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Commit 4f0a387f authored by ogueler@idiap.ch's avatar ogueler@idiap.ch
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fixed formatting

parent 35765013
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Pipeline #78153 failed
Stage: qa
Stage: test
Stage: doc
Stage: dist
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src/ptbench/engine/visualizer.py
+ 1
− 3
View file @ 4f0a387f
......@@ -9,11 +9,9 @@ import cv2
import numpy as np
import pandas as pd
from PIL import Image
from torchvision.transforms.functional import to_pil_image
from tqdm import tqdm
from torchvision.transforms.functional import to_pil_image, to_tensor
from ..utils.cam_utils import (
draw_boxes_on_image,
draw_largest_component_bbox_on_image,
......
src/ptbench/scripts/compare_vis.py
+ 15
− 3
View file @ 4f0a387f
......@@ -26,6 +26,7 @@ def _sorting_rule(folder_name):
else: # Everything else will be sorted alphabetically after fullgrad and before randomcam
return (3, folder_name)
def _get_images_from_directory(dir_path):
image_files = []
for root, _, files in os.walk(dir_path):
......@@ -34,6 +35,7 @@ def _get_images_from_directory(dir_path):
image_files.append(os.path.join(root, file))
return image_files
@click.command(
epilog="""Examples:
......@@ -133,13 +135,19 @@ def compare_vis(input_folder, output_folder) -> None:
os.makedirs(output_directory, exist_ok=True)
# Use a set (unordered collection of unique elements) for efficient membership tests
image_names = set([os.path.basename(img) for img in _get_images_from_directory(comparison_folders[0])])
image_names = {
os.path.basename(img)
for img in _get_images_from_directory(comparison_folders[0])
}
# Only keep image names that exist in all folders
for folder in comparison_folders[1:]:
# This is basically an intersection-check of contents of different folders
# Images that don't exist in all folders are removed from the set
image_names &= set([os.path.basename(img) for img in _get_images_from_directory(folder)])
image_names &= {
os.path.basename(img)
for img in _get_images_from_directory(folder)
}
if not image_names:
raise ValueError("No common images found in the folders.")
......@@ -165,7 +173,11 @@ def compare_vis(input_folder, output_folder) -> None:
)
axs = axs.ravel()
for i, folder in enumerate(comparison_folders):
image_path = [img for img in _get_images_from_directory(folder) if os.path.basename(img) == image_name][0]
image_path = [
img
for img in _get_images_from_directory(folder)
if os.path.basename(img) == image_name
][0]
try:
img = cv2.imread(image_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
......
tests/test_cam_utils.py
+ 14
− 2
View file @ 4f0a387f
......@@ -217,8 +217,20 @@ def test_draw_boxes_on_image():
img_with_boxes2 = np.zeros((256, 256, 3), dtype=np.uint8)
img_with_boxes3 = np.zeros((512, 512, 3), dtype=np.uint8)
bboxes = [
[torch.tensor(1),torch.tensor(10),torch.tensor(10),torch.tensor(20),torch.tensor(20)],
[torch.tensor(1),torch.tensor(30),torch.tensor(30),torch.tensor(40),torch.tensor(40)]
[
torch.tensor(1),
torch.tensor(10),
torch.tensor(10),
torch.tensor(20),
torch.tensor(20),
],
[
torch.tensor(1),
torch.tensor(30),
torch.tensor(30),
torch.tensor(40),
torch.tensor(40),
],
]
result = draw_boxes_on_image(img_with_boxes, bboxes)
......
tests/test_saliencymap_evaluator.py
+ 98
− 14
View file @ 4f0a387f
......@@ -15,8 +15,20 @@ from ptbench.engine.saliencymap_evaluator import (
def test_compute_max_iou_and_ioda():
detected_box = (10, 10, 100, 100)
gt_box_dict = [torch.tensor(1),torch.tensor(50),torch.tensor(50),torch.tensor(50),torch.tensor(50)]
gt_box_dict2 = [torch.tensor(1),torch.tensor(20),torch.tensor(20),torch.tensor(60),torch.tensor(60)]
gt_box_dict = [
torch.tensor(1),
torch.tensor(50),
torch.tensor(50),
torch.tensor(50),
torch.tensor(50),
]
gt_box_dict2 = [
torch.tensor(1),
torch.tensor(20),
torch.tensor(20),
torch.tensor(60),
torch.tensor(60),
]
gt_boxes = [gt_box_dict, gt_box_dict2]
iou, ioda = _compute_max_iou_and_ioda(detected_box, gt_boxes)
......@@ -30,7 +42,13 @@ def test_compute_max_iou_and_ioda():
def test_compute_max_iou_and_ioda_zero_detected_area():
detected_box = (10, 10, 0, 0)
gt_box_dict = [torch.tensor(1),torch.tensor(50),torch.tensor(50),torch.tensor(50),torch.tensor(50)]
gt_box_dict = [
torch.tensor(1),
torch.tensor(50),
torch.tensor(50),
torch.tensor(50),
torch.tensor(50),
]
gt_boxes = [gt_box_dict]
iou, ioda = _compute_max_iou_and_ioda(detected_box, gt_boxes)
......@@ -42,7 +60,13 @@ def test_compute_max_iou_and_ioda_zero_detected_area():
def test_compute_max_iou_and_ioda_zero_gt_area():
detected_box = (10, 10, 100, 100)
gt_box_dict = [torch.tensor(1),torch.tensor(50),torch.tensor(50),torch.tensor(0),torch.tensor(0)]
gt_box_dict = [
torch.tensor(1),
torch.tensor(50),
torch.tensor(50),
torch.tensor(0),
torch.tensor(0),
]
gt_boxes = [gt_box_dict]
iou, ioda = _compute_max_iou_and_ioda(detected_box, gt_boxes)
......@@ -54,7 +78,13 @@ def test_compute_max_iou_and_ioda_zero_gt_area():
def test_compute_max_iou_and_ioda_zero_intersection():
detected_box = (10, 10, 100, 100)
gt_box_dict = [torch.tensor(1),torch.tensor(0),torch.tensor(0),torch.tensor(5),torch.tensor(5)]
gt_box_dict = [
torch.tensor(1),
torch.tensor(0),
torch.tensor(0),
torch.tensor(5),
torch.tensor(5),
]
gt_boxes = [gt_box_dict]
iou, ioda = _compute_max_iou_and_ioda(detected_box, gt_boxes)
......@@ -65,8 +95,20 @@ def test_compute_max_iou_and_ioda_zero_intersection():
def test_compute_simultaneous_iou_and_ioda():
detected_box = (10, 10, 100, 100)
gt_box_dict1 = [torch.tensor(1),torch.tensor(50),torch.tensor(50),torch.tensor(50),torch.tensor(50)]
gt_box_dict2 = [torch.tensor(1),torch.tensor(70),torch.tensor(70),torch.tensor(30),torch.tensor(30)]
gt_box_dict1 = [
torch.tensor(1),
torch.tensor(50),
torch.tensor(50),
torch.tensor(50),
torch.tensor(50),
]
gt_box_dict2 = [
torch.tensor(1),
torch.tensor(70),
torch.tensor(70),
torch.tensor(30),
torch.tensor(30),
]
gt_boxes = [gt_box_dict1, gt_box_dict2]
iou, ioda = _compute_simultaneous_iou_and_ioda(detected_box, gt_boxes)
......@@ -80,7 +122,13 @@ def test_compute_avg_saliency_focus():
grayscale_cams2 = np.full((512, 512), 0.5)
grayscale_cams3 = np.zeros((256, 256))
grayscale_cams3[50:75, 50:100] = 1
gt_box_dict = [torch.tensor(1),torch.tensor(50),torch.tensor(50),torch.tensor(50),torch.tensor(50)]
gt_box_dict = [
torch.tensor(1),
torch.tensor(50),
torch.tensor(50),
torch.tensor(50),
torch.tensor(50),
]
gt_boxes = [gt_box_dict]
avg_saliency_focus = _compute_avg_saliency_focus(gt_boxes, grayscale_cams)
......@@ -94,7 +142,13 @@ def test_compute_avg_saliency_focus():
def test_compute_avg_saliency_focus_no_activations():
grayscale_cams = np.zeros((200, 200))
gt_box_dict = [torch.tensor(1),torch.tensor(50),torch.tensor(50),torch.tensor(50),torch.tensor(50)]
gt_box_dict = [
torch.tensor(1),
torch.tensor(50),
torch.tensor(50),
torch.tensor(50),
torch.tensor(50),
]
gt_boxes = [gt_box_dict]
avg_saliency_focus = _compute_avg_saliency_focus(gt_boxes, grayscale_cams)
......@@ -104,7 +158,13 @@ def test_compute_avg_saliency_focus_no_activations():
def test_compute_avg_saliency_focus_zero_gt_area():
grayscale_cams = np.ones((200, 200))
gt_box_dict = [torch.tensor(1),torch.tensor(50),torch.tensor(50),torch.tensor(0),torch.tensor(0)]
gt_box_dict = [
torch.tensor(1),
torch.tensor(50),
torch.tensor(50),
torch.tensor(0),
torch.tensor(0),
]
gt_boxes = [gt_box_dict]
avg_saliency_focus = _compute_avg_saliency_focus(gt_boxes, grayscale_cams)
......@@ -117,7 +177,13 @@ def test_compute_proportional_energy():
grayscale_cams2 = np.full((512, 512), 0.5)
grayscale_cams3 = np.zeros((512, 512))
grayscale_cams3[100:200, 100:200] = 1
gt_box_dict = [torch.tensor(1),torch.tensor(50),torch.tensor(50),torch.tensor(100),torch.tensor(100)]
gt_box_dict = [
torch.tensor(1),
torch.tensor(50),
torch.tensor(50),
torch.tensor(100),
torch.tensor(100),
]
gt_boxes = [gt_box_dict]
proportional_energy = _compute_proportional_energy(gt_boxes, grayscale_cams)
......@@ -135,7 +201,13 @@ def test_compute_proportional_energy():
def test_compute_proportional_energy_no_activations():
grayscale_cams = np.zeros((200, 200))
gt_box_dict = [torch.tensor(1),torch.tensor(50),torch.tensor(50),torch.tensor(50),torch.tensor(50)]
gt_box_dict = [
torch.tensor(1),
torch.tensor(50),
torch.tensor(50),
torch.tensor(50),
torch.tensor(50),
]
gt_boxes = [gt_box_dict]
proportional_energy = _compute_proportional_energy(gt_boxes, grayscale_cams)
......@@ -145,7 +217,13 @@ def test_compute_proportional_energy_no_activations():
def test_compute_proportional_energy_no_gt_box():
grayscale_cams = np.ones((200, 200))
gt_box_dict = [torch.tensor(1),torch.tensor(0),torch.tensor(0),torch.tensor(0),torch.tensor(0)]
gt_box_dict = [
torch.tensor(1),
torch.tensor(0),
torch.tensor(0),
torch.tensor(0),
torch.tensor(0),
]
gt_boxes = [gt_box_dict]
proportional_energy = _compute_proportional_energy(gt_boxes, grayscale_cams)
......@@ -156,7 +234,13 @@ def test_compute_proportional_energy_no_gt_box():
def test_calculate_localization_metrics():
grayscale_cams = np.zeros((200, 200))
detected_box = (10, 10, 100, 100)
gt_box_dict = [torch.tensor(1),torch.tensor(50),torch.tensor(50),torch.tensor(50),torch.tensor(50)]
gt_box_dict = [
torch.tensor(1),
torch.tensor(50),
torch.tensor(50),
torch.tensor(50),
torch.tensor(50),
]
ground_truth_box = [gt_box_dict]
(
......
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