbox_text = draw.textbbox((0, 0), label, font=font)
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text_width = bbox_text[2] - bbox_text[0]
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text_height = bbox_text[3] - bbox_text[1]
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# Draw background for text
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draw.rectangle([x1, y1 - text_height - 4, x1 + text_width + 4, y1],
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fill=color, outline=color)
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# Draw text
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draw.text((x1 + 2, y1 - text_height - 2), label, fill=(255, 255, 255), font=font)
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return image
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def detect_from_array(self, image_array, conf_threshold=0.5, iou_threshold=0.45):
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"""
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Detect memory modules from a numpy array.
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Args:
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image_array (np.ndarray): Input image as numpy array
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conf_threshold (float): Confidence threshold for detections
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iou_threshold (float): IoU threshold for NMS
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Returns:
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tuple: (detections, annotated_image)
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"""
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if self.model is None:
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raise ValueError("Model not loaded. Please check model path.")
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# Convert numpy array to PIL Image if needed
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if isinstance(image_array, np.ndarray):
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if image_array.dtype != np.uint8:
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image_array = (image_array * 255).astype(np.uint8)
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image = Image.fromarray(image_array)
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else:
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image = image_array
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# Run inference
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results = self.model(image, conf=conf_threshold, iou=iou_threshold)
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# Extract detections
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detections = []
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if len(results) > 0 and results[0].boxes is not None:
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boxes = results[0].boxes
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for i in range(len(boxes)):
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box = boxes.xyxy[i].cpu().numpy() # x1, y1, x2, y2
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conf = boxes.conf[i].cpu().numpy()
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cls = int(boxes.cls[i].cpu().numpy())
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detection = {
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'bbox': box.tolist(),
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'confidence': float(conf),
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'class': int(cls),
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'class_name': self.class_names[cls] if cls < len(self.class_names) else 'unknown'
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}
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detections.append(detection)
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# Create annotated image
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annotated_image = self.draw_detections_on_image(image, detections)
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return detections, annotated_image
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def draw_detections_on_image(self, image, detections):
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"""
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Draw bounding boxes on a PIL Image.
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Args:
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image (PIL.Image): Input image
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detections (list): List of detection dictionaries
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Returns:
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PIL.Image: Annotated image
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"""
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# Make a copy to avoid modifying the original
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annotated_image = image.copy()
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draw = ImageDraw.Draw(annotated_image)
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# Try to load a font
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try:
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font = ImageFont.truetype("arial.ttf", 16)
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except:
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font = ImageFont.load_default()
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# Draw each detection
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for detection in detections:
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bbox = detection['bbox']
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confidence = detection['confidence']
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class_name = detection['class_name']
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# Extract coordinates
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x1, y1, x2, y2 = bbox
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# Draw bounding box
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color = self.colors[0] # Green for memory modules
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draw.rectangle([x1, y1, x2, y2], outline=color, width=3)
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# Draw label
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label = f"{class_name}: {confidence:.2f}"
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# Get text size for background
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bbox_text = draw.textbbox((0, 0), label, font=font)
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text_width = bbox_text[2] - bbox_text[0]
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text_height = bbox_text[3] - bbox_text[1]
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# Draw background for text
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draw.rectangle([x1, y1 - text_height - 4, x1 + text_width + 4, y1],
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fill=color, outline=color)
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# Draw text
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draw.text((x1 + 2, y1 - text_height - 2), label, fill=(255, 255, 255), font=font)
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return annotated_image
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def test_inference(image_path, model_path='runs/detect/memory_module_detection/weights/best.pt'):
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"""
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Test inference on a single image.
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Args:
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image_path (str): Path to test image
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model_path (str): Path to trained model
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"""
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detector = MemoryModuleDetector(model_path)
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if detector.model is None:
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print("Cannot run inference without a trained model.")
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return
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print(f"Running inference on: {image_path}")
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detections, annotated_image = detector.detect(image_path)
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print(f"Found {len(detections)} memory modules:")
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for i, detection in enumerate(detections):
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print(f" {i+1}. {detection['class_name']} (confidence: {detection['confidence']:.3f})")
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# Save annotated image
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output_path = f"annotated_{os.path.basename(image_path)}"
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annotated_image.save(output_path)
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print(f"Annotated image saved as: {output_path}")
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return detections, annotated_image
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser(description='Test memory module detection')
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parser.add_argument('--image', type=str, required=True, help='Path to test image')
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parser.add_argument('--model', type=str, default='runs/detect/memory_module_detection/weights/best.pt',
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help='Path to trained model')
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parser.add_argument('--conf', type=float, default=0.5, help='Confidence threshold')
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args = parser.parse_args()
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test_inference(args.image, args.model)
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