def get_area_size_counts_multiple_thresholds(resmaps_val_uint8,
th_min=128,
th_max=255,
binning=True,
nbins=200):
if binning == True:
# compute max_area
max_area = get_max_area(resmaps_val_uint8, th_min)
# initialize variables and parameters
thresholds = np.arange(th_min, th_max + 1)
range_area = (0.5, float(max_area) + 0.5)
counts = np.zeros(shape=(len(thresholds), nbins))
# loop over all thresholds and calculate area counts (with binning)
for i, threshold in enumerate(thresholds):
resmaps_val_uint8_th = threshold_images(resmaps_val_uint8,
threshold)
# resmaps_val_uint8_th_fil = filter_median_images(resmaps_val_uint8_th)
resmaps_val_labeled, areas_all_val = label_images(
resmaps_val_uint8_th)
areas_all_val_1d = [
item for sublist in areas_all_val for item in sublist
]
count, edges = np.histogram(areas_all_val_1d,
bins=nbins,
density=False,
range=range_area)
counts[i] = count
return counts, edges
else:
max_area = get_max_area(resmaps_val_uint8, th_min)
# initialize variables and parameters
thresholds = np.arange(th_min, th_max + 1)
areas = np.arange(1, max_area + 1)
counts = np.zeros(shape=(len(thresholds), max_area))
# loop over all thresholds and calculate area counts
for i, threshold in enumerate(thresholds):
resmaps_val_uint8_th = threshold_images(resmaps_val_uint8,
threshold)
resmaps_val_uint8_th_fil = filter_median_images(
resmaps_val_uint8_th)
resmaps_val_labeled, areas_all_val = label_images(
resmaps_val_uint8_th_fil)
areas_all_val_1d = [
item for sublist in areas_all_val for item in sublist
]
counts[i] = np.array(
[areas_all_val_1d.count(area) for area in areas])
return counts, areas
def get_max_area(resmaps, threshold):
resmaps_th = threshold_images(resmaps, threshold)
# resmaps_fil = filter_median_images(resmaps_th)
resmaps_labeled, areas_all = label_images(resmaps_th)
areas_all_1d = [item for sublist in areas_all for item in sublist]
max_area = np.amax(np.array(areas_all_1d))
return max_area
def plot_area_distro_for_multiple_thresholds(resmaps,
thresholds_to_plot,
th_min,
nbins=200,
method="line",
title="provide a title"):
# fix range so that counts have the save x value
max_area = get_max_area(resmaps, th_min)
range_area = (0.5, float(max_area) + 0.5)
# compute residual maps for multiple thresholds
fig = plt.figure(figsize=(12, 5))
for threshold in thresholds_to_plot:
resmaps_th = threshold_images(resmaps, threshold)
# resmaps_fil = filter_median_images(resmaps_th, kernel_size=3)
resmaps_labeled, areas_all_val = label_images(resmaps_th)
areas_all_1d = [item for sublist in areas_all_val for item in sublist]
if method == "hist":
count, bins, ignored = plt.hist(
areas_all_1d,
bins=nbins,
density=False,
range=range_area, # previously commented
histtype="barstacked",
label="threshold = {}".format(threshold),
)
elif method == "line":
count, bins = np.histogram(
areas_all_1d,
bins=nbins,
density=False,
range=range_area # previously commented
)
bins_middle = bins[:-1] + ((bins[1] - bins[0]) / 2)
plt.plot(
bins_middle,
count,
linestyle="-",
linewidth=0.5, # 0.5
marker="x", # "o"
markersize=0.5, # 0.5
label="threshold = {}".format(threshold),
)
# plt.fill_between(bins_middle, count)
plt.title(title)
plt.legend()
plt.xlabel("area size in pixel")
plt.ylabel("count")
plt.grid()
plt.show()
def main(args):
# Get validation arguments
model_path = args.path
save = args.save
if args.area is None:
pass
else:
area_list = sorted(list(set(args.area)))
if len(area_list) > 2:
raise ValueError(
"Exactly two area values must be passed: area_min and area_max"
)
else:
min_areas = list(np.arange(area_list[0], area_list[1] + 1))
# ========================= SETUP ==============================
# load model, setup and history
model, setup, history = utils.load_model_HDF5(model_path)
# data setup
directory = setup["data_setup"]["directory"]
val_data_dir = os.path.join(directory, "train")
nb_training_images = setup["data_setup"]["nb_training_images"]
nb_validation_images = setup["data_setup"]["nb_validation_images"]
# preprocessing_setup
rescale = setup["preprocessing_setup"]["rescale"]
shape = setup["preprocessing_setup"]["shape"]
preprocessing = setup["preprocessing_setup"]["preprocessing"]
# train_setup
color_mode = setup["train_setup"]["color_mode"]
learning_rate = setup["train_setup"]["learning_rate"]
decay = setup["train_setup"]["decay"]
epochs_trained = setup["train_setup"]["epochs_trained"]
nb_training_images_aug = setup["train_setup"]["nb_training_images_aug"]
epochs = setup["train_setup"]["epochs"]
batch_size = setup["train_setup"]["batch_size"]
channels = setup["train_setup"]["channels"]
validation_split = setup["train_setup"]["validation_split"]
architecture = setup["train_setup"]["architecture"]
loss = setup["train_setup"]["loss"]
tag = setup["tag"]
# create a results directory if not existent
model_dir_name = os.path.basename(str(Path(model_path).parent))
save_dir = os.path.join(
os.getcwd(),
"results",
directory,
architecture,
loss,
model_dir_name,
"validation",
# "a_" + str(min_area),
)
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
# Plot and save loss and val_loss
# plot = pd.DataFrame(history[["loss", "val_loss"]]).plot(figsize=(8, 5))
# fig = plot.get_figure()
# fig.savefig(os.path.join(save_dir, "train_val_losses.png"))
# ============================= PREPROCESSING ===============================
# This will do preprocessing
if architecture in ["mvtec", "mvtec2"]:
preprocessing_function = None
elif architecture == "resnet":
preprocessing_function = keras.applications.inception_resnet_v2.preprocess_input
elif architecture == "nasnet":
preprocessing_function = keras.applications.nasnet.preprocess_input
# same preprocessing as in training
validation_datagen = ImageDataGenerator(
rescale=rescale,
data_format="channels_last",
validation_split=validation_split,
preprocessing_function=preprocessing_function,
)
# retrieve preprocessed validation images as a numpy array
validation_generator = validation_datagen.flow_from_directory(
directory=val_data_dir,
target_size=shape,
color_mode=color_mode,
batch_size=nb_validation_images,
shuffle=False,
class_mode="input",
subset="validation",
)
imgs_val_input = validation_generator.next()[0]
# retrieve validation image_names
filenames = validation_generator.filenames
# get reconstructed images (i.e predictions) on validation dataset
imgs_val_pred = model.predict(imgs_val_input)
# compute residual maps on validation dataset
resmaps_val = calculate_resmaps(imgs_val_input, imgs_val_pred, loss="SSIM")
if color_mode == "rgb":
resmaps_val = tf.image.rgb_to_grayscale(resmaps_val)
if save:
utils.save_np(imgs_val_input, save_dir, "imgs_val_input.npy")
utils.save_np(imgs_val_pred, save_dir, "imgs_val_pred.npy")
utils.save_np(resmaps_val, save_dir, "resmaps_val.npy")
if args.area is None:
print("[INFO] exiting")
exit()
# scale pixel values linearly to [0,1]
# resmaps_val = scale_pixel_values(architecture, resmaps_val)
# Convert to 8-bit unsigned int
resmaps_val = img_as_ubyte(resmaps_val)
# blur resmaps
# resmaps_val = filter_gauss_images(resmaps_val)
# ========================= VALIDATION ALGORITHM ==============================
# initialize validation dictionary
dict_val = {"min_area": [], "threshold": []}
# set threshold boundaries
threshold_min = np.amin(resmaps_val)
threshold_max = np.amax(resmaps_val)
# initialize progress bar
l = len(min_areas)
printProgressBar(0, l, prefix="Progress:", suffix="Complete", length=50)
# loop over all min_areas and compute corresponding thresholds
for i, min_area in enumerate(min_areas):
for threshold in range(threshold_min, threshold_max + 1):
# threshold residual maps
resmaps_th = threshold_images(resmaps_val, threshold)
# filter images to remove salt noise
# resmaps_val = filter_median_images(resmaps_val, kernel_size=3)
# compute connected components
resmaps_labeled, areas_all = label_images(resmaps_th)
# check if area of largest anomalous region is below the minimum area
areas_all_flat = [
item for sublist in areas_all for item in sublist
]
areas_all_flat.sort(reverse=True)
if min_area > areas_all_flat[0]:
break
# print progress bar
time.sleep(0.1)
printProgressBar(i + 1,
l,
prefix="Progress:",
suffix="Complete",
length=50)
# append min_area and corresponding threshold to validation dictionary
dict_val["min_area"].append(min_area)
dict_val["threshold"].append(threshold)
# print validation DataFrame to console
df_val = pd.DataFrame.from_dict(dict_val)
with pd.option_context("display.max_rows", None, "display.max_columns",
None):
print(df_val)
# save validation DataFrame as .txt and .pkl files
with open(os.path.join(save_dir, "validation_results.txt"), "w+") as f:
# f.truncate(0)
f.write(df_val.to_string(header=True, index=True))
print("validation_results.txt saved at {}".format(save_dir))
df_val.to_pickle(os.path.join(save_dir, "validation_results.pkl"))
print("validation_results.pkl saved at {}".format(save_dir))
def main(args):
# parse arguments
model_path = args.path
save = args.save
threshold = args.threshold
min_area = args.area
if threshold is None and min_area is None:
adopt_validation = True
print("[INFO] Testing with validation areas and thresholds.")
elif threshold is not None and min_area is not None:
adopt_validation = False
print("[INFO] Testing with user passed areas and thresholds")
else:
raise Exception("Threshold and area must both be passed.")
# ========================= SETUP ==============================
# load model, setup and history
model, setup, history = utils.load_model_HDF5(model_path)
# data setup
directory = setup["data_setup"]["directory"]
test_data_dir = os.path.join(directory, "test")
nb_training_images = setup["data_setup"]["nb_training_images"]
nb_validation_images = setup["data_setup"]["nb_validation_images"]
# preprocessing_setup
rescale = setup["preprocessing_setup"]["rescale"]
shape = setup["preprocessing_setup"]["shape"]
preprocessing = setup["preprocessing_setup"]["preprocessing"]
# train_setup
color_mode = setup["train_setup"]["color_mode"]
learning_rate = setup["train_setup"]["learning_rate"]
decay = setup["train_setup"]["decay"]
epochs_trained = setup["train_setup"]["epochs_trained"]
nb_training_images_aug = setup["train_setup"]["nb_training_images_aug"]
epochs = setup["train_setup"]["epochs"]
batch_size = setup["train_setup"]["batch_size"]
channels = setup["train_setup"]["channels"]
validation_split = setup["train_setup"]["validation_split"]
architecture = setup["train_setup"]["architecture"]
loss = setup["train_setup"]["loss"]
tag = setup["tag"]
# create directory to save test results
model_dir_name = os.path.basename(str(Path(model_path).parent))
save_dir = os.path.join(
os.getcwd(),
"results",
directory,
architecture,
loss,
model_dir_name,
"test",
# "th_" + str(threshold) + "_a_" + str(min_area),
)
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
if adopt_validation:
# load areas and corresponding thresholds from validation text file
parent_dir = str(Path(save_dir).parent)
val_dir = os.path.join(parent_dir, "validation")
df_val = pd.read_pickle(os.path.join(val_dir,
"validation_results.pkl"))
dict_val = df_val.to_dict(orient="list")
# create a list containing (min_area, threshold) pairs
elems = list(zip(dict_val["min_area"], dict_val["threshold"]))
else:
# compute combinations of given areas and thresholds
areas = sorted(list(set(args.area)))
thresholds = sorted(list(set(args.threshold)))
# create a list containing (min_area, threshold) pairs
elems = [(area, threshold) for threshold in thresholds
for area in areas]
with open(os.path.join(save_dir, "classification.txt"), "w+") as f:
f.write("Classification of image files.\n\n")
# ============================= PREPROCESSING ===============================
# This will do preprocessing
if architecture in ["mvtec", "mvtec2"]:
preprocessing_function = None
elif architecture == "resnet":
preprocessing_function = keras.applications.inception_resnet_v2.preprocess_input
elif architecture == "nasnet":
preprocessing_function = keras.applications.nasnet.preprocess_input
test_datagen = ImageDataGenerator(
rescale=rescale,
data_format="channels_last",
preprocessing_function=preprocessing_function,
)
total_number = utils.get_total_number_test_images(test_data_dir)
# retrieve preprocessed test images as a numpy array
test_generator = test_datagen.flow_from_directory(
directory=test_data_dir,
target_size=shape,
color_mode=color_mode,
batch_size=total_number,
shuffle=False,
class_mode="input",
)
imgs_test_input = test_generator.next()[0]
# retrieve test image names
filenames = test_generator.filenames
# predict on test images
imgs_test_pred = model.predict(imgs_test_input)
# calculate residual maps on test set
resmaps_test = calculate_resmaps(imgs_test_input,
imgs_test_pred,
loss="SSIM")
if color_mode == "rgb":
resmaps_test = tf.image.rgb_to_grayscale(resmaps_test)
# format float64, values between [-1,1]
if save:
utils.save_np(imgs_test_input, save_dir, "imgs_test_input.npy")
utils.save_np(imgs_test_pred, save_dir, "imgs_test_pred.npy")
utils.save_np(resmaps_test, save_dir, "resmaps_test.npy")
# scale pixel values linearly to [0,1]
# resmaps_test = scale_pixel_values(architecture, resmaps_test)
# Convert to 8-bit unsigned int
resmaps_test = img_as_ubyte(resmaps_test)
# blur resmaps
# resmaps_val = filter_gauss_images(resmaps_val)
# =================== Classification Algorithm ==========================
# initialize dictionary to store test results
dict_test = {"min_area": [], "threshold": [], "TPR": [], "TNR": []}
# initialize progress bar
l = len(elems)
printProgressBar(0, l, prefix="Progress:", suffix="Complete", length=50)
# classify test images for all (min_area, threshold) pairs
for i, elem in enumerate(elems):
# get (min_area, threshold) pair
min_area, threshold = elem[0], elem[1]
# threshold residual maps with the given threshold
resmaps_th = threshold_images(resmaps_test, threshold)
# filter images to remove salt noise
# resmaps_test = filter_median_images(resmaps_test, kernel_size=3)
# compute connected components
resmaps_labeled, areas_all = label_images(resmaps_th)
# classify images
y_pred = classify(areas_all, min_area)
# retrieve ground truth
y_true = [
1 if "good" not in filename.split("/") else 0
for filename in filenames
]
# save classification of image files in a .txt file
classification = {
"filenames": filenames,
"predictions": y_pred,
"truth": y_true,
}
df_clf = pd.DataFrame.from_dict(classification)
with open(os.path.join(save_dir, "classification.txt"), "a") as f:
f.write("min_area = {}, threshold = {}, index = {}\n\n".format(
min_area, threshold, i))
f.write(df_clf.to_string(header=True, index=True))
f.write("\n" + "_" * 50 + "\n\n")
# condition positive (P)
P = y_true.count(1)
# condition negative (N)
N = y_true.count(0)
# true positive (TP)
TP = np.sum([
1 if y_pred[i] == y_true[i] == 1 else 0
for i in range(total_number)
])
# true negative (TN)
TN = np.sum([
1 if y_pred[i] == y_true[i] == False else 0
for i in range(total_number)
])
# sensitivity, recall, hit rate, or true positive rate (TPR)
TPR = TP / P
# specificity, selectivity or true negative rate (TNR)
TNR = TN / N
# confusion matrix
conf_matrix = confusion_matrix(y_true, y_pred, normalize="true")
tn, fp, fn, tp = confusion_matrix(y_true, y_pred,
normalize="true").ravel()
# append test results to dictionary
dict_test["min_area"].append(min_area)
dict_test["threshold"].append(threshold)
dict_test["TPR"].append(TPR)
dict_test["TNR"].append(TNR)
# print progress bar
time.sleep(0.1)
printProgressBar(i + 1,
l,
prefix="Progress:",
suffix="Complete",
length=50)
# print test results to console
df_test = pd.DataFrame.from_dict(dict_test)
df_test.sort_values(by=["min_area", "threshold"], inplace=True)
with pd.option_context("display.max_rows", None, "display.max_columns",
None):
print(df_test)
# save DataFrame (as .txt and .pkl)
with open(os.path.join(save_dir, "test_results.txt"), "w+") as f:
f.write(df_test.to_string(header=True, index=True))
print("test_results.txt saved at {}".format(save_dir))
df_test.to_pickle(os.path.join(save_dir, "test_results.pkl"))
print("test_results.pkl saved at {}".format(save_dir))
def main(args):
# Get finetuning arguments
model_path = args.path
save = args.save
img_val = args.val
img_test = args.test
thresholds_to_plot = sorted(list(set(args.list)))
# thresholds_to_plot.sort()
# ========================= SETUP ==============================
# load model, setup and history
model, setup, history = utils.load_model_HDF5(model_path)
# data setup
directory = setup["data_setup"]["directory"]
val_data_dir = os.path.join(directory, "train")
nb_training_images = setup["data_setup"]["nb_training_images"]
nb_validation_images = setup["data_setup"]["nb_validation_images"]
# preprocessing_setup
rescale = setup["preprocessing_setup"]["rescale"]
shape = setup["preprocessing_setup"]["shape"]
preprocessing = setup["preprocessing_setup"]["preprocessing"]
# train_setup
color_mode = setup["train_setup"]["color_mode"]
learning_rate = setup["train_setup"]["learning_rate"]
decay = setup["train_setup"]["decay"]
epochs_trained = setup["train_setup"]["epochs_trained"]
nb_training_images_aug = setup["train_setup"]["nb_training_images_aug"]
epochs = setup["train_setup"]["epochs"]
batch_size = setup["train_setup"]["batch_size"]
channels = setup["train_setup"]["channels"]
validation_split = setup["train_setup"]["validation_split"]
architecture = setup["train_setup"]["architecture"]
loss = setup["train_setup"]["loss"]
# create directory to save results
model_dir_name = os.path.basename(str(Path(model_path).parent))
save_dir = os.path.join(
os.getcwd(),
"results",
directory,
architecture,
loss,
model_dir_name,
"finetune",
)
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
# ============================= PREPROCESSING ===============================
# get preprocessing function corresponding to model
preprocessing_function = utils.get_preprocessing_function(architecture)
# same preprocessing as in training
validation_datagen = ImageDataGenerator(
rescale=rescale,
data_format="channels_last",
validation_split=validation_split,
preprocessing_function=preprocessing_function,
)
# retrieve preprocessed validation images as a numpy array
validation_generator = validation_datagen.flow_from_directory(
directory=val_data_dir,
target_size=shape,
color_mode=color_mode,
batch_size=nb_validation_images,
shuffle=False,
class_mode="input",
subset="validation",
)
imgs_val_input = validation_generator.next()[0]
# ============== RECONSTRUCT IMAGES AND COMPUTE RESIDUAL MAPS ==============
# get reconstructed images (i.e predictions) on validation dataset
print("computing reconstructions of validation images...")
imgs_val_pred = model.predict(imgs_val_input)
# compute residual maps on validation dataset
resmaps_val = imgs_val_input - imgs_val_pred
if color_mode == "rgb":
resmaps_val = tf.image.rgb_to_grayscale(resmaps_val)
if save:
utils.save_np(imgs_val_input, save_dir, "imgs_val_input.npy")
utils.save_np(imgs_val_pred, save_dir, "imgs_val_pred.npy")
utils.save_np(resmaps_val, save_dir, "resmaps_val.npy")
# plot a sample validation image alongside its corresponding reconstruction and resmap for inspection
if img_val is not None:
plt.style.use("default")
# compute image index
index_val = validation_generator.filenames.index(img_val)
fig = utils.plot_input_pred_resmaps_val(imgs_val_input, imgs_val_pred,
resmaps_val, index_val)
fig.savefig(os.path.join(save_dir, "val_plots.png"))
print("figure saved at {}".format(
os.path.join(save_dir, "val_plots.png")))
# scale pixel values linearly to [0,1]
resmaps_val = scale_pixel_values(architecture, resmaps_val)
# Convert to 8-bit unsigned int for further processing
resmaps_val = img_as_ubyte(resmaps_val)
# ======= NUMBER OF REGIONS, THEIR MEAN SIZE AND STD DEVIATION WITH INCREASING THRESHOLDS =======
# blur resmaps
# resmaps_val = filter_gauss_images(resmaps_val)
# compute descriptive values
min_pixel_value = np.amin(resmaps_val)
max_pixel_value = np.amax(resmaps_val)
mu = resmaps_val.flatten().mean()
sigma = resmaps_val.flatten().std()
# set relevant threshold interval
threshold_min = int(round(scipy.stats.norm(mu, sigma).ppf(0.97), 1)) - 1
threshold_max = max_pixel_value
dict_out = {
"threshold": [],
"nb_regions": [],
"mean_areas_size": [],
"std_areas_size": [],
"sum_areas_size": [],
}
# compute and plot number of anomalous regions and their area sizes with increasing thresholds
print(
"computing anomalous regions and area sizes with increasing thresholds..."
)
for threshold in range(threshold_min, threshold_max + 1):
# threshold residual maps
resmaps_th = threshold_images(resmaps_val, threshold)
# filter images to remove salt noise
resmaps_fil = filter_median_images(resmaps_th, kernel_size=3)
# compute anomalous regions and their size for current threshold
resmaps_labeled, areas_all = label_images(resmaps_fil)
areas_all_1d = [item for sublist in areas_all for item in sublist]
# compute the size of the biggest anomalous region (corresponds with smallest threshold)
if threshold == threshold_min:
max_region_size = np.amax(np.array(areas_all_1d))
nb_regions = len(areas_all_1d)
if nb_regions == 0:
break
mean_areas_size = np.mean(areas_all_1d)
std_areas_size = np.std(areas_all_1d)
sum_areas_size = np.sum(areas_all_1d)
# append values to dictionnary
dict_out["threshold"].append(threshold)
dict_out["nb_regions"].append(nb_regions)
dict_out["mean_areas_size"].append(mean_areas_size)
dict_out["std_areas_size"].append(std_areas_size)
dict_out["sum_areas_size"].append(sum_areas_size)
print("threshold: {}".format(threshold))
plt.style.use("seaborn-darkgrid")
# print DataFrame to console
df_out = pd.DataFrame.from_dict(dict_out)
with pd.option_context("display.max_rows", None, "display.max_columns",
None):
print(df_out)
# save DataFrame (as text AND as pkl)
with open(os.path.join(save_dir, "test_results_all.txt"), "a") as f:
f.truncate(0)
f.write(df_out.to_string(header=True, index=True))
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.plot(df_out.threshold, df_out.sum_areas_size, "#1f77b4")
ax1.set_ylabel("sum of anomalous region's area size", color="#1f77b4")
for tl in ax1.get_yticklabels():
tl.set_color("#1f77b4")
ax2 = ax1.twinx()
ax2.plot(df_out.threshold, df_out.nb_regions, "#ff7f0e")
ax2.set_ylabel("number of anomalous regions", color="#ff7f0e")
for tl in ax2.get_yticklabels():
tl.set_color("#ff7f0e")
fig.savefig(os.path.join(save_dir, "plot_stat.pdf"))
# plt.show()
# plot a sample test image alongside its corresponding reconstruction and resmap for inspection
if img_test is not None:
plt.style.use("default")
test_data_dir = os.path.join(directory, "test")
total_number = utils.get_total_number_test_images(test_data_dir)
test_datagen = ImageDataGenerator(
rescale=rescale,
data_format="channels_last",
preprocessing_function=preprocessing_function,
)
# retrieve preprocessed test images as a numpy array
test_generator = test_datagen.flow_from_directory(
directory=test_data_dir,
target_size=shape,
color_mode=color_mode,
batch_size=total_number,
shuffle=False,
class_mode="input",
)
imgs_test_input = test_generator.next()[0]
# predict on test images
print("computing reconstructions of validation images...")
imgs_test_pred = model.predict(imgs_test_input)
# compute residual maps on test set
resmaps_test = imgs_test_input - imgs_test_pred
if color_mode == "rgb":
resmaps_test = tf.image.rgb_to_grayscale(resmaps_test)
# compute image index
index_test = test_generator.filenames.index(img_test)
# save three images
fig = utils.plot_input_pred_resmaps_test(imgs_test_input,
imgs_test_pred, resmaps_test,
index_test)
fig.savefig(os.path.join(save_dir, "test_plots.png"))
print("figure saved at {}".format(
os.path.join(save_dir, "test_plots.png")))
def get_stats(resmaps, th_min=128, th_max=255, plot=False):
dict_stat = {
"threshold": [],
"nb_regions": [],
"mean_areas_size": [],
"std_areas_size": [],
"sum_areas_size": [],
}
# compute and plot number of anomalous regions and their area sizes with increasing thresholds
print(
"computing anomalous regions and area sizes with increasing thresholds..."
)
for threshold in range(th_min, th_max + 1):
# threshold residual maps
resmaps_th = threshold_images(resmaps, threshold)
# filter images to remove salt noise
# resmaps_fil = filter_median_images(resmaps_th, kernel_size=3)
# compute anomalous regions and their size for current threshold
resmaps_labeled, areas_all = label_images(resmaps_th)
areas_all_1d = [item for sublist in areas_all for item in sublist]
# compute the size of the biggest anomalous region (corresponds with smallest threshold)
if threshold == th_min:
max_region_size = np.amax(np.array(areas_all_1d))
nb_regions = len(areas_all_1d)
if nb_regions == 0:
break
mean_areas_size = np.mean(areas_all_1d)
std_areas_size = np.std(areas_all_1d)
sum_areas_size = np.sum(areas_all_1d)
# append values to dictionnary
dict_stat["threshold"].append(threshold)
dict_stat["nb_regions"].append(nb_regions)
dict_stat["mean_areas_size"].append(mean_areas_size)
dict_stat["std_areas_size"].append(std_areas_size)
dict_stat["sum_areas_size"].append(sum_areas_size)
df_stat = pd.DataFrame.from_dict(dict_stat)
if plot:
fig = plt.figure()
plt.style.use("seaborn-darkgrid")
ax1 = fig.add_subplot(111)
lns1 = ax1.plot(df_stat.threshold,
df_stat.mean_areas_size,
"C0",
label="mean_areas_size") #1f77b4
lns2 = ax1.plot(df_stat.threshold,
df_stat.std_areas_size,
"C1",
label="std_areas_size")
ax1.set_xlabel("Thresholds")
ax1.set_ylabel("areas size [number of pixels]")
ax2 = ax1.twinx()
lns3 = ax2.plot(df_stat.threshold,
df_stat.nb_regions,
"C2",
label="nb_regions") #ff7f0e
ax2.set_ylabel("number of anomalous regions", color="C2")
for tl in ax2.get_yticklabels():
tl.set_color("C2")
lns = lns1 + lns2 + lns3
labs = [l.get_label() for l in lns]
ax1.legend(lns, labs, loc=0)
plt.show()
return df_stat