detection / utils.py
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Update utils.py
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from PIL import Image
import io
import pandas as pd
import numpy as np
from typing import Optional
from ultralytics import YOLO
from ultralytics.yolo.utils.plotting import Annotator, colors
# Initialize the models
model_sample_model = YOLO("https://github.com/NVT-Freelancer/LICENSES/raw/main/detect/playing-card/best_350.pt")
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_predict_origin(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
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_model,
input_image=input_image,
save=False,
image_size=640,
augment=False,
conf=0.5,
)
return predict
def detect_sample_model_origin(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_origin(
model=model_sample_model,
input_image=input_image,
save=False,
image_size=640,
augment=False,
conf=0.5,
)
return predict