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True prediction equal the same range cell as GT. Fix bug where no preds but GT exist. whether statistics, ONNX save pre-processed file,

pull/2071/head
hanoch 2025-02-03 15:14:16 +02:00
parent 4c0190e554
commit d00653795f
4 changed files with 77 additions and 24 deletions
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12
YOLOv7onnx.py
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@ -21,7 +21,7 @@ from utils.general import labels_to_class_weights, increment_path, labels_to_ima
from utils.metrics import ap_per_class
from utils.general import box_iou
from utils.plots import plot_one_box
import pickle
import os
from utils.general import xywh2xyxy
from collections import defaultdict
@ -591,7 +591,9 @@ def main(opt):
tta_res = dict()
tta_res['predictions'] = predictions
tta_res['ground_truths'] = ground_truths
tta_res['iou_threshold'] = iou_threshold
tta_res['iou_threshold'] = opt.iou_thres
tta_res['conf_thres'] = opt.conf_thres
np.savetxt(os.path.join(opt.save_path,"image_preprocessed.csv"), img[0,:,:,0], delimiter=",")
with open(os.path.join(opt.save_path, 'metadata_for_pre_re_detection_threshold_' + str(det_threshold) + '.pkl'), 'wb') as f:
pickle.dump(tta_res, f)
@ -692,8 +694,12 @@ if __name__ == '__main__':
Plotting P/R curve over detections th=0.05
--cache-images --device 0 --weights /mnt/Data/hanoch/tir_old_tf/tir_od_1.5.onnx --img-size 512 --conf-thres 0.05 --iou-thres 0.5 --norm-type no_norm --save-path /mnt/Data/hanoch/runs/tir_old_1.5 --images-parent-folder /home/hanoch/projects/tir_frames_rois/marmon_noisy_sy --detection-no-gt
DEtections only New model Yolov7999
Detection
--cache-images --device 0 --weights /mnt/Data/hanoch/runs/train/yolov7999/weights/best.onnx --img-size 640 --conf-thres 0.66 --iou-thres 0.6 --norm-type single_image_percentile_0_1 --images-parent-folder /home/hanoch/projects/tir_frames_rois/marmon_noisy_sy --save-path /mnt/Data/hanoch/runs/yolov7999_onnx_run --detection-no-gt --adding-ext-noise
DEtections only New model Yolov7999 with noise addition
--cache-images --device 0 --weights /mnt/Data/hanoch/runs/train/yolov7999/weights/best.onnx --img-size 640 --conf-thres 0.48 --iou-thres 0.6 --norm-type single_image_percentile_0_1 --images-parent-folder /mnt/Data/hanoch/tir_frames_rois/onnx_bm --save-path /mnt/Data/hanoch/runs/yolov7999_onnx_run --detection-no-gt
P/R curve
--cache-images --device 0 --weights /mnt/Data/hanoch/runs/train/yolov7999/weights/best.onnx --img-size 640 --conf-thres 0.01 --iou-thres 0.6 --norm-type single_image_percentile_0_1 --test-files-path /home/hanoch/projects/tir_od/yolov7/tir_od/test_set/Test51a_Test40A_test_set.txt --save-path /mnt/Data/hanoch/runs/yolov7999_onnx_run/P_R_curve_test_set --adding-ext-noise
"""

71
test.py
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@ -347,21 +347,47 @@ def test(data,
# sensor type
if dataloader.dataset.use_csv_meta_data_file:
weather_condition = (dataloader.dataset.df_metadata[dataloader.dataset.df_metadata['tir_frame_image_file_name'] == str(path).split('/')[-1]]['weather_condition'].item())
if isinstance(weather_condition, str):
weather_condition = weather_condition.lower()
exec([x for x in weather_condition_vars if str(weather_condition) in x][0] + '.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))')
try:
weather_condition = (dataloader.dataset.df_metadata[dataloader.dataset.df_metadata['tir_frame_image_file_name'] == str(path).split('/')[-1]]['weather_condition'].item())
if isinstance(weather_condition, str):
weather_condition = weather_condition.lower()
exec([x for x in weather_condition_vars if str(weather_condition) in x][0] + '.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))')
except Exception as e:
print(f'{weather_condition} fname WARNING: Ignoring corrupted image and/or label {weather_condition}: {e}')
time_in_day = dataloader.dataset.df_metadata[dataloader.dataset.df_metadata['tir_frame_image_file_name'] == str(path).split('/')[-1]]['part_in_day'].item().lower()
# eval([x for x in time_vars if str(time_in_day) in x][0]).append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))
exec([x for x in time_vars if str(time_in_day) in x][0] + '.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))')
sensor_type = dataloader.dataset.df_metadata[dataloader.dataset.df_metadata['tir_frame_image_file_name'] == str(path).split('/')[-1]]['sensor_type'].item()
obj_range_m = torch.tensor(
[(object_size_to_range(obj_height_pixels=h.cpu(), focal=sensor_type, class_id=class_id.cpu().numpy().item()))
for class_id, (x, y, w, h) in zip(pred[:, 5], xyxy2xywh(pred[:, :4]))])
# obj_range_m = torch.tensor(
# [(object_size_to_range(obj_height_pixels=h.cpu(), focal=sensor_type, class_id=class_id.cpu().numpy().item()))
# for class_id, (x, y, w, h) in zip(pred[:, 5], xyxy2xywh(pred[:, :4]))])
gt_range = [(object_size_to_range(obj_height_pixels=h, focal=sensor_type, class_id=class_id.numpy().item())) for
class_id, (x, y, w, h) in zip(labels[:, 0].cpu(), labels[:, 1:5].cpu())]
# coupling the range cell between any overlapped IOU >TH between pred bbox and GT bbox
obj_range_m = list()
i = 0
for class_id_pred, (x1_p, y1_p, x2_p, y2_p) in zip(pred[:, 5].cpu(), pred[:, :4].cpu()):
range_candidate = torch.tensor(
object_size_to_range(obj_height_pixels=xyxy2xywh(pred[:, :4])[0][-1].cpu(),
focal=sensor_type,
class_id=class_id_pred.cpu().numpy().item()))
# Find any IOU overlapped between GT and prediction
for class_id_gt, (x1_gt, y1_gt, x2_gt, y2_gt), (xc,yc,w,h) in zip(labels[:, 0].cpu(),
xywh2xyxy(labels[:, 1:5].cpu()), labels[:, 1:5].cpu()):
ious = box_iou(torch.tensor((x1_gt, y1_gt, x2_gt, y2_gt)).unsqueeze(axis=0), torch.tensor((x1_p, y1_p, x2_p, y2_p)).unsqueeze(axis=0))
i += 1
if ious > iouv.cpu()[0]: #
range_candidate = torch.tensor(
object_size_to_range(obj_height_pixels=h.cpu(),
focal=sensor_type,
class_id=class_id_pred.cpu().numpy().item()))
break # the aligned GT/Pred was found no need to iterate more, this is the atmost candidate
obj_range_m.append(range_candidate)
# else: #ranges = func(sqrt(height*width))
# obj_range_m = torch.tensor([(object_size_to_range(obj_height_pixels=(np.sqrt(h.cpu()*w.cpu())), focal=sensor_type)) for ix, (x, y, w, h) in enumerate(xyxy2xywh(pred[:, :4]))])
# gt_range = [(object_size_to_range(obj_height_pixels=(np.sqrt(h*w)), focal=sensor_type)) for ix, (x, y, w, h) in enumerate(labels[:,1:5].cpu())]
@ -490,6 +516,8 @@ def test(data,
nt_stat_list_per_range = np.array([0, 0])
r_stat_list_per_range = np.array([0, 0])
p_stat_list_per_range = np.array([0, 0])
map50_per_range = np.array(0)
# ind = np.array([])
# exec('ind = np.where(ranges == rng_100)[0]')
ind = eval('np.where(ranges == rng_100)[0]')
@ -515,14 +543,17 @@ def test(data,
ap50_per_range, ap_per_range = ap_stat_list_per_range[:, 0], ap_stat_list_per_range.mean(1) # AP@0.5, AP@0.5:0.95
mp_per_range, mr_per_range, map50_per_range, map_per_range = p_stat_list_per_range.mean(), r_stat_list_per_range.mean(), ap50_per_range.mean(), ap_per_range.mean()
else:# no prediction at this range
if not bool(gt_per_range_bins[sensor_focal][rng_100]):# there are GT but no pred
nt_stat_list_per_range = np.array([0,0])
r_stat_list_per_range = np.array([0,0])
p_stat_list_per_range = np.array([0,0])
fn = len(gt_per_range_bins[sensor_focal][rng_100])
recall = 0 # no TP 0/TP+FN
precision = 0
map50_per_range = np.array(0)
r_stat_list_per_range = np.array([0, 0])
p_stat_list_per_range = np.array([0, 0])
fn = len(gt_per_range_bins[sensor_focal][rng_100])
recall = 0 # no TP 0/TP+FN
precision = 0
map50_per_range = np.array(0)
if not bool(gt_per_range_bins[sensor_focal][rng_100]):# there are no GT no pred
nt_stat_list_per_range = np.array([0,0]) # actual GT
else:
nt_stat_list_per_range = np.array(gt_per_range_bins[sensor_focal][rng_100]).sum()
# there are GT but no pred
# print(map50_per_range)
range_bins_map[sensor_focal][rng_100] = map50_per_range.item()
@ -827,8 +858,18 @@ tir_tiff_w_center_roi_validation_set_train_cls_usa.txt
# per day/nigh SY/ML
--weights /mnt/Data/hanoch/runs/train/yolov7999/weights/best.pt --device 0 --batch-size 16 --data data/tir_od_test_set.yaml --img-size 640 --conf 0.001 --verbose --norm-type single_image_percentile_0_1 --input-channels 1 --project test --task test --iou-thres 0.6 --csv-metadata-path tir_od/tir_center_merged_seq_tiff_last_original_png.xlsx
P/R
--weights /mnt/Data/hanoch/runs/train/yolov7999/weights/best.pt --device 0 --batch-size 16 --data data/tir_od_test_set.yaml --img-size 640 --verbose --norm-type single_image_percentile_0_1 --input-channels 1 --project test --task test --iou-thres 0.6 --csv-metadata-path tir_od/tir_center_merged_seq_tiff_last_original_png.xlsx --conf 0.65
mAP:
--weights /mnt/Data/hanoch/runs/train/yolov7999/weights/best.pt --device 0 --batch-size 16 --data data/tir_od_test_set.yaml --img-size 640 --verbose --norm-type single_image_percentile_0_1 --input-channels 1 --project test --task test --iou-thres 0.6 --csv-metadata-path tir_od/tir_center_merged_seq_tiff_last_original_png.xlsx --conf 0.01
Fixed wether csv P/R
--weights /mnt/Data/hanoch/runs/train/yolov7999/weights/best.pt --device 0 --batch-size 16 --data data/tir_od_test_set.yaml --img-size 640 --verbose --norm-type single_image_percentile_0_1 --input-channels 1 --project test --task test --csv-metadata-path tir_od/tir_tiff_seq_png_3_class_fixed_whether.xlsx --iou-thres 0.6 --conf 0.65
FOG
--weights /mnt/Data/hanoch/runs/train/yolov7999/weights/best.pt --device 0 --batch-size 16 --data data/tir_od_fog_set.yaml --img-size 640 --verbose --norm-type single_image_percentile_0_1 --input-channels 1 --project test --task test --csv-metadata-path tir_od/tir_tiff_seq_png_3_class_fixed_whether.xlsx --conf 0.01 --iou-thres 0.6
------- Error analysis ------------
1st run with conf_th=0.0001 then observe the desired threshold, re-run with the desired threshold abd observe images with bboxes given the deired threshold
"""

10
utils/datasets.py
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@ -677,6 +677,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
if self.use_csv_meta_data_file:
df = load_csv_xls_2_df(self.csv_meta_data_file)
self.df_metadata = pd.DataFrame(columns=['sensor_type', 'part_in_day', 'weather_condition', 'country', 'train_state', 'tir_frame_image_file_name'])
# TODO :HK @@ itereate tqdm(zip(self.img_files, self.label_files) and upon --force-csv-list remove missing entries in the csv from train/test lists!!!
for ix, fname in enumerate(self.img_files):
file_name = fname.split('/')[-1]
if not (df['tir_frame_image_file_name'] == file_name).any():
@ -720,13 +721,18 @@ class LoadImagesAndLabels(Dataset): # for training/testing
segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in l] # (cls, xy1...)
l = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1) # (cls, xywh)
l = np.array(l, dtype=np.float32)
# if (l[:, 0].max() >= num_cls):
# print('ka', i, l, lb_file, im_file)
l = np.array([lbl for lbl in l if lbl[0] < num_cls]) # take only labels index upto num of classes and omit others
if len(l):
assert l.shape[1] == 5, 'labels require 5 columns each' #@@HK TODO adding truncation ratio increase here : assert l.shape[1] == 6,
assert (l >= 0).all(), 'negative labels'
assert (l[:, 1:] <= 1).all(), 'non-normalized or out of bounds coordinate labels'
assert np.unique(l, axis=0).shape[0] == l.shape[0], 'duplicate labels'
l = np.array([lbl for lbl in l if lbl[0] < num_cls]) # take only labels index upto num of classes and omit others
# assert if (l[:, 0].max() < num_cls), 'class label out of range -- invalid' # max label can't be greater than num of labels
assert (l[:, 0].max() < num_cls), 'class label out of range -- invalid' # max label can't be greater than num of labels
# print(l[:, 0])
else:
ne += 1 # label empty

8
utils/metrics.py
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@ -200,8 +200,8 @@ def range_p_r_bar_plot(n_bins:int, range_bins_precision_all_classes:dict, range_
x = 100 * np.arange(n_bins) + 100
for (k_p, v_p), (k_r, v_r) in zip(range_bins_precision_all_classes.items(), range_bins_recall_all_classes.items()):
plt.figure()
bar1 = plt.bar(x , [x[class_graph] for x in v_p[:n_bins]], bar_width, color='b', label='p')
bar2 = plt.bar(x+10 , [x[class_graph] for x in v_r[:n_bins]], bar_width, color='g', label='r')
bar1 = plt.bar(x-bar_width , [x[class_graph] for x in v_p[:n_bins]], bar_width, color='b', label='p')
bar2 = plt.bar(x-bar_width//2 , [x[class_graph] for x in v_r[:n_bins]], bar_width, color='g', label='r')
plt.xticks(x, (x/100).astype('int'))
@ -210,13 +210,13 @@ def range_p_r_bar_plot(n_bins:int, range_bins_precision_all_classes:dict, range_
# height = rect.get_height()
# plt.text(rect.get_x() + rect.get_width() / 2.0, height, 'p/r', ha='center', va='bottom')
# plt.legend()
plt.legend()
# plt.tight_layout()
# plt.ylim([0.0, 1.05])
plt.ylabel('P/R')
plt.xlabel('Range[x100m]')
plt.grid()
plt.title('Sensor {}mm Precision/Recall vs. range class: {} conf: {}'.format(k_p, str(names[class_graph]), conf))
plt.title('Sensor {}mm Precision/Recall vs. range. Class: {} conf: {}'.format(k_p, str(names[class_graph]), conf))
# plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
plt.savefig(os.path.join(save_dir, 'p_r_distribution_distance_sensor_' + str(k_p) + '_class_' + str(names[class_graph]) +'.png'), dpi=250)
plt.clf()