app.py

from PIL import Image
import io
import pandas as pd
import numpy as np

import gradio as gr
import cv2
import requests
import os
from ultralytics import YOLO

from ultralytics.utils.plotting import Annotator, colors
from render import custom_render_result

file_urls = [
    'https://www.dropbox.com/s/b5g97xo901zb3ds/pothole_example.jpg?dl=1',
    'https://www.dropbox.com/s/86uxlxxlm1iaexa/pothole_screenshot.png?dl=1',
    'https://www.dropbox.com/s/7sjfwncffg8xej2/video_7.mp4?dl=1'
]

def download_file(url, save_name):
    url = url
    if not os.path.exists(save_name):
        file = requests.get(url)
        open(save_name, 'wb').write(file.content)

for i, url in enumerate(file_urls):
    if 'mp4' in file_urls[i]:
        download_file(
            file_urls[i],
            f"video.mp4"
        )
    else:
        download_file(
            file_urls[i],
            f"image_{i}.jpg"
        )

def get_image_from_bytes(binary_image: bytes) -> Image:
    """Convert image from bytes to PIL RGB format

    **Args:**
        - **binary_image (bytes):** The binary representation of the image

    **Returns:**
        - **PIL.Image:** The image in PIL RGB format
    """
    input_image = Image.open(io.BytesIO(binary_image)).convert("RGB")
    return input_image


def get_bytes_from_image(image: Image) -> bytes:
    """
    Convert PIL image to Bytes

    Args:
    image (Image): A PIL image instance

    Returns:
    bytes : BytesIO object that contains the image in JPEG format with quality 85
    """
    return_image = io.BytesIO()
    image.save(return_image, format='JPEG', quality=85)  # save the image in JPEG format with quality 85
    return_image.seek(0)  # set the pointer to the beginning of the file
    return return_image


def transform_predict_to_df(results: list, labeles_dict: dict) -> pd.DataFrame:
    """
    Transform predict from yolov8 (torch.Tensor) to pandas DataFrame.

    Args:
        results (list): A list containing the predict output from yolov8 in the form of a torch.Tensor.
        labeles_dict (dict): A dictionary containing the labels names, where the keys are the class ids and the values are the label names.

    Returns:
        predict_bbox (pd.DataFrame): A DataFrame containing the bounding box coordinates, confidence scores and class labels.
    """
    # Transform the Tensor to numpy array
    predict_bbox = pd.DataFrame(results[0].to("cpu").numpy().boxes.xyxy, columns=['xmin', 'ymin', 'xmax', 'ymax'])
    # Add the confidence of the prediction to the DataFrame
    predict_bbox['confidence'] = results[0].to("cpu").numpy().boxes.conf
    # Add the class of the prediction to the DataFrame
    predict_bbox['class'] = (results[0].to("cpu").numpy().boxes.cls).astype(int)
    # Replace the class number with the class name from the labeles_dict
    predict_bbox['name'] = predict_bbox["class"].replace(labeles_dict)
    return predict_bbox


def get_model_predict(model: YOLO, input_image: Image, save: bool = False, image_size: int = 1248, conf: float = 0.5,
                      augment: bool = False) -> pd.DataFrame:
    """
    Get the predictions of a model on an input image.

    Args:
        model (YOLO): The trained YOLO model.
        input_image (Image): The image on which the model will make predictions.
        save (bool, optional): Whether to save the image with the predictions. Defaults to False.
        image_size (int, optional): The size of the image the model will receive. Defaults to 1248.
        conf (float, optional): The confidence threshold for the predictions. Defaults to 0.5.
        augment (bool, optional): Whether to apply data augmentation on the input image. Defaults to False.

    Returns:
        pd.DataFrame: A DataFrame containing the predictions.
    """
    # Make predictions
    predictions = model.predict(
        imgsz=image_size,
        source=input_image,
        conf=conf,
        save=save,
        augment=augment,
        flipud=0.0,
        fliplr=0.0,
        mosaic=0.0,
    )

    # Transform predictions to pandas dataframe
    predictions = transform_predict_to_df(predictions, model.model.names)
    return predictions


def get_model_segment(model: YOLO, input_image: Image, save: bool = False, image_size: int = 1248, conf: float = 0.25,
                      augment: bool = False) -> pd.DataFrame:
    """
    Get the predictions of a model on an input image.

    Args:
        model (YOLO): The trained YOLO model.
        input_image (Image): The image on which the model will make predictions.
        save (bool, optional): Whether to save the image with the predictions. Defaults to False.
        image_size (int, optional): The size of the image the model will receive. Defaults to 1248.
        conf (float, optional): The confidence threshold for the predictions. Defaults to 0.25.
        augment (bool, optional): Whether to apply data augmentation on the input image. Defaults to False.

    Returns:
        pd.DataFrame: A DataFrame containing the predictions.
    """
    # Make predictions
    predictions = model.predict(
        imgsz=image_size,
        source=input_image,
        conf=conf,
        save=save,
        augment=augment,
        flipud=0.0,
        fliplr=0.0,
        mosaic=0.0,
    )

    # Transform predictions to pandas dataframe
    predictions = transform_predict_to_df(predictions, model.model.names)
    return predictions


################################# BBOX Func #####################################

def add_bboxs_on_img(image: Image, predict: pd.DataFrame()) -> Image:
    """
    add a bounding box on the image

    Args:
    image (Image): input image
    predict (pd.DataFrame): predict from model

    Returns:
    Image: image whis bboxs
    """
    # Create an annotator object
    annotator = Annotator(np.array(image))

    # sort predict by xmin value
    predict = predict.sort_values(by=['xmin'], ascending=True)

    # iterate over the rows of predict dataframe
    for i, row in predict.iterrows():
        # create the text to be displayed on image
        text = f"{row['name']}: {int(row['confidence'] * 100)}%"
        # get the bounding box coordinates
        bbox = [row['xmin'], row['ymin'], row['xmax'], row['ymax']]
        # add the bounding box and text on the image
        annotator.box_label(bbox, text, color=colors(row['class'], True))
    # convert the annotated image to PIL image
    return Image.fromarray(annotator.result())


################################# Models #####################################


def detect_sample_model(input_image: Image) -> pd.DataFrame:
    """
    Predict from sample_model.
    Base on YoloV8

    Args:
        input_image (Image): The input image.

    Returns:
        pd.DataFrame: DataFrame containing the object location.
    """
    predict = get_model_predict(
        model=model_sample_detect,
        input_image=input_image,
        save=False,
        image_size=640,
        augment=False,
        conf=0.2,
    )
    return predict
    
def yoloV8_func(image: gr.Image = None,
                image_size: int = 640,
                conf_threshold: float = 0.4,
                iou_threshold: float = 0.5,
                model_name: str = 'YOLOv8-medium'):
    """This function performs YOLOv8 object detection on the given image.

    Args:
        image (gr.Image, optional): Input image to detect objects on. Defaults to None.
        image_size (int, optional): Desired image size for the model. Defaults to 640.
        conf_threshold (float, optional): Confidence threshold for object detection. Defaults to 0.4.
        iou_threshold (float, optional): Intersection over Union threshold for object detection. Defaults to 0.50.
    """
    # Load the YOLOv8 model from the 'best.pt' checkpoint
    # model_path = "best.pt"
    # model = torch.hub.load('ultralytics/yolov8', 'custom', path='/content/best.pt', force_reload=True, trust_repo=True)

    # Perform object detection on the input image using the YOLOv8 model
    results = model.predict(image,
                            conf=conf_threshold,
                            iou=iou_threshold,
                            imgsz=image_size)

    # Print the detected objects' information (class, coordinates, and probability)
    box = results[0].boxes
    #print("Object type:", box.cls)
    #print("Coordinates:", box.xyxy)
    #print("Probability:", box.conf)

    # Render the output image with bounding boxes around detected objects
    render = custom_render_result(model=model, image=image, result=results[0])
    return render

model = YOLO('best.pt')
path  = [['image_tyre.png'], ['image_ladder.png']]
video_path = [['video.mp4']]

outputs_image = gr.components.Image(label="Output Image")

inputs_image= [
    gr.components.Image(label="Input Image"),
    gr.Slider(minimum=320, maximum=1280, step=32, label="Image Size", value=640),
    gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label="Confidence Threshold",value=0.4, info="Usual value is 0.5"),
    gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label="IOU Threshold",value=0.5, info="Usual value greater than 0.2"),
    gr.components.Dropdown(["YOLOv8-nano", "YOLOv8-small", "YOLOv8-medium", "YOLOv8-large", "YOLOv8-xlarge"], value="YOLOv8-medium", label="YOLOv8 Model")
]

interface_image = gr.Interface(
    fn=yoloV8_func,
    inputs=inputs_image,
    outputs=[outputs_image],
    title="NonConforming Detector",
    examples=path,
    cache_examples=False,
)
def show_preds_video(video_path):
    cap = cv2.VideoCapture(video_path)
    
    conf_threshold = 0.4
    iou_threshold = 0.5
    image_size = 640
    
    while(cap.isOpened()):
        ret, frame = cap.read()
        if ret:
            frame_copy = frame.copy()
            
            results = model.predict(frame,
                            conf=conf_threshold,
                            iou=iou_threshold,
                            imgsz=image_size)

            # Print the detected objects' information (class, coordinates, and probability)
            box = results[0].boxes
            #print("Object type:", box.cls)
            #print("Coordinates:", box.xyxy)
            #print("Probability:", box.conf)

            # Render the output image with bounding boxes around detected objects
            render = custom_render_result(model=model, image=frame, result=results[0])
            yield render
            """
            outputs = model.predict(source=frame)
            results = outputs[0].cpu().numpy()
            for i, det in enumerate(results.boxes.xyxy):
                cv2.rectangle(
                    frame_copy,
                    (int(det[0]), int(det[1])),
                    (int(det[2]), int(det[3])),
                    color=(0, 0, 255),
                    thickness=2,
                    lineType=cv2.LINE_AA
                )
            yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
            """

inputs_video = [
    gr.components.Video(label="Input Video"),

]
outputs_video = [
    gr.components.Image(label="Output Image"),
]
interface_video = gr.Interface(
    fn=show_preds_video,
    inputs=inputs_video,
    outputs=outputs_video,
    title="NonConforming Video Detector",
    examples=video_path,
    cache_examples=False,
)

gr.TabbedInterface(
    [interface_image, interface_video],
    tab_names=['Image inference', 'Video inference']
).queue().launch()