import os
import pandas as pd
import re
import cv2
import numpy as np
import matplotlib.pyplot as plt
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def graph_size():
"""
Plot the size of individuals over time.
"""
file_path = "output/data/size.xlsx"
data = pd.read_excel(file_path)
data = data.fillna(0)
individus = data.iloc[:, 0]
intensites = data.iloc[:, 1:]
threshold = 0
for i, individu in enumerate(individus):
y = intensites.iloc[i]
x = intensites.columns
y_masked = np.where(y == threshold, np.nan, y)
plt.plot(x, y_masked, label=individu)
step = 5
plt.xticks(intensites.columns[::step], rotation=45)
plt.xlabel("Temps")
plt.ylabel("taille")
plt.title("Courbes de taille des individus au cours du temps")
plt.savefig("output/plot/courbes_taille_individus.png", format="png")
plt.close()
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def object_size(image_path):
"""
Calculate the size of an object in an image.
Parameters:
image_path (str): Path to the input image.
Returns:
object_area (float): Area of the detected object in pixels.
"""
image = cv2.imread(image_path)
if image is None:
raise ValueError("L'image n'a pas pu être chargée.")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if not contours:
return 0
contour = contours[0]
object_area = cv2.contourArea(contour)
return object_area
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def size(n):
"""
Calculate the size of objects in images and save the results to an Excel file. Uses the object_size function to calculate the size of each object in the images. Uses the graph_size function to plot the size of individuals over time.
Parameters:
n (int): Number of images to process.
See also:
object_size: Function to calculate the size of an object in an image.
graph_size: Function to plot the size of individuals over time.
"""
base_folder = "output/list_track"
output_file = "output/data/size.xlsx"
if not output_file.endswith(".xlsx"):
raise ValueError("Output file must have an .xlsx extension")
folders = [
d
for d in os.listdir(base_folder)
if os.path.isdir(os.path.join(base_folder, d)) and d.startswith("macrophage_")
]
folders.sort(key=lambda x: int(re.findall(r"\d+", x)[0]))
results = []
for folder in folders:
folder_path = os.path.join(base_folder, folder)
folder_result = {"Time": folder}
print(folder_path)
for a in range(0, n):
found = False
for file in os.listdir(folder_path):
if re.match(rf"{a}_\d+\.png", file):
found = True
input = os.path.join(folder_path, file)
x = object_size(input)
folder_result[f"{a}"] = x
break
if not found:
folder_result[f"{a}"] = "NA"
results.append(folder_result)
df = pd.DataFrame(results)
# Replace the 0 values (if merge) by the mean of the 5 previous and the 5 next values of the row they belong to
for i in range(len(df)):
for j in range(1, len(df.columns)):
if df.iloc[i, j] == 0:
# idx of the row
# list of the 5 previous in the same row (i)
prev = df.iloc[i, j - 5 : j].tolist()
# list of the 5 next in the same row (i)
next_ = df.iloc[i, j + 1 : j + 6].tolist()
mean_value = int((sum(prev) + sum(next_)) / (len(prev) + len(next_)))
df.iloc[i, j] = mean_value
df.to_excel(output_file, index=False, engine="openpyxl")
print(f"Report saved to {output_file}")
graph_size()
return output_file