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import os
import numpy as np
import pandas as pd
from PIL import Image
def preprocess_image(image):
"""
Preprocesses the input image.
Parameters:
image (numpy.array or PIL.Image): Image to preprocess.
Returns:
numpy.array: Resized and converted RGB version of the input image.
"""
# Convert PIL image to numpy array if required
if isinstance(image, Image.Image):
image = np.array(image)
# Resize and convert the image to RGB
input_image = Image.fromarray(image)
input_image = input_image.resize((640, 640))
input_image = input_image.convert("RGB")
return np.array(input_image)
import pandas as pd
import os
def count_instance(result, filenames, uuid, width_list, orientation_list):
"""
Counts the instances in the result and generates a CSV with the counts.
Parameters:
result (list): List containing results for each instance.
filenames (list): Corresponding filenames for each result.
uuid (str): Unique ID for the output folder name.
width_list (list): List containing width values for each instance.
orientation_list (list): List containing orientation values for each instance.
Returns:
tuple: Path to the generated CSV and dataframe with counts.
"""
# Initializing the dataframe
data = {
'Index': [],
'FileName': [],
'Orientation': [],
'Width': [],
'Instance': []
}
df = pd.DataFrame(data)
# Populate the dataframe with counts, width, and orientation
for i, res in enumerate(result):
instance_count = len(res)
df.loc[i] = [i, os.path.basename(filenames[i]), orientation_list[i], width_list[i], instance_count]
# Save dataframe to a CSV file
path = os.path.join('output', uuid)
os.makedirs(path, exist_ok=True)
csv_filename = os.path.join(path, '_results.csv')
# Reorder columns
df = df[['Index', 'FileName', 'Orientation', 'Width', 'Instance']]
df.to_csv(csv_filename, index=False)
return csv_filename, df
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