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src/exception.py

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+"""
+This module defines a custom exception handling class and a function to get error message with details of the error.
+"""
+
+# Standard Library
+import sys
+
+# Local imports
+from src.logger import logging
+
+
+# Function Definition to get error message with details of the error (file name and line number) when an error occurs in the program
+def get_error_message(error, error_detail: sys):
+    """
+    Get error message with details of the error.
+
+    Args:
+        - error (Exception): The error that occurred.
+        - error_detail (sys): The details of the error.
+
+    Returns:
+        str: A string containing the error message along with the file name and line number where the error occurred.
+    """
+    _, _, exc_tb = error_detail.exc_info()
+
+    # Get error details
+    file_name = exc_tb.tb_frame.f_code.co_filename
+    return "Error occured in python script name [{0}] line number [{1}] error message[{2}]".format(
+        file_name, exc_tb.tb_lineno, str(error)
+    )
+
+
+# Custom Exception Handling Class Definition
+class CustomExceptionHandling(Exception):
+    """
+    Custom Exception Handling:
+        This class defines a custom exception that can be raised when an error occurs in the program.
+        It takes an error message and an error detail as input and returns a formatted error message when the exception is raised.
+    """
+
+    # Constructor
+    def __init__(self, error_message, error_detail: sys):
+        """Initialize the exception"""
+        super().__init__(error_message)
+
+        self.error_message = get_error_message(error_message, error_detail=error_detail)
+
+    def __str__(self):
+        """String representation of the exception"""
+        return self.error_message

src/logger.py

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+# Importing the required modules
+import os
+import logging
+from datetime import datetime
+
+# Creating a log file with the current date and time as the name of the file
+LOG_FILE = f"{datetime.now().strftime('%m_%d_%Y_%H_%M_%S')}.log"
+
+# Creating a logs folder if it does not exist
+logs_path = os.path.join(os.getcwd(), "logs", LOG_FILE)
+os.makedirs(logs_path, exist_ok=True)
+
+# Setting the log file path and the log level
+LOG_FILE_PATH = os.path.join(logs_path, LOG_FILE)
+
+# Configuring the logger
+logging.basicConfig(
+    filename=LOG_FILE_PATH,
+    format="[ %(asctime)s ] %(lineno)d %(name)s - %(levelname)s - %(message)s",
+    level=logging.INFO,
+)

src/yolo/predict_pose.py

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+import sys
+import PIL.Image as Image
+from ultralytics import YOLO
+import gradio as gr
+import spaces
+
+# Local imports
+from src.logger import logging
+from src.exception import CustomExceptionHandling
+
+
+@spaces.GPU
+def predict_pose(
+    img: str,
+    conf_threshold: float,
+    iou_threshold: float,
+    max_detections: int,
+    model_name: str,
+) -> Image.Image:
+    """
+    Predicts objects in an image using a YOLOv8 model with adjustable confidence and IOU thresholds.
+
+    Args:
+        - img (str or numpy.ndarray): The input image or path to the image file.
+        - conf_threshold (float): The confidence threshold for object detection.
+        - iou_threshold (float): The Intersection Over Union (IOU) threshold for non-max suppression.
+        - max_detections (int): The maximum number of detections allowed.
+        - model_name (str): The name or path of the YOLOv8 model to be used for prediction.
+
+    Returns:
+        PIL.Image.Image: The image with predicted objects plotted on it.
+    """
+    try:
+        # Check if image is None
+        if img is None:
+            gr.Warning("Please provide an image.")
+
+        # Load the YOLO model
+        model = YOLO(model_name)
+
+        # Predict objects in the image
+        results = model.predict(
+            source=img,
+            conf=conf_threshold,
+            iou=iou_threshold,
+            max_det=max_detections,
+            show_labels=True,
+            show_conf=True,
+            imgsz=640,
+            half=True,
+            device="cuda:0",
+        )
+
+        # Plot the predicted objects on the image
+        for r in results:
+            im_array = r.plot()
+            im = Image.fromarray(im_array[..., ::-1])
+
+        # Log the successful prediction
+        logging.info("Pose estimated successfully.")
+
+        # Return the image
+        return im
+
+    # Handle exceptions that may occur during the process
+    except Exception as e:
+        # Custom exception handling
+        raise CustomExceptionHandling(e, sys) from e