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import torch
from PIL import Image, ImageDraw, ImageFont
import numpy as np
import cv2
import os
from utilities import get_path, show_image, show_image_with_matplotlib
import transformers
class ObjectDetector:
def __init__(self):
self.model = None
self.processor = None
self.model_name = None
def load_model(self, model_name='detic', pretrained=True, model_version='yolov5s'):
"""
Load the specified object detection model.
:param model_name: Name of the model to load.
:param pretrained: Boolean indicating if pretrained model should be used.
:param model_version: Version of the model, applicable for YOLOv5.
"""
self.model_name = model_name
if model_name == 'detic':
self.load_detic_model(pretrained)
elif model_name == 'yolov5':
self.load_yolov5_model(pretrained, model_version)
else:
raise ValueError("Unsupported model name")
def load_detic_model(self, pretrained):
"""Load the Detic model."""
try:
model_path = get_path('deformable-detr-detic', 'Models')
from transformers import AutoImageProcessor, AutoModelForObjectDetection
self.processor = AutoImageProcessor.from_pretrained(model_path)
self.model = AutoModelForObjectDetection.from_pretrained(model_path)
except Exception as e:
print(f"Error loading Detic model: {e}")
def load_yolov5_model(self, pretrained, model_version):
"""Load the YOLOv5 model."""
try:
model_path = get_path('yolov5', 'Models')
if model_path and os.path.exists(model_path):
with os.scandir(model_path) as main_dir:
self.model = torch.hub.load(model_path, model_version, pretrained=pretrained, source="local")
else:
self.model = torch.hub.load('ultralytics/yolov5', model_version, pretrained=pretrained)
except Exception as e:
print(f"Error loading YOLOv5 model: {e}")
def process_image(self, image_path: str) -> Image.Image:
"""
Process the image from the given path.
:param image_path: Path to the image file.
:return: Processed image.
"""
with Image.open(image_path) as image:
return image.convert("RGB")
def detect_objects(self, image: Image.Image, threshold: float = 0.4):
"""
Detect objects in the given image.
:param image: Image in which to detect objects.
:param threshold: Detection threshold.
:return: Tuple of detected objects string and list.
"""
detected_objects_str, detected_objects_list = "", []
if self.model_name == 'detic':
detected_objects_str, detected_objects_list = self.detect_with_detic(image, threshold)
elif self.model_name == 'yolov5':
detected_objects_str, detected_objects_list = self.detect_with_yolov5(image, threshold)
return detected_objects_str.strip(), detected_objects_list
def detect_with_detic(self, image: Image.Image, threshold: float):
"""Detect objects using Detic model."""
inputs = self.processor(images=image, return_tensors="pt")
outputs = self.model(**inputs)
target_sizes = torch.tensor([image.size[::-1]])
results = self.processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=threshold)[
0]
detected_objects_str = ""
detected_objects_list = []
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
if score >= threshold:
label_name = self.model.config.id2label[label.item()]
box_rounded = [round(coord, 2) for coord in box.tolist()]
certainty = round(score.item() * 100, 2)
detected_objects_str += f"{{object: {label_name}, bounding box: {box_rounded}, certainty: {certainty}%}}\n"
detected_objects_list.append((label_name, box_rounded, certainty))
return detected_objects_str, detected_objects_list
def detect_with_yolov5(self, image: Image.Image, threshold: float):
"""Detect objects using YOLOv5 model."""
cv2_img = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
results = self.model(cv2_img)
detected_objects_str = ""
detected_objects_list = []
for *bbox, conf, cls in results.xyxy[0]:
if conf >= threshold:
label_name = results.names[int(cls)]
box_rounded = [round(coord.item(), 2) for coord in bbox] # Convert each tensor to float and round
certainty = round(conf.item() * 100, 2)
detected_objects_str += f"{{object: {label_name}, bounding box: {box_rounded}, certainty: {certainty}%}}\n"
detected_objects_list.append((label_name, box_rounded, certainty))
return detected_objects_str, detected_objects_list
def draw_boxes(self, image: Image.Image, detected_objects: list, show_confidence: bool = True) -> Image.Image:
"""
Draw bounding boxes around detected objects in the image.
:param image: Image on which to draw.
:param detected_objects: List of detected objects.
:param show_confidence: Boolean to show confidence scores.
:return: Image with drawn boxes.
"""
draw = ImageDraw.Draw(image)
try:
font = ImageFont.truetype("arial.ttf", 15)
except IOError:
font = ImageFont.load_default()
colors = ["red", "green", "blue", "yellow", "purple", "orange"]
label_color_map = {}
for label_name, box, score in detected_objects:
if label_name not in label_color_map:
label_color_map[label_name] = colors[len(label_color_map) % len(colors)]
color = label_color_map[label_name]
draw.rectangle(box, outline=color, width=3)
label_text = f"{label_name}"
if show_confidence:
label_text += f" ({round(score, 2)}%)"
draw.text((box[0], box[1]), label_text, fill=color, font=font)
return image
if __name__=="__main__":
detector = ObjectDetector()
image_path = get_path('horse.jpg', 'Sample_Images')
detector.load_model('yolov5') # pass either 'detic' or 'yolov5'
image = detector.process_image(image_path)
detected_objects_string, detected_objects_list = detector.detect_objects(image, threshold=0.2)
image_with_boxes = detector.draw_boxes(image, detected_objects_list, show_confidence=False)
print(detected_objects_string)
show_image(image_with_boxes)
#show_image_with_matplotlib(image_path) |