From 63edbf731698ea4b91e137179c29493118840cab Mon Sep 17 00:00:00 2001 From: "hy.kim" Date: Wed, 13 Sep 2023 09:19:40 +0900 Subject: [PATCH] update1 --- README.md | 725 ++++++++++++++++++++++++++++++++++++++++++++- images/output1.png | Bin 0 -> 37166 bytes images/output2.png | Bin 0 -> 39475 bytes images/output3.png | Bin 0 -> 22129 bytes images/output4.png | Bin 0 -> 42159 bytes images/output5.png | Bin 0 -> 44035 bytes val_dduk.ipynb | 2 +- 7 files changed, 725 insertions(+), 2 deletions(-) create mode 100644 images/output1.png create mode 100644 images/output2.png create mode 100644 images/output3.png create mode 100644 images/output4.png create mode 100644 images/output5.png diff --git a/README.md b/README.md index 710162e..6abc3a1 100644 --- a/README.md +++ b/README.md @@ -40,12 +40,708 @@ # 프로젝트 코드 (Validation)
- +Import Packages
+``` python + +import os +import time +import cv2 +import math +import numpy as np +%matplotlib inline +import matplotlib.pyplot as plt +import torch +import torch.nn.functional as F +from torchvision import transforms +from sklearn.metrics import accuracy_score +import yaml + +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + +``` + +
+
+ +
+Classfication +
+ + +``` python + +def crop_and_classify(img, boxes, classification_model, transform): + # classification_model.to(device) + # classification_model.eval() + + for box in boxes: + x1, y1, x2, y2 = box + # Expand the bounding box by 10% + width = x2 - x1 + height = y2 - y1 + x1 = max(int(x1 - 0.1 * width), 0) + y1 = max(int(y1 - 0.1 * height), 0) + x2 = min(int(x2 + 0.1 * width), img.shape[1] - 1) + y2 = min(int(y2 + 0.1 * height), img.shape[0] - 1) + + # Crop the image + cropped_img = img[y1:y2, x1:x2] + + # Transform the image to fit the input size of the classification model + transformed_img = transform(cropped_img) + transformed_img = transformed_img.unsqueeze(0) + + # Classify the cropped image + with torch.no_grad(): + outputs = classification_model(transformed_img) + _, predicted = torch.max(outputs.data, 1) + confidence = torch.nn.functional.softmax(outputs, dim=1)[0][predicted].item() + + # Print the classification result + print(f"Class: {predicted.item()}, Confidence: {confidence:.2f}") + + # Draw the bounding box and classification result on the image + cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2) + cv2.putText(img, f"Class: {predicted.item()}, Confidence: {confidence:.2f}", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0,255,0), 2) + return img + +``` + +
+
+ + +
+Detection +
+ + +``` python + +def run_detection_model(img, model_detect): + # img = transforms.ToTensor()(img).unsqueeze(0).to(device) # Convert image to tensor and add batch dimension + # model_detect.eval() # Set model to evaluation mode + img = transforms.ToTensor()(img).unsqueeze(0) + + # Move the model to the GPU + model_detect = model_detect.to(device) + + # Run inference + with torch.no_grad(): + detections = model_detect(img) + + # Extract bounding boxes and confidence scores + boxes = detections.pred[0][:, :4].cpu().numpy().tolist() + scores = detections.pred[0][:, 4].cpu().numpy().tolist() + + return boxes, scores + + +``` + +
+
+ + +
+Load detection model +
+ + +``` python + +weights_detect = "C:/Users/pinb/Desktop/pytorch/yolov5/runs/train/dduk_64_/weights/best.pt" +model_detect = torch.hub.load('ultralytics/yolov5', 'custom', path=weights_detect) +model_detect = model_detect.to(device).eval() + +``` + +``` planetext + +Using cache found in C:\Users\pinb/.cache\torch\hub\ultralytics_yolov5_master +YOLOv5 2023-6-12 Python-3.10.9 torch-1.12.1 CUDA:0 (NVIDIA GeForce RTX 3060, 12288MiB) + +Fusing layers... +Model summary: 157 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... + +``` + +
+
+ + +
+Load classification model +
+ + +``` python + +weights_cls = "C:/Users/pinb/Desktop/pytorch/yolov5/runs/train-cls/dduk_cls3/weights/best.pt" +model_cls = torch.hub.load('ultralytics/yolov5', 'custom', path=weights_cls) +model_cls = model_cls.to(device).eval() + +``` + +``` planetext + +Using cache found in C:\Users\pinb/.cache\torch\hub\ultralytics_yolov5_master +YOLOv5 2023-6-12 Python-3.10.9 torch-1.12.1 CUDA:0 (NVIDIA GeForce RTX 3060, 12288MiB) + +Fusing layers... +Model summary: 117 layers, 4173093 parameters, 0 gradients, 10.4 GFLOPs +WARNING YOLOv5 ClassificationModel is not yet AutoShape compatible. You must pass torch tensors in BCHW to this model, i.e. shape(1,3,224,224). + +``` + +
+
+ + +
+Load YAML +
+ + +``` python + +# # YAML 파일 경로 +yaml_path = "C:/Users/pinb/Desktop/pytorch/yolov5/runs/detect/test/data.yaml" + +def read_dataset_paths(file_path): + with open(file_path, 'r') as file: + data = yaml.safe_load(file) + test_path = data.get('test') + train_path = data.get('train') + val_path = data.get('val') + + return os.path.join(os.path.dirname(file_path), val_path) + +# # YAML 파일에서 데이터셋 경로 읽기 +val_path = read_dataset_paths(yaml_path) + +# 이미지가 있는 폴더 경로 +folder_path = val_path +result_path = os.path.join(folder_path, "result") +# folder_path = "C:/Users/pinb/Desktop/pytorch/yolov5/runs/detect/test/" +# result_path = "C:/Users/pinb/Desktop/pytorch/yolov5/runs/detect/test/result" + +def get_image_and_label_paths(folder_path): + image_paths = [] + label_paths = [] + for file_name in os.listdir(folder_path): + if file_name.endswith(".bmp") or file_name.endswith(".jpg") or file_name.endswith(".png"): + image_path = os.path.join(folder_path, file_name) + image_paths.append(image_path) + file_name = os.path.splitext(file_name)[0] + ".txt" + label_path = folder_path.replace("images", "labels") + label_path = os.path.join(label_path, file_name) + if os.path.exists(label_path): + label_paths.append(label_path) + else: + label_paths.append("") + + return image_paths, label_paths + +# 폴더 내의 이미지 파일 경로들을 가져옴 +image_paths, label_paths = get_image_and_label_paths(folder_path) + +if not os.path.exists(result_path): + os.makedirs(result_path) + +``` + +
+
+ + +
+Read Label Data +
+ + +``` python + +def read_label(label_path): + labels = [] + with open(label_path, 'r') as file: + lines = file.readlines() + for line in lines: + label = line.strip().split(' ') + label_class = int(label[0]) + bounding_box = tuple(map(float, label[1:])) + labels.append((label_class, bounding_box)) + return labels + +``` + +
+
+ + +
+Find closet label class +
+ + +``` python + +def find_closest_label_class(pred_box, labels): + min_distance = float('inf') + closest_class = -1 + + for i, label in enumerate(labels): + label_box = label[1] + label_center_x = (label_box[0] + label_box[2]) / 2 + label_center_y = (label_box[1] + label_box[3]) / 2 + + pred_center_x = (pred_box[0] + pred_box[2]) / 2 + pred_center_y = (pred_box[1] + pred_box[3]) / 2 + + distance = math.sqrt((label_center_x - pred_center_x) ** 2 + (label_center_y - pred_center_y) ** 2) + + if distance < min_distance: + min_distance = distance + closest_class = label[0] + + return closest_class + +``` + +
+
+ + + +
+Evaluate +
+ + +``` python + +lst_result = [[],[],[],[],[]] # class 0~4 +eval_times = [] + +def Evaluate(model_detect, model_cls, img, img_name, labels, output=True): + height, width, _ = img.shape + expand_ratio = 0.5 # The ratio to expand the ROI area + + # Inference detection model + start_time = time.time() + results = model_detect(img) + + # For each detection, crop the ROI and classify it + for *box, detect_conf, cls in results.xyxy[0]: + x1, y1, x2, y2 = map(int, box) + fbox = map(float, box) + + # Calculate the width and height of the bounding box + bbox_width = x2 - x1 + bbox_height = y2 - y1 + roi = (x1, y1, bbox_width, bbox_height) + + # Calculate the expanded coordinates + x1_expanded = max(0, x1 - int(bbox_width * expand_ratio)) + y1_expanded = max(0, y1 - int(bbox_height * expand_ratio)) + x2_expanded = min(width, x2 + int(bbox_width * expand_ratio)) + y2_expanded = min(height, y2 + int(bbox_height * expand_ratio)) + + roi_flat = (x1_expanded, + y1_expanded, + bbox_width + int(bbox_width * expand_ratio) * 2, + bbox_height + int(bbox_height * expand_ratio) * 2) + + # Crop the expanded ROI from the image + # roi = img[y1:y2, x1:x2] + roi_expanded = img.copy() + roi_expanded = roi_expanded[y1_expanded:y2_expanded, x1_expanded:x2_expanded] + roi_expanded = cv2.resize(roi_expanded, (224, 224)) + + # Convert numpy array to torch tensor, and normalize pixel values + roi_expanded = torch.from_numpy(roi_expanded).float().div(255.0) + + # Reshape tensor to (channels, height, width) + roi_expanded = roi_expanded.permute(2, 0, 1) + + # Add an extra dimension for the batch + roi_expanded = roi_expanded.unsqueeze(0) + + # Move roi_expanded to the same device as class_model + roi_expanded = roi_expanded.to(device) + class_result = model_cls(roi_expanded) + + # classfication result + probabilities = F.softmax(class_result, dim=1) + max_confidence, max_indices = torch.max(probabilities, dim=1) + class_pred = max_indices.item() + class_conf = max_confidence.item() + + # confidence + total_conf = (detect_conf + class_conf) * 0.5 + + # label - class + class_gt = find_closest_label_class((x1, y1, x2, y2), labels) + + # append (class, confidence, bounding box) + lst_result[class_pred].append((class_pred, class_gt, total_conf, (x1, y1, x2, y2))) + + # Put classification result on each ROI + thin = 1 + color = () + if class_pred == 0: # head + color = (0, 0, 255) + elif class_pred == 1: # helmet + color = (255, 0, 0) + elif class_pred == 2: # face + color = (0, 255, 0) + elif class_pred == 3: # mask + color = (255, 0, 255) + elif class_pred == 4: # helmet & mask + color = (255, 255, 0) + cv2.putText(img, f'{class_pred}: {total_conf}', (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, thin) + # cv2.rectangle(img, roi_flat, color, thin) + cv2.rectangle(img, roi, color, thin) + if output is True: + print(f'{img_name}\'s det + cls result [{class_pred}]: {total_conf}%, {roi}:') + + end_time = time.time() + total_time = end_time - start_time + eval_times.append(total_time) + if output is True: + print(f'Detect + Classfication Time: {total_time}s') + + # Save image + cv2.imwrite(f'{result_path}/{img_name}_rst.jpg', img) + + +``` + +
+
+ + + +
+Compare Ground Truth & Bounding Box +
+ + +``` python + +# Class +# 0: head +# 1: helmet +# 2: face +# 3: mask +# 4: helmet & mask + +def CompareClass(pr, gt): + if gt == 0: # head or face + if pr == 0 or pr == 2: + return True + else: + False + elif gt == 1: # helmet or mask + if pr == 1 or pr == 3 or pr == 4: + return True + else: + return False + +``` + +``` python + +def CompareGTandBox(predict, setting_mAP=0): + # 0: mAP@.5, 1: mAP@.5:.95 0 + # 비교하여 TP와 FP 구분 + results = [] + + tp_cnt = 0 + fp_cnt = 0 + totalbox_cnt = 0 + targetbox_cnt = len(predict) + + for p_class, gt_class, confidence, p_box in predict: + if CompareClass(p_class, gt_class): + # iou = calculate_iou(p_box, l_box) + # iou = confidence + if setting_mAP == 0: # mAP@.5 + if confidence >= 0.5: + # True Positive (TP) + tp_cnt = tp_cnt + 1 + totalbox_cnt = totalbox_cnt +1 + # class, confidence, boudingbox, TP/FP, precision, recall + results.append((p_class, confidence, p_box, True, tp_cnt/totalbox_cnt, tp_cnt/targetbox_cnt)) + elif setting_mAP == 1: # mAP@.5:.95 + if confidence >= 0.5 and confidence < 0.95: + # True Positive (TP) + tp_cnt = tp_cnt + 1 + totalbox_cnt = totalbox_cnt + 1 + # class, confidence, boudingbox, TP/FP, precision, recall + results.append((p_class, confidence, p_box, True, tp_cnt/totalbox_cnt, tp_cnt/targetbox_cnt)) + else: # all P, R + tp_cnt = tp_cnt + 1 + totalbox_cnt = totalbox_cnt + 1 + results.append((p_class, confidence, p_box, True, tp_cnt/totalbox_cnt, tp_cnt/targetbox_cnt)) + + else: + # False Positive (FP) + totalbox_cnt = totalbox_cnt + 1 + results.append((p_class, confidence, p_box, False, tp_cnt/totalbox_cnt, tp_cnt/targetbox_cnt)) + + return results + +``` + +
+
+ + +
+Get Precisions, Recalls and PR Curve +
+ + +``` python + +def GetPR(results): + results = sorted(results, key=lambda x: x[5], reverse=True) + # results = sorted(results, key=lambda x: x[1], reverse=True) + precisions = [item[4] for item in results] + recalls = [item[5] for item in results] + + return precisions, recalls + +``` + +
+
+ + +
+Calculate AP +
+ + +``` python + +def calculate_AP(precisions, recalls): + assert len(recalls) == len(precisions) + + sorted_indices = sorted(range(len(recalls)), key=lambda i: recalls[i]) + sorted_precisions = [precisions[i] for i in sorted_indices] + sorted_recalls = [recalls[i] for i in sorted_indices] + + ap = 0 + for i in range(1, len(sorted_recalls)): + recall_diff = sorted_recalls[i] - sorted_recalls[i-1] + ap += recall_diff * (sorted_precisions[i] + sorted_precisions[i-1]) * 0.5 + + return ap + +``` + +
+
+ + +
+Run +
+ + +``` python + +lst_result = [[],[],[],[],[]] # class 0~4 +eval_times = [] + +for i in range(0, len(image_paths)): + # Image name + img_dir = os.path.dirname(image_paths[i]) + img_name = os.path.basename(image_paths[i]) + + # Load image + img = cv2.imread(image_paths[i]) + img = cv2.resize(img, (640, 640)) + labels = read_label(label_paths[i]) + Evaluate(model_detect, model_cls, img, img_name, labels, False) + +Ps = [] +Rs = [] +ap5s = [] +ap95s = [] + +# Class +# 0: head +# 1: helmet +# 2: face +# 3: mask +# 4: helmet & mask + +# each class result +class_len = len(lst_result) + +for i in range(0, class_len): + results_all = CompareGTandBox(lst_result[i], -1) # all P, R + results_5 = CompareGTandBox(lst_result[i], 0) # mAP@.5 + results_95 = CompareGTandBox(lst_result[i], 1) # mAP@.5:.95 + + p, r = GetPR(results_all) + p1, r1 = GetPR(results_5) + p2, r2 = GetPR(results_95) + ap1 = calculate_AP(p1, r1) + ap2 = calculate_AP(p2, r2) + # print(p1) + # print(r1) + # print(p2) + # print(r2) + if len(p) != 0: + p = sum(p) / len(p) + else: + p = 0 + if len(r) != 0: + r = sum(r) / len(r) + else: + r = 0 + + Ps.append(p) + Rs.append(r) + ap5s.append(ap1) + ap95s.append(ap2) + print(f'Class {i}') + print(f'meanP:', p) + print(f'meanR:', r) + print(f"AP@.5:", ap1) + print(f"AP@.5:.95:", ap2) + + fig, axs = plt.subplots(1, 2, figsize=(8, 4)) + axs[0].plot(r1, p1) + axs[0].set_xlabel('Recall') + axs[0].set_ylabel('Precision') + axs[1].plot(r2, p2) + axs[1].set_xlabel('Recall') + axs[1].set_ylabel('Precision') + plt.suptitle(f'Class {i} Precision-Recall Curve (mAP@.5, mAP@.5:.95)') + print() + +# plt.tight_layout() +plt.show() + +mP = sum(Ps) / class_len +mR = sum(Rs) / class_len +mAP_5 = sum(ap5s) / class_len +mAP_95 = sum(ap95s) / class_len +mean_time = sum(eval_times) / len(eval_times) + +print(f'==> Union Model (Detection + Classification) - 5 classes') +print(f'[mean Precisions] {mP}') +print(f'[mean Recalls] {mR}') +print(f'[mAP@.5] {mAP_5}') +print(f'[mAP@.5:.95] {mAP_95}') +print(f'[mean time] {mean_time}s') +print() + + +#### Convert to evaluate #### + +# Class +# 0: head +# 1: helmet +# 2: face +# 3: mask +# 4: helmet & mask + +# => Model 1: (0, 2, 3) - head, (1, 4) - helmet +# => Model 2: (0, 1, 2) - face, (3, 4) - mask + +print(f'==> Convert to Model 1') +print(f'[mean Precisions] {((Ps[0] + Ps[2] + Ps[3]) / 3 + (Ps[1] + Ps[4]) / 2) / 2}') +print(f'[mean Recalls] {((Rs[0] + Rs[2] + Rs[3]) / 3 + (Rs[1] + Rs[4]) / 2) / 2}') +print(f'[mAP@.5] {((ap5s[0] + ap5s[2] + ap5s[3]) / 3 + (ap5s[1] + ap5s[4]) / 2) / 2}') +print(f'[mAP@.5:.95] {((ap95s[0] + ap95s[2] + ap95s[3]) / 3 + (ap95s[1] + ap95s[4]) / 2) / 2}') +print(f'[mean time] {mean_time}s') +print() + +print(f'==> Convert to Model 2') +print(f'[mean Precisions] {((Ps[0] + Ps[1] + Ps[2]) / 3 + (Ps[3] + Ps[4]) / 2) / 2}') +print(f'[mean Recalls] {((Rs[0] + Rs[1] + Rs[2]) / 3 + (Rs[3] + Rs[4]) / 2) / 2}') +print(f'[mAP@.5] {((ap5s[0] + ap5s[1] + ap5s[2]) / 3 + (ap5s[3] + ap5s[4]) / 3) / 2}') +print(f'[mAP@.5:.95] {((ap95s[0] + ap95s[1] + ap95s[2]) / 3 + (ap95s[3] + ap95s[4]) / 2)}') +print(f'[mean time] {mean_time}s') +print() + +``` + + +``` planetext + +Class 0 +meanP: 0.2451500387154128 +meanR: 0.1266693073789041 +AP@.5: 0.056300405455419165 +AP@.5:.95: 0.056300405455419165 + +Class 1 +meanP: 0.8831870385742567 +meanR: 0.43243055555555554 +AP@.5: 0.6344911937169155 +AP@.5:.95: 0.6344911937169155 + +Class 2 +meanP: 0 +meanR: 0 +AP@.5: 0 +AP@.5:.95: 0 + +Class 3 +meanP: 0.8349450369473351 +meanR: 0.40800165622897366 +AP@.5: 0.591933273620593 +AP@.5:.95: 0.591933273620593 + +Class 4 +... +meanR: 0.43473958333333357 +AP@.5: 0.727062010345605 +AP@.5:.95: 0.727062010345605 + +``` + +![output1](/images/output1.png) +![output2](/images/output2.png) +![output3](/images/output3.png) +![output4](/images/output4.png) +![output5](/images/output5.png) + + ``` planetext +==> Union Model (Detection + Classification) - 5 classes +[mean Precisions] 0.5704349663931353 +[mean Recalls] 0.2803682204993534 +[mAP@.5] 0.40195737662770653 +[mAP@.5:.95] 0.40195737662770653 +[mean time] 0.042649137104018055s + +==> Convert to Model 1 +[mean Precisions] 0.6230357850195234 +[mean Recalls] 0.3059043619902019 +[mAP@.5] 0.4484272475282988 +[mAP@.5:.95] 0.4484272475282988 +[mean time] 0.042649137104018055s + +==> Convert to Model 2 +[mean Precisions] 0.6190156182172799 +[mean Recalls] 0.30386862037965345 +[mAP@.5] 0.3349644805230888 +[mAP@.5:.95] 0.8897615083738772 +[mean time] 0.042649137104018055s + ``` @@ -53,6 +749,33 @@
+# 결과 + +
+Detection Result +
+ +![Confusion Matrix](/runs/train/dduk_64_/confusion_matrix.png) +![F1-Curve](/runs/train/dduk_64_/F1_curve.png) +![PR-Curve](/runs/train/dduk_64_/PR_curve.png) +![Results](/runs/train/dduk_64_/results.png) +![Train-Batch](/runs/train/dduk_64_/train_batch0.jpg) +![Val-Batch](/runs/train/dduk_64_/val_batch0_pred.jpg) + +
+
+ +
+Classification Result +
+ +![Train-Images](/runs/train-cls/dduk_cls3/train_images.jpg) +![Test-Images](/runs/train-cls/dduk_cls3/test_images.jpg) + +
+
+ + ### 참고[¶]() - 지능화캡스톤 과목, 김현용 교수 \ No newline at end of file diff --git a/images/output1.png b/images/output1.png new file mode 100644 index 0000000000000000000000000000000000000000..165cb5eaea9b6387aee8039be7f2d1cb6fcd70a5 GIT binary patch literal 37166 zcmbrmby!wkw>J8aA|WM>l!6M<0wS%Hgmg)FNOub&T_Pc&q#%vdLx++I3MidYf^>rd z`iRJghb6m?Q4{9%CA*`cRGtpB5j5LJ>WXmsUriFwrO!#%nxW z_|8fY&olUsu)B<|yM~jMyO*h}B}&=U-PzvB-QL!W$uyldCniC{;NLJmjLYyq+5hMQn=v52H}xtt|@mJmZ11l&1Hq%^5FmjpeBe zJ5FC~eloaIDFj!2N@=l{+E9_9VaW~3UbuJpIt%w5sjg50X^R`|z0MezRhK7v$sWn- zaSp!T5_CvhIlp?%A^nYVVvX(B?UhDf!-J()ORoy1Timi%8WTT=zLI?;@{yZ7m8{4&-u^%NfcuYuEaFJ%ryM%vH=2Trz3=O~Y?*m)EtW4$e zv;Beb7C)g`-}SObPs&8L>aoZou{*+u_#5Jn?&lmSvL}C-n`6YfaH%t0;BkLo+1Ib? zmnkV9d9>=38}Ob;WS`^X<5#UUJno346JkpI$dn+zvspKnK3$<#ZRI<*u(lS(rkrlF z&>1N&FVAy&xVOJw7KfEQw;g57-MsTuYk&xagM;IJIHEIDti{p0?23Kig8A9WZoTvT z)Ay}@M;p98Tr*8xV^d>YT{!zI13f}>{vnCiRUQRozEQE-CeNO9+nf|I{2=7~U9vx2 zfWXw$RQ1N?lhf_C-LHz=rt70+z?lw(ge8!6U8-DX5Re03*yx$mX<{*TWm`vVfZ&fnW7n+X$*{5A!e z_danv?^_lSKbmfEdoF*i)>%8nXCdp0_)mF<#JNQnE~Yu|JJ}{GBOQ>&XIvL5nYia{BGc>q5@;yd3SjAiq! zAojgWyx0*nE+KVx9VZXZph=s{V)uL~l~KFCQ%h)8;O~#cZutt>!ubmeW#8QTsvGN%h5;F+G*s`o`6wW;~Iy(AJ;8{In2#Si<-^D}cHM|?!ZGT6n z0+&Bg%+Jp=`v(LN1sd7f+M0DnkYWZ=-*L{?t#{4=<0qa#^mT%NbOjvGTo)A;m2UFf z?A%@K`7FND_mZ0cWj5RFBhWH^_pNq`Of)5DXK%vQE;+`4Yp4jfl`k|iLhS4c(sFWi z_D{B3j;c7K2W&NmMdJF6`FI)8l5=mHaUiflJu^*!3Sd5tVG zmCrKj20J_7Y4hWKr_t+K{w%179tJ-pCqFZ@+?|~rlB=>YgHx@3$Br11rMi_LHpaY{ zlXadcv5^ZoPQUBA`HfS_{`K%m))OvN1pNQ(hItH5mHR+MpJQ98aBFVXRB7R4u^?(1I$|SNW zKPxFI!6PKREarD~8B9@eL|=RH8ZpFeN1T)iqIB_&mRN}%?Kf3n4|?!EcsaZavO2ySFy;SCpXQr^HbZ&GUN8>k2f z3VdQ>Vp2M~#|Iv;e>POacI)ZGI^{C$XmD^NZ&es8ZNITqe=)>k z&J6r*i_yM%ns?st`mTpU3^nh)larGO*;v|0+iwMtXJ`Ikoi0dS*K_mokQ0fBj3jOQ z^SfkdOsz

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