506 lines
19 KiB
Python
506 lines
19 KiB
Python
#!/usr/bin/env python 3
|
|
|
|
import os
|
|
import json
|
|
import argparse
|
|
import warnings
|
|
from typing import List, Tuple, Optional
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
from sklearn.model_selection import train_test_split
|
|
from sklearn.decomposition import PCA
|
|
from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering
|
|
from sklearn.metrics import (
|
|
silhouette_score,
|
|
calinski_harabasz_score,
|
|
davies_bouldin_score,
|
|
adjusted_rand_score,
|
|
normalized_mutual_info_score,
|
|
homogeneity_completeness_v_measure,
|
|
)
|
|
from sklearn.preprocessing import StandardScaler
|
|
import joblib
|
|
import matplotlib.pyplot as plt
|
|
import seaborn as sns
|
|
import tensorflow as tf
|
|
from tensorflow.keras.applications import MobileNetV2, EfficientNetB0
|
|
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input as mobilenet_preprocess
|
|
from tensorflow.keras.applications.efficientnet import preprocess_input as efficientnet_preprocess
|
|
from tensorflow.keras.utils import load_img, img_to_array
|
|
|
|
# -----------------------------
|
|
# Utilities
|
|
# -----------------------------
|
|
|
|
def set_seed(seed: int = 42):
|
|
np.random.seed(seed)
|
|
tf.random.set_seed(seed)
|
|
os.environ["PYTHONHASHSEED"] = str(seed)
|
|
|
|
|
|
def ensure_dir(path: str):
|
|
os.makedirs(path, exist_ok=True)
|
|
|
|
|
|
def guess_basename(s: Optional[str]) -> Optional[str]:
|
|
if s is None or (isinstance(s, float) and np.isnan(s)) or str(s).strip() == "":
|
|
return None
|
|
name = os.path.basename(str(s))
|
|
base, _ = os.path.splitext(name)
|
|
return base if base else None
|
|
|
|
|
|
def first_existing_column(df: pd.DataFrame, candidates: List[str]) -> Optional[str]:
|
|
for c in candidates:
|
|
if c in df.columns:
|
|
return c
|
|
return None
|
|
|
|
|
|
def build_filename_from_row(row: pd.Series, img_ext: str = ".jpg") -> Optional[str]:
|
|
"""
|
|
Build the current filename in order of preference:
|
|
- New_Name_With_Date (must end with extension or add one)
|
|
- New_Name
|
|
- Nombre_Nuevo
|
|
- basename_final + ext
|
|
- basename + ext
|
|
"""
|
|
for key in ["New_Name_With_Date", "New_Name", "Nombre_Nuevo"]:
|
|
if key in row and pd.notna(row[key]) and str(row[key]).strip() != "":
|
|
fname = str(row[key]).strip()
|
|
if not os.path.splitext(fname)[1]:
|
|
fname = fname + img_ext
|
|
return fname
|
|
|
|
for key in ["basename_final", "basename"]:
|
|
if key in row and pd.notna(row[key]) and str(row[key]).strip() != "":
|
|
return f"{row[key]}{img_ext}"
|
|
|
|
# As a fallback, try Old_Name
|
|
if "Old_Name" in row and pd.notna(row["Old_Name"]) and str(row["Old_Name"]).strip() != "":
|
|
fname = str(row["Old_Name"]).strip()
|
|
if not os.path.splitext(fname)[1]:
|
|
fname = fname + img_ext
|
|
return fname
|
|
|
|
return None
|
|
|
|
|
|
# -----------------------------
|
|
# Data loading
|
|
# -----------------------------
|
|
|
|
def load_and_merge_csvs(csv_GBIF: str, csv_AV: str) -> pd.DataFrame:
|
|
"""
|
|
Load two CSVs and outer-merge them on basename (robust extraction).
|
|
"""
|
|
def read_csv_any(path: str) -> pd.DataFrame:
|
|
for enc in ("utf-8", "utf-8-sig", "latin-1"):
|
|
try:
|
|
return pd.read_csv(path, encoding=enc)
|
|
except UnicodeDecodeError:
|
|
continue
|
|
return pd.read_csv(path, encoding="utf-8", errors="replace")
|
|
|
|
df_GBIF = read_csv_any(csv_GBIF)
|
|
df_AV = read_csv_any(csv_AV)
|
|
|
|
# Create basename columns
|
|
# For df_GBIF try in this order
|
|
a_fname_col = first_existing_column(df_GBIF, ["New_Name_With_Date", "New_Name", "Nombre_Nuevo", "Old_Name", "Nombre_Anterior", "Filename"])
|
|
if a_fname_col is None:
|
|
# Try any string column
|
|
str_cols = [c for c in df_GBIF.columns if df_GBIF[c].dtype == object]
|
|
a_fname_col = str_cols[0] if str_cols else None
|
|
|
|
df_GBIF = df_GBIF.copy()
|
|
if a_fname_col:
|
|
df_GBIF["basename_a"] = df_GBIF[a_fname_col].apply(guess_basename)
|
|
else:
|
|
df_GBIF["basename_a"] = None
|
|
|
|
# For df_AV try basename columns
|
|
b_base_col = first_existing_column(df_AV, ["basename", "basename_final", "basename_json", "basename_csv"])
|
|
if b_base_col is None:
|
|
# Try to derive from any filename-like column
|
|
b_fname_col = first_existing_column(df_AV, ["New_Name_With_Date", "New_Name", "Nombre_Nuevo", "Old_Name", "Nombre_Anterior", "Filename"])
|
|
if b_fname_col:
|
|
df_AV["basename_b"] = df_AV[b_fname_col].apply(guess_basename)
|
|
else:
|
|
df_AV["basename_b"] = None
|
|
else:
|
|
df_AV["basename_b"] = df_AV[b_base_col].apply(lambda x: str(x).strip() if pd.notna(x) else None)
|
|
|
|
# Outer merge
|
|
merged = pd.merge(df_GBIF, df_AV, left_on="basename_a", right_on="basename_b", how="outer", suffixes=("_a", "_b"))
|
|
|
|
# Create unified basename
|
|
merged["basename"] = merged["basename_a"].fillna(merged["basename_b"])
|
|
|
|
return merged
|
|
|
|
|
|
def attach_filenames_and_paths(df: pd.DataFrame, images_dir: str, img_ext: str = ".jpg") -> pd.DataFrame:
|
|
"""
|
|
Build 'filename' and 'path' columns per row based on best-available fields.
|
|
"""
|
|
rows = []
|
|
for _, row in df.iterrows():
|
|
fname = build_filename_from_row(row, img_ext=img_ext)
|
|
if fname is None:
|
|
rows.append(None)
|
|
continue
|
|
full_path = os.path.join(images_dir, fname)
|
|
rows.append((fname, full_path))
|
|
|
|
df = df.copy()
|
|
df["filename_path_tuple"] = rows
|
|
df["filename"] = df["filename_path_tuple"].apply(lambda t: t[0] if t else None)
|
|
df["path"] = df["filename_path_tuple"].apply(lambda t: t[1] if t else None)
|
|
df.drop(columns=["filename_path_tuple"], inplace=True)
|
|
|
|
# Verify file existence
|
|
df["exists"] = df["path"].apply(lambda p: os.path.exists(p) if isinstance(p, str) else False)
|
|
missing = (~df["exists"]).sum()
|
|
if missing > 0:
|
|
warnings.warn(f"{missing} files listed but not found on disk. They will be ignored.")
|
|
|
|
return df[df["exists"]].reset_index(drop=True)
|
|
|
|
|
|
# -----------------------------
|
|
# Embeddings
|
|
# -----------------------------
|
|
|
|
def make_preprocess(backbone: str):
|
|
if backbone == "mobilenet":
|
|
return mobilenet_preprocess
|
|
elif backbone == "efficientnet":
|
|
return efficientnet_preprocess
|
|
else:
|
|
return mobilenet_preprocess
|
|
|
|
|
|
def make_backbone_model(img_size: int, backbone: str = "mobilenet") -> tf.keras.Model:
|
|
input_shape = (img_size, img_size, 3)
|
|
if backbone == "mobilenet":
|
|
base = MobileNetV2(include_top=False, weights="imagenet", input_shape=input_shape, pooling="avg")
|
|
elif backbone == "efficientnet":
|
|
base = EfficientNetB0(include_top=False, weights="imagenet", input_shape=input_shape, pooling="avg")
|
|
else:
|
|
base = MobileNetV2(include_top=False, weights="imagenet", input_shape=input_shape, pooling="avg")
|
|
base.trainable = False
|
|
return base
|
|
|
|
|
|
def load_image(path: str, img_size: int) -> np.ndarray:
|
|
img = load_img(path, target_size=(img_size, img_size))
|
|
arr = img_to_array(img)
|
|
return arr
|
|
|
|
|
|
def build_dataset(paths: List[str], img_size: int, preprocess_fn, batch_size: int = 32) -> tf.data.Dataset:
|
|
path_ds = tf.data.Dataset.from_tensor_slices(paths)
|
|
|
|
def _load(p):
|
|
img = tf.numpy_function(lambda x: load_image(x.decode(), img_size), [p], tf.float32)
|
|
img.set_shape((img_size, img_size, 3))
|
|
img = preprocess_fn(img)
|
|
return img
|
|
|
|
ds = path_ds.map(lambda p: _load(p), num_parallel_calls=tf.data.AUTOTUNE)
|
|
ds = ds.batch(batch_size).prefetch(tf.data.AUTOTUNE)
|
|
return ds
|
|
|
|
|
|
def compute_embeddings(model: tf.keras.Model, ds: tf.data.Dataset) -> np.ndarray:
|
|
emb = model.predict(ds, verbose=1)
|
|
return emb
|
|
|
|
|
|
# -----------------------------
|
|
# Clustering and evaluation
|
|
# -----------------------------
|
|
|
|
def fit_reduction(train_emb: np.ndarray, n_pca: int = 50):
|
|
scaler = StandardScaler()
|
|
train_scaled = scaler.fit_transform(train_emb)
|
|
pca = PCA(n_components=min(n_pca, train_scaled.shape[1]))
|
|
train_pca = pca.fit_transform(train_scaled)
|
|
return scaler, pca, train_pca
|
|
|
|
|
|
def transform_reduction(emb: np.ndarray, scaler: StandardScaler, pca: PCA) -> np.ndarray:
|
|
return pca.transform(scaler.transform(emb))
|
|
|
|
|
|
def fit_cluster_algo(cluster: str, n_clusters: int, train_feats: np.ndarray):
|
|
if cluster == "kmeans":
|
|
km = KMeans(n_clusters=n_clusters, n_init="auto", random_state=42)
|
|
km.fit(train_feats)
|
|
return km, km.labels_, km.cluster_centers_
|
|
elif cluster == "dbscan":
|
|
db = DBSCAN(eps=0.8, min_samples=5, n_jobs=-1)
|
|
db.fit(train_feats)
|
|
# Compute centroids for assignment on val/test
|
|
centers = []
|
|
labels = db.labels_
|
|
for c in sorted(set(labels)):
|
|
if c == -1:
|
|
continue
|
|
centers.append(train_feats[labels == c].mean(axis=0))
|
|
centers = np.array(centers) if centers else None
|
|
return db, labels, centers
|
|
else: # agglomerative
|
|
ag = AgglomerativeClustering(n_clusters=n_clusters)
|
|
labels = ag.fit_predict(train_feats)
|
|
# Compute centroids
|
|
centers = []
|
|
for c in range(n_clusters):
|
|
centers.append(train_feats[labels == c].mean(axis=0))
|
|
centers = np.array(centers)
|
|
return ag, labels, centers
|
|
|
|
|
|
def assign_to_nearest_centroid(feats: np.ndarray, centers: Optional[np.ndarray]) -> np.ndarray:
|
|
if centers is None or len(centers) == 0:
|
|
return np.full((feats.shape[0],), -1, dtype=int)
|
|
dists = ((feats[:, None, :] - centers[None, :, :]) ** 2).sum(axis=2)
|
|
return np.argmin(dists, axis=1)
|
|
|
|
|
|
def internal_metrics(X: np.ndarray, labels: np.ndarray) -> dict:
|
|
# Ignore noise label -1 for silhouette etc.
|
|
mask = labels != -1
|
|
res = {}
|
|
if mask.sum() > 1 and len(np.unique(labels[mask])) > 1:
|
|
res["silhouette"] = float(silhouette_score(X[mask], labels[mask]))
|
|
res["calinski_harabasz"] = float(calinski_harabasz_score(X[mask], labels[mask]))
|
|
res["davies_bouldin"] = float(davies_bouldin_score(X[mask], labels[mask]))
|
|
else:
|
|
res["silhouette"] = None
|
|
res["calinski_harabasz"] = None
|
|
res["davies_bouldin"] = None
|
|
return res
|
|
|
|
|
|
def external_metrics(y_true: Optional[np.ndarray], y_pred: np.ndarray) -> dict:
|
|
if y_true is None or pd.isna(y_true).all():
|
|
return {}
|
|
# Filter where y_true is valid
|
|
m = pd.notna(y_true).values
|
|
if m.sum() == 0:
|
|
return {}
|
|
yt = y_true[m]
|
|
yp = y_pred[m]
|
|
res = {}
|
|
try:
|
|
res["ARI"] = float(adjusted_rand_score(yt, yp))
|
|
res["NMI"] = float(normalized_mutual_info_score(yt, yp))
|
|
h, c, v = homogeneity_completeness_v_measure(yt, yp)
|
|
res["homogeneity"] = float(h)
|
|
res["completeness"] = float(c)
|
|
res["v_measure"] = float(v)
|
|
except Exception:
|
|
pass
|
|
return res
|
|
|
|
|
|
# -----------------------------
|
|
# Plotting
|
|
# -----------------------------
|
|
|
|
def plot_scatter_2d(X2d: np.ndarray, labels: np.ndarray, title: str, out_path: str):
|
|
plt.figure(figsize=(8, 6))
|
|
palette = sns.color_palette("tab20", n_colors=max(2, len(np.unique(labels))))
|
|
sns.scatterplot(x=X2d[:, 0], y=X2d[:, 1], hue=labels, palette=palette, s=12, linewidth=0, legend=False)
|
|
plt.title(title)
|
|
plt.tight_layout()
|
|
plt.savefig(out_path, dpi=180)
|
|
plt.close()
|
|
|
|
|
|
# -----------------------------
|
|
# Main pipeline
|
|
# -----------------------------
|
|
|
|
def parse_args():
|
|
parser = argparse.ArgumentParser(description="Unsupervised image clustering with pretrained CNN embeddings")
|
|
parser.add_argument("--images_dir", default=r"C:\Users\sof12\Desktop\ML\Datasets\Nocciola_GBIF")
|
|
parser.add_argument("--csv_GBIF", default=r"C:\Users\sof12\Desktop\ML\Datasets\Nocciola_GBIF\change_namesAV.csv")
|
|
parser.add_argument("--csv_AV", default=r"C:\Users\sof12\Desktop\ML\Datasets\Nocciola_GBIF\metadatos_unidos.csv")
|
|
parser.add_argument("--out_dir", default=r"C:\Users\sof12\Desktop\ML\Datasets\Nocciola_GBIF\TrainingV2")
|
|
parser.add_argument("--label_col", default=None, help="Optional label column to evaluate external metrics")
|
|
parser.add_argument("--img_ext", default=".jpg")
|
|
parser.add_argument("--img_size", type=int, default=224)
|
|
parser.add_argument("--batch_size", type=int, default=32)
|
|
parser.add_argument("--seed", type=int, default=42)
|
|
parser.add_argument("--sample", type=int, default=None, help="Optional max number of images to sample")
|
|
parser.add_argument("--backbone", choices=["mobilenet", "efficientnet"], default="mobilenet")
|
|
parser.add_argument("--cluster", choices=["kmeans", "dbscan", "agglomerative"], default="kmeans")
|
|
parser.add_argument("--n_clusters", type=int, default=7)
|
|
return parser.parse_args()
|
|
|
|
|
|
def main():
|
|
args = parse_args()
|
|
set_seed(args.seed)
|
|
ensure_dir(args.out_dir)
|
|
|
|
# 1) Load and merge CSVs
|
|
print("Loading and merging CSVs...")
|
|
merged = load_and_merge_csvs(args.csv_GBIF, args.csv_AV)
|
|
|
|
# 2) Build filenames and paths based on merged info
|
|
print("Resolving filenames and verifying files on disk...")
|
|
merged = attach_filenames_and_paths(merged, args.images_dir, img_ext=args.img_ext)
|
|
|
|
if len(merged) == 0:
|
|
print("No images found. Check images_dir and CSVs.")
|
|
return
|
|
|
|
# Labels (optional)
|
|
y_label = None
|
|
if args.label_col and args.label_col in merged.columns:
|
|
y_label = merged[args.label_col].astype(str)
|
|
print(f"Label column '{args.label_col}' found. Will compute external metrics.")
|
|
else:
|
|
if args.label_col:
|
|
print(f"Label column '{args.label_col}' not found. External metrics will be skipped.")
|
|
|
|
# Optional sampling
|
|
if args.sample is not None and args.sample < len(merged):
|
|
merged = merged.sample(n=args.sample, random_state=args.seed).reset_index(drop=True)
|
|
|
|
# 3) Split train/val/test
|
|
print("Splitting train/val/test...")
|
|
idx = np.arange(len(merged))
|
|
stratify = y_label if y_label is not None and y_label.nunique() > 1 else None
|
|
|
|
idx_train, idx_tmp = train_test_split(idx, test_size=0.30, random_state=args.seed, stratify=stratify)
|
|
y_tmp = y_label.iloc[idx_tmp] if y_label is not None else None
|
|
stratify_tmp = y_tmp if (y_tmp is not None and y_tmp.nunique() > 1) else None
|
|
idx_val, idx_test = train_test_split(idx_tmp, test_size=0.50, random_state=args.seed, stratify=stratify_tmp)
|
|
|
|
df_train = merged.iloc[idx_train].reset_index(drop=True)
|
|
df_val = merged.iloc[idx_val].reset_index(drop=True)
|
|
df_test = merged.iloc[idx_test].reset_index(drop=True)
|
|
|
|
print(f"Train: {len(df_train)} | Val: {len(df_val)} | Test: {len(df_test)}")
|
|
|
|
# 4) Embeddings
|
|
print("Building embedding model...")
|
|
preprocess_fn = make_preprocess(args.backbone)
|
|
model = make_backbone_model(args.img_size, backbone=args.backbone)
|
|
|
|
print("Computing embeddings...")
|
|
ds_train = build_dataset(df_train["path"].tolist(), args.img_size, preprocess_fn, args.batch_size)
|
|
ds_val = build_dataset(df_val["path"].tolist(), args.img_size, preprocess_fn, args.batch_size)
|
|
ds_test = build_dataset(df_test["path"].tolist(), args.img_size, preprocess_fn, args.batch_size)
|
|
|
|
emb_train = compute_embeddings(model, ds_train)
|
|
emb_val = compute_embeddings(model, ds_val)
|
|
emb_test = compute_embeddings(model, ds_test)
|
|
|
|
# 5) Reduction
|
|
print("Fitting PCA reduction (50D for clustering, 2D for plots)...")
|
|
scaler, pca50, train_50 = fit_reduction(emb_train, n_pca=50)
|
|
val_50 = transform_reduction(emb_val, scaler, pca50)
|
|
test_50 = transform_reduction(emb_test, scaler, pca50)
|
|
|
|
pca2 = PCA(n_components=2).fit(scaler.transform(emb_train))
|
|
train_2d = pca2.transform(scaler.transform(emb_train))
|
|
val_2d = pca2.transform(scaler.transform(emb_val))
|
|
test_2d = pca2.transform(scaler.transform(emb_test))
|
|
|
|
# 6) Clustering
|
|
print(f"Clustering with {args.cluster}...")
|
|
cluster_model, y_train_clusters, centers = fit_cluster_algo(args.cluster, args.n_clusters, train_50)
|
|
|
|
if args.cluster == "kmeans":
|
|
y_val_clusters = cluster_model.predict(val_50)
|
|
y_test_clusters = cluster_model.predict(test_50)
|
|
else:
|
|
# Assign by nearest centroid computed on train
|
|
y_val_clusters = assign_to_nearest_centroid(val_50, centers)
|
|
y_test_clusters = assign_to_nearest_centroid(test_50, centers)
|
|
|
|
# 7) Metrics
|
|
print("Computing internal metrics...")
|
|
train_internal = internal_metrics(train_50, y_train_clusters)
|
|
val_internal = internal_metrics(val_50, y_val_clusters)
|
|
test_internal = internal_metrics(test_50, y_test_clusters)
|
|
|
|
if args.label_col and args.label_col in merged.columns:
|
|
print("Computing external metrics vs labels...")
|
|
y_train_true = df_train[args.label_col].astype(str)
|
|
y_val_true = df_val[args.label_col].astype(str)
|
|
y_test_true = df_test[args.label_col].astype(str)
|
|
train_external = external_metrics(y_train_true, y_train_clusters)
|
|
val_external = external_metrics(y_val_true, y_val_clusters)
|
|
test_external = external_metrics(y_test_true, y_test_clusters)
|
|
else:
|
|
train_external = val_external = test_external = {}
|
|
|
|
# 8) Save outputs
|
|
print("Saving outputs...")
|
|
ensure_dir(args.out_dir)
|
|
|
|
# Save assignments
|
|
def save_split_csv(df_split, emb_split, y_clusters, split_name):
|
|
out_csv = os.path.join(args.out_dir, f"{split_name}_assignments.csv")
|
|
out_npy = os.path.join(args.out_dir, f"{split_name}_embeddings.npy")
|
|
df_out = df_split.copy()
|
|
df_out["cluster"] = y_clusters
|
|
df_out.to_csv(out_csv, index=False, encoding="utf-8")
|
|
np.save(out_npy, emb_split)
|
|
|
|
save_split_csv(df_train, emb_train, y_train_clusters, "train")
|
|
save_split_csv(df_val, emb_val, y_val_clusters, "val")
|
|
save_split_csv(df_test, emb_test, y_test_clusters, "test")
|
|
|
|
# Save models
|
|
joblib.dump(scaler, os.path.join(args.out_dir, "scaler.joblib"))
|
|
joblib.dump(pca50, os.path.join(args.out_dir, "pca50.joblib"))
|
|
joblib.dump(pca2, os.path.join(args.out_dir, "pca2.joblib"))
|
|
joblib.dump(cluster_model, os.path.join(args.out_dir, f"{args.cluster}.joblib"))
|
|
|
|
# Plots
|
|
plot_scatter_2d(train_2d, y_train_clusters, f"Train clusters ({args.cluster})", os.path.join(args.out_dir, "train_clusters_2d.png"))
|
|
plot_scatter_2d(val_2d, y_val_clusters, f"Val clusters ({args.cluster})", os.path.join(args.out_dir, "val_clusters_2d.png"))
|
|
plot_scatter_2d(test_2d, y_test_clusters, f"Test clusters ({args.cluster})", os.path.join(args.out_dir, "test_clusters_2d.png"))
|
|
|
|
if args.label_col and args.label_col in merged.columns:
|
|
# Color by labels for comparison
|
|
plot_scatter_2d(train_2d, df_train[args.label_col].astype(str).values, "Train by labels", os.path.join(args.out_dir, "train_labels_2d.png"))
|
|
plot_scatter_2d(val_2d, df_val[args.label_col].astype(str).values, "Val by labels", os.path.join(args.out_dir, "val_labels_2d.png"))
|
|
plot_scatter_2d(test_2d, df_test[args.label_col].astype(str).values, "Test by labels", os.path.join(args.out_dir, "test_labels_2d.png"))
|
|
|
|
# Summary JSON
|
|
summary = {
|
|
"counts": {"train": len(df_train), "val": len(df_val), "test": len(df_test)},
|
|
"cluster": args.cluster,
|
|
"n_clusters": args.n_clusters,
|
|
"backbone": args.backbone,
|
|
"img_size": args.img_size,
|
|
"internal_metrics": {
|
|
"train": train_internal,
|
|
"val": val_internal,
|
|
"test": test_internal,
|
|
},
|
|
"external_metrics": {
|
|
"train": train_external,
|
|
"val": val_external,
|
|
"test": test_external,
|
|
},
|
|
}
|
|
with open(os.path.join(args.out_dir, "summary.json"), "w", encoding="utf-8") as f:
|
|
json.dump(summary, f, indent=2, ensure_ascii=False)
|
|
|
|
print("Done. Results saved to:", args.out_dir)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main() |