Spaces:
Sleeping
Sleeping
File size: 2,437 Bytes
0a83dd2 |
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 |
import logging
import numpy as np
from PIL import Image
import cv2 # Ensure OpenCV is installed
# Set up logging configuration
logging.basicConfig(level=logging.INFO, filename='image_processing.log', filemode='a')
def load_image(image_path):
"""Load an image from the specified path."""
try:
image = Image.open(image_path)
logging.info(f"Image loaded successfully from {image_path}.")
return image
except Exception as e:
logging.error(f"Failed to load image from {image_path}: {e}")
return None
def preprocess_image(image, target_size=(640, 640)):
"""Preprocess the image for YOLO model."""
try:
# Resize and convert to RGB
image = image.resize(target_size)
image = image.convert("RGB")
logging.info(f"Image preprocessed to size {target_size}.")
return np.array(image) / 255.0 # Normalize to [0, 1]
except Exception as e:
logging.error(f"Error in preprocessing image: {e}")
return None
def draw_bounding_boxes(image, detections):
"""Draw bounding boxes on the image based on YOLO detections."""
try:
image = np.array(image)
for detection in detections:
x1, y1, x2, y2 = map(int, detection['coordinates']) # Ensure coordinates are integers
label = detection.get('name', 'Unknown')
color = (255, 0, 0) # Red color for bounding boxes
# Draw rectangle
cv2.rectangle(image, (x1, y1), (x2, y2), color, 2)
# Draw label
cv2.putText(image, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
logging.info("Bounding boxes drawn on the image.")
return Image.fromarray(image)
except Exception as e:
logging.error(f"Error in drawing bounding boxes: {e}")
return image
def format_detection_output(detections):
"""Format the YOLO detection output for reporting."""
formatted_detections = []
for detection in detections:
formatted_detection = {
"name": detection.get("name", "Unknown"),
"class": detection.get("class", "Unknown"),
"confidence": round(detection.get("confidence", 0.0), 2),
"coordinates": detection.get("coordinates", [])
}
formatted_detections.append(formatted_detection)
logging.info("Detection output formatted successfully.")
return formatted_detections
|