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import cv2 | |
import numpy as np | |
from ultralytics import YOLO | |
import os | |
import random | |
# Load YOLOv8m-seg model for crack detection | |
BASE_DIR = os.path.dirname(os.path.abspath(__file__)) | |
MODEL_PATH = os.path.join(BASE_DIR, "../models/yolov8m-seg.pt") | |
model = YOLO(MODEL_PATH) | |
import random | |
def detect_cracks(frame, model): | |
""" | |
Detect cracks in a frame using YOLOv8. | |
Args: | |
frame: Input frame (numpy array) | |
model: YOLO model | |
Returns: | |
list: List of detected cracks with type, label, coordinates, confidence, and severity | |
""" | |
# Run YOLOv8 inference for cracks | |
results = model(frame) | |
detected_items = [] | |
line_counter = 1 # Initialize counter for numbered labels | |
for r in results: | |
for box in r.boxes: | |
conf = float(box.conf[0]) | |
if conf < 0.5: | |
continue | |
cls = int(box.cls[0]) | |
label = model.names[cls] | |
if label != "crack": # Process only cracks | |
continue | |
xyxy = box.xyxy[0].cpu().numpy() | |
x_min, y_min, x_max, y_max = map(int, xyxy) | |
# Simulate severity for cracks | |
severity = random.choice(["low", "medium", "high"]) | |
# Add numbered label | |
detection_label = f"Line {line_counter} - Crack (Conf: {conf:.2f})" | |
item = { | |
"type": label, | |
"label": detection_label, | |
"confidence": conf, | |
"coordinates": [x_min, y_min, x_max, y_max], | |
"severity": severity | |
} | |
detected_items.append(item) | |
line_counter += 1 | |
return detected_items | |
def detect_potholes(frame, model): | |
""" | |
Detect potholes in a frame using YOLOv8. | |
Args: | |
frame: Input frame (numpy array) | |
model: YOLO model | |
Returns: | |
list: List of detected potholes with type, label, coordinates, and confidence | |
""" | |
# Run YOLOv8 inference for potholes | |
results = model(frame) | |
detected_items = [] | |
line_counter = 1 # Initialize counter for numbered labels | |
for r in results: | |
for box in r.boxes: | |
conf = float(box.conf[0]) | |
if conf < 0.5: | |
continue | |
cls = int(box.cls[0]) | |
label = model.names[cls] | |
if label != "pothole": # Process only potholes | |
continue | |
xyxy = box.xyxy[0].cpu().numpy() | |
x_min, y_min, x_max, y_max = map(int, xyxy) | |
# Add numbered label | |
detection_label = f"Line {line_counter} - Pothole (Conf: {conf:.2f})" | |
item = { | |
"type": label, | |
"label": detection_label, | |
"confidence": conf, | |
"coordinates": [x_min, y_min, x_max, y_max] | |
} | |
detected_items.append(item) | |
line_counter += 1 | |
return detected_items | |
def detect_objects(frame, model): | |
""" | |
Detect objects in a frame using YOLOv8. | |
Args: | |
frame: Input frame (numpy array) | |
model: YOLO model | |
Returns: | |
list: List of detected objects with type, label, coordinates, and confidence | |
""" | |
# Run YOLOv8 inference for other objects | |
results = model(frame) | |
detected_items = [] | |
line_counter = 1 # Initialize counter for numbered labels | |
for r in results: | |
for box in r.boxes: | |
conf = float(box.conf[0]) | |
if conf < 0.5: | |
continue | |
cls = int(box.cls[0]) | |
label = model.names[cls] | |
if label != "object": # Process only objects | |
continue | |
xyxy = box.xyxy[0].cpu().numpy() | |
x_min, y_min, x_max, y_max = map(int, xyxy) | |
# Add numbered label | |
detection_label = f"Line {line_counter} - Object (Conf: {conf:.2f})" | |
item = { | |
"type": label, | |
"label": detection_label, | |
"confidence": conf, | |
"coordinates": [x_min, y_min, x_max, y_max] | |
} | |
detected_items.append(item) | |
line_counter += 1 | |
return detected_items | |
def detect_items_in_sequence(frame, model): | |
""" | |
Run crack, pothole, and object detection sequentially. | |
Args: | |
frame: Input frame (numpy array) | |
model: YOLO model | |
Returns: | |
list: List of detected items (crack, pothole, object) | |
""" | |
detected_items = [] | |
# Detect cracks first | |
detected_items.extend(detect_cracks(frame, model)) | |
# Detect potholes second | |
detected_items.extend(detect_potholes(frame, model)) | |
# Detect objects third | |
detected_items.extend(detect_objects(frame, model)) | |
return detected_items | |