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inference4submission.py
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inference4submission.py
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import cv2
import torch
import math
import numpy as np
import os
import json
import random
from PIL import Image, ImageDraw
import matplotlib.pyplot as plt
import torchvision.transforms as transforms
from torchvision.transforms import ToTensor
import torchvision
from mmdet.apis import async_inference_detector, inference_detector
from mmdet.apis.inference import init_detector
from ultralytics import YOLO
from tqdm import tqdm
from ensemble_boxes import *
from inference_utils import *
import natsort
from sahi import AutoDetectionModel
from sahi.utils.cv import read_image
from sahi.utils.file import download_from_url
from sahi.predict import get_prediction, get_sliced_prediction, predict
from IPython.display import Image
mmdet_config1 = './pretrained_weights/codetrSwinLO365_nafnet_kfold1_2048_16_3019_071/swinL_detr_o365_coco_nafnet.py'
mmdet_checkpoint1 = './pretrained_weights/codetrSwinLO365_nafnet_kfold1_2048_16_3019_071/epoch_16 (1).pth'
mmdet_config2 = './pretrained_weights/codetrSwinLO365_nafnet_kfold1_1568_12/swinL_detr_o365_coco_nafnet.py'
mmdet_checkpoint2 = './pretrained_weights/codetrSwinLO365_nafnet_kfold1_1568_12/epoch_24.pth'
mmdet_config3 = './pretrained_weights/codetr_nafnet_night2day_1536_0489/swinL_detr_o365_coco_nafnet.py'
mmdet_checkpoint3 = './pretrained_weights/codetr_nafnet_night2day_1536_0489/epoch_6_best.pth'
mmdet_config4 = './pretrained_weights/codetrSwinLO365_nafnet_kfold3_2048_16_3019_071/swinL_detr_o365_coco_nafnet (1).py'
mmdet_checkpoint4 = './pretrained_weights/codetrSwinLO365_nafnet_kfold3_2048_16_3019_071/epoch_16 (1).pth'
mmdet_config5 = './pretrained_weights/codetrSwinLO365_nafnet_kfold4_2048_16_3019_071/swinL_detr_o365_coco_nafnet (2).py'
mmdet_checkpoint5 = './pretrained_weights/codetrSwinLO365_nafnet_kfold4_2048_16_3019_071/epoch_16.pth'
mmdet_config6 = './pretrained_weights/codetrSwinLO365_nafnet_kfold5_2048_16_3019/swinL_detr_o365_coco_nafnet.py'
mmdet_checkpoint6 = './pretrained_weights/codetrSwinLO365_nafnet_kfold5_2048_16_3019/epoch_14.pth'
mmdet_config7 = './pretrained_weights/codetrSwinLO365_nafnet_kfold2_1568_16_/swinL_detr_o365_coco_nafnet.py'
mmdet_checkpoint7 = './pretrained_weights/codetrSwinLO365_nafnet_kfold2_1568_16_/epoch_10.pth'
mmdet_config8 = './pretrained_weights/codetr_trainall_nigh2day_1536/swinL_detr_o365_coco_nafnet.py'
mmdet_checkpoint8 = './pretrained_weights/codetr_trainall_nigh2day_1536/epoch_24 (1).pth'
mmdet_config9 = './pretrained_weights/pseudo_codetr_1536/swinL_detr_o365_coco_nafnet.py'
mmdet_checkpoint9 = './pretrained_weights/pseudo_codetr_1536/epoch_32.pth'
mmdet_config10 = './pretrained_weights/pseudo_codetr_1024/swinL_detr_o365_coco_nafnet.py'
mmdet_checkpoint10 = './pretrained_weights/pseudo_codetr_1024/epoch_29.pth'
# Yolo models
# yolov8x
yolo_checkpoint1 = './pretrained_weights/yolov8_trainall/best.pt'
# Yolov9e
yolov9_checkpoint1 = './pretrained_weights/yolov9_trainall/best.pt'
# Load MMDet models
# print('Loading SAHI models...')
# # Load SAHI models
# sahi_model1 = AutoDetectionModel.from_pretrained(
# model_type='mmdet3',
# model_path=mmdet_checkpoint10,
# config_path=mmdet_config10,
# confidence_threshold=0.5,
# image_size=None, # not supported
# device="cuda:0", # or 'cuda:0'
# )
# sahi_model2 = AutoDetectionModel.from_pretrained(
# model_type='mmdet3',
# model_path=mmdet_checkpoint1,
# config_path=mmdet_config1,
# confidence_threshold=0.5,
# image_size=None, # not supported
# device="cuda:0", # or 'cuda:0'
# )
print("SAHI models loaded")
mmdet_model1 = init_detector(mmdet_config1, mmdet_checkpoint1, device='cuda:0')
mmdet_model2 = init_detector(mmdet_config2, mmdet_checkpoint2, device='cuda:0')
mmdet_model3 = init_detector(mmdet_config3, mmdet_checkpoint3, device='cuda:0')
mmdet_model4 = init_detector(mmdet_config4, mmdet_checkpoint4, device='cuda:0')
mmdet_model5 = init_detector(mmdet_config5, mmdet_checkpoint5, device='cuda:0')
mmdet_model6 = init_detector(mmdet_config6, mmdet_checkpoint6, device='cuda:0')
mmdet_model7 = init_detector(mmdet_config7, mmdet_checkpoint7, device='cuda:0')
mmdet_model8 = init_detector(mmdet_config8, mmdet_checkpoint8, device='cuda:0')
mmdet_model9 = init_detector(mmdet_config9, mmdet_checkpoint9, device='cuda:0')
mmdet_model10 = init_detector(mmdet_config10, mmdet_checkpoint10, device='cuda:0')
yolo_model1 = YOLO(yolo_checkpoint1)
import yolov9
yolo_model2 = yolov9.load(
yolov9_checkpoint1,
device="cuda:0",
)
# SETTING
VISUALIZE = False
mmdet_threshold_base = 0.35
org_yolo_threshold = 0.5
weights = [5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 5.5, 15, 15, 5.5, 5.5]
iou_thr = 0.5
skip_box_thr = 0.001
# object colors
colors_dict ={'Bus':(255, 0, 0),
'Bike': (0, 255, 0),
'Car': (0, 0, 255),
'Pedestrian': (255, 255, 0),
'Truck': (255, 0, 255)}
mapping_dict = {0:'Bus', 1:'Bike', 2:'Car', 3:'Pedestrian', 4:'Truck'}
TEST_DIR = '/home/daitranskku/code/cvpr2024/aicity/AIC2024-TRACK4-TEAM15/src/lib/infer_DAT/results/test_single_x4/visualization'
ORG_CVPR_DIR = '/home/daitranskku/code/cvpr2024/aicity/AIC2024-TRACK4-TEAM15/sample_dataset/CVPR_test'
test_image_file_names = os.listdir(TEST_DIR)
# extract .png only
test_image_file_names = [f for f in test_image_file_names if f.endswith('.png')]
test_image_file_names = sorted(test_image_file_names)
SUBMISSION = []
# Inference
print('Start inference...')
for test_image_file_name in tqdm(test_image_file_names):
# ##########################################################################
# test_image_file_name = random.choice(test_image_file_names)
# ##########################################################################
if "N" in test_image_file_name:
mmdet_threshold = mmdet_threshold_base/2
print("Night time")
else:
mmdet_threshold = mmdet_threshold_base
image_path = os.path.join(TEST_DIR, test_image_file_name)
image = cv2.imread(image_path)
# inference mmdet model 1
mmdet_results1 = inference_detector(mmdet_model1, image)
mmdet_results2 = inference_detector(mmdet_model2, image)
mmdet_results3 = inference_detector(mmdet_model3, image)
mmdet_results4 = inference_detector(mmdet_model4, image)
mmdet_results5 = inference_detector(mmdet_model5, image)
mmdet_results6 = inference_detector(mmdet_model6, image)
mmdet_results7 = inference_detector(mmdet_model7, image)
mmdet_results8 = inference_detector(mmdet_model8, image)
mmdet_results9 = inference_detector(mmdet_model9, image)
mmdet_results10 = inference_detector(mmdet_model10, image)
mmdet_results = [mmdet_results1, mmdet_results2, mmdet_results3, mmdet_results4, mmdet_results5, mmdet_results6, mmdet_results7, mmdet_results8, mmdet_results9, mmdet_results10]
boxes_list = []
scores_list = []
labels_list = []
# Processing MMDet models
for mmdet_result in mmdet_results:
raw_public_bboxes, raw_public_labels, raw_public_scores = mmdet3x_convert_to_bboxes_mmdet(mmdet_result, mmdet_threshold)
vis_image = image.copy()
# visualize each model's result
temp_norm_boxes = []
temp_scores = []
temp_labels = []
for i,box in enumerate(raw_public_bboxes):
x1, y1, x2, y2 = box
x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
# normalize for ensemble
norm_x1 = x1 / image.shape[1]
norm_y1 = y1 / image.shape[0]
norm_x2 = x2 / image.shape[1]
norm_y2 = y2 / image.shape[0]
temp_norm_boxes.append([norm_x1, norm_y1, norm_x2, norm_y2])
temp_scores.append(raw_public_scores[i])
temp_labels.append(int(raw_public_labels[i]))
# add to list
boxes_list.append(temp_norm_boxes)
scores_list.append(temp_scores)
labels_list.append(temp_labels)
##### Processing YOLOv8X models
yolo_results1 = yolo_model1(image, verbose=False)
class_names = yolo_results1[0].boxes.cls.tolist()
boxes = yolo_results1[0].boxes.xyxy.tolist()
scores = yolo_results1[0].boxes.conf.tolist()
temp_norm_boxes = []
temp_scores = []
temp_labels = []
for i, box in enumerate(boxes):
if scores[i] > org_yolo_threshold:
x1, y1, x2, y2 = box
x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
norm_x1 = x1 / image.shape[1]
norm_y1 = y1 / image.shape[0]
norm_x2 = x2 / image.shape[1]
norm_y2 = y2 / image.shape[0]
temp_norm_boxes.append([norm_x1, norm_y1, norm_x2, norm_y2])
temp_scores.append(scores[i])
temp_labels.append(int(class_names[i]))
boxes_list.append(temp_norm_boxes)
scores_list.append(temp_scores)
labels_list.append(temp_labels)
#### Processing YOLOv9e models
yolov9_results = yolo_model2(image_path)
predictions = yolov9_results.pred[0]
boxes = predictions[:, :4] # x1, y1, x2, y2
scores = predictions[:, 4]
categories = predictions[:, 5]
categories = [int(c) for c in categories]
temp_norm_boxes = []
temp_scores = []
temp_labels = []
for i, box in enumerate(boxes):
if scores[i] > org_yolo_threshold:
x1, y1, x2, y2 = box
x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
norm_x1 = x1 / image.shape[1]
norm_y1 = y1 / image.shape[0]
norm_x2 = x2 / image.shape[1]
norm_y2 = y2 / image.shape[0]
temp_norm_boxes.append([norm_x1, norm_y1, norm_x2, norm_y2])
temp_scores.append(scores[i])
temp_labels.append(int(categories[i]))
boxes_list.append(temp_norm_boxes)
scores_list.append(temp_scores)
labels_list.append(temp_labels)
# ENSEMBLE
boxes, scores, labels = weighted_boxes_fusion(boxes_list, scores_list, labels_list, weights=weights, iou_thr=iou_thr, skip_box_thr=skip_box_thr)
labels = [int(x) for x in labels]
# # # # Heuristic: check overlap between pedestrian and bike, if overlap, remove bike
for i, label in enumerate(labels):
if label == 3:
for j, label in enumerate(labels):
if label == 1:
iou = compute_iou(boxes[i], boxes[j])
if iou > 0.8:
print('Remove bike')
labels[j] = -1
scores[j] = 0
# check box area
box = boxes[i]
x1, y1, x2, y2 = box
x1, y1, x2, y2 = int(x1*image.shape[1]), int(y1*image.shape[0]), int(x2*image.shape[1]), int(y2*image.shape[0])
# scale
area = (x2-x1)*(y2-y1)
if area < 120:
labels[i] = -1
scores[i] = 0
print('Remove small box')
# check overlap between same class
for i, box in enumerate(boxes):
for j, box in enumerate(boxes):
if i != j:
if labels[i] == labels[j]:
iou = compute_iou(boxes[i], boxes[j])
if iou > 0.9:
print('Remove overlap')
if scores[i] > scores[j]:
labels[j] = -1
scores[j] = 0
else:
labels[i] = -1
scores[i] = 0
# # remove -1
boxes = [boxes[i] for i in range(len(boxes)) if labels[i] != -1]
scores = [scores[i] for i in range(len(scores)) if labels[i] != -1]
labels = [labels[i] for i in range(len(labels)) if labels[i] != -1]
### Remove box lower than threshold
if "N" in test_image_file_name:
box_threshold = mmdet_threshold_base/2
else:
box_threshold =mmdet_threshold_base
boxes = [boxes[i] for i in range(len(boxes)) if scores[i] >= box_threshold]
labels = [labels[i] for i in range(len(labels)) if scores[i] >= box_threshold]
scores = [scores[i] for i in range(len(scores)) if scores[i] >= box_threshold]
##### Start visualize with org image
org_image_file_name = test_image_file_name.split('/')[-1].split('.')[0][:-3] + '.png'
print(org_image_file_name)
org_image_path = os.path.join(ORG_CVPR_DIR, org_image_file_name)
vis_image = cv2.imread(org_image_path)
if VISUALIZE:
for i,box in enumerate(boxes):
x1, y1, x2, y2 = box
x1, y1, x2, y2 = int(x1*vis_image.shape[1]), int(y1*vis_image.shape[0]), int(x2*vis_image.shape[1]), int(y2*vis_image.shape[0])
detected_class = mapping_dict[labels[i]]
color = colors_dict[detected_class]
# Visualize
cv2.rectangle(vis_image, (x1, y1), (x2, y2), color, 2)
# show score
cv2.putText(vis_image, str(np.round(scores[i],2)), (x1, y1), cv2.FONT_HERSHEY_SIMPLEX, 1, color,2)
## Visualize prediction and GT
# # # ##### VISUALIZE
cv2.imwrite('cvpr_submission.png', vis_image)
scale_x = 800
scale_y = 800
vis_image = cv2.resize(vis_image, (scale_x, scale_y))
cv2.imshow('Prediction', vis_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
# add to submission
temp_submission = []
for i,box in enumerate(boxes):
x1, y1, x2, y2 = box
x1, y1, x2, y2 = int(x1*vis_image.shape[1]), int(y1*vis_image.shape[0]), int(x2*vis_image.shape[1]), int(y2*vis_image.shape[0])
detected_class = mapping_dict[labels[i]]
score = scores[i]
temp_submission.append({
"image_id": get_image_Id(org_image_file_name),
"category_id": int(labels[i]),
"bbox": [x1, y1, x2-x1, y2-y1],
"score": score
})
SUBMISSION.extend(temp_submission)
# break
# Save submission
current_dir = os.getcwd()
print('Save submission to: {}'.format(current_dir))
with open('cvpr_submission.json', 'w') as f:
json.dump(SUBMISSION, f)