+        image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)

+

+    # rainy_data_path = glob.glob("/home/takensoft/Pictures/화창한날, 비오는날 프레임2000장/SUNNY/**/**/*.png")

+    rainy_data_path = glob.glob("/home/takensoft/Pictures/폭우 빗방울 (475개)/*.png")

+        with torch.no_grad():

+            image_arnn = arnn(image_tensor)

+        with torch.no_grad():

+            result = classifier(image_arnn['attention_map_list'][-1])

+import random

+import time

+import visdom

+import glob

+import torch

+import cv2

+from torchvision.transforms import ToTensor, Compose, Normalize

+from flask import request

+from model.AttentiveRNN import AttentiveRNN

+from model.Classifier import Resnet as Classifier

+from subfuction.image_crop import crop_image

+

+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

+

+# execute visdom instance first

+# to do that, install visdom via pip and execute in terminal

+

+def process_image():

+    # vis = visdom.Visdom()

+    arnn = AttentiveRNN(6, 3, 2)

+    arnn.load_state_dict(torch.load("weights/ARNN_trained_weight_6_3_2.pt"))

+    arnn.to(device=device)

+    arnn.eval()

+    crop_size = (512, 512)

+    start_point = (750, 450)

+    tf_toTensor = ToTensor()

+    classifier = Classifier(in_ch=1)

+    classifier.load_state_dict(torch.load("weights/classifier_e19_weight_1080p_512512_fixed_wrong_resolution_and_ch.pt"))

+    classifier.to(device=device)

+    classifier.eval()

+    rainy_data_path = glob.glob("/home/takensoft/Desktop/KOLAS_TEST/정상/*.png")

+    # rainy_data_path = glob.glob("/home/takensoft/Pictures/화창한날, 비오는날 프레임2000장/RAIN/**/**/*.png")

+    # rainy_data_path = glob.glob("/home/takensoft/Pictures/폭우 빗방울 (475개)/*.png")

+    img_path = rainy_data_path

+    # clean_data_path = glob.glob("/home/takensoft/Documents/AttentiveRNNClassifier/output/original/*.png")

+

+    # img_path = rainy_data_path + clean_data_path

+    # normalize = Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])

+    random.shuffle(img_path)

+

+    index = []

+

+    for i in iter(range(len(img_path))):

+        ori_img = cv2.imread(img_path[i])

+        image = crop_image(ori_img, crop_size, start_point)

+        if not image.any():

+            continue

+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

+        image_tensor = tf_toTensor(image)

+        image_tensor = image_tensor.unsqueeze(0)

+        image_tensor = image_tensor.to(device)

+        with torch.no_grad():

+            image_arnn = arnn(image_tensor)

+

+        input_win = 'input_window'

+        attention_map_wins = [f'attention_map_{i}' for i in range(6)]

+        prediction_win = 'prediction_window'

+

+        # Visualize attention maps using visdom

+        # vis.images(

+        #     image_tensor,

+        #     opts=dict(title=f"input"),

+        #     win=input_win

+        # )

+        # for idx, attention_map in enumerate(image_arnn['attention_map_list']):

+        #     if idx == 0 or idx == 5:

+        #         vis.images(

+        #             attention_map.cpu(),  # Expected shape: (batch_size, C, H, W)

+        #             opts=dict(title=f'Attention Map {idx + 1}'),

+        #             win=attention_map_wins[idx]

+        #         )

+        # arnn_result = normalize(image_arnn['x'])

+        with torch.no_grad():

+            result = classifier(image_arnn['attention_map_list'][-1])

+        result = result.to("cpu")

+        _, predicted = torch.max(result.data, 1)

+        print(result.data)

+        print(_)

+        print(predicted)

+        index += (predicted)

+        # # Load and display the corresponding icon

+        # if predicted == 0:

+        #     icon_path = 'asset/sun-svgrepo-com.png'

+        # else:  # elif result == 1

+        #     icon_path = 'asset/rain-svgrepo-com.png'

+

+        # # Load icon and convert to tensor

+        # icon_image = cv2.imread(icon_path, cv2.IMREAD_UNCHANGED)

+        # transform = Compose([

+        #     ToTensor()

+        # ])

+        # icon_tensor = transform(icon_image).unsqueeze(0)  # Add batch dimension

+

+        # # Visualize icon using visdom

+        # vis.images(

+        #     icon_tensor,

+        #     opts=dict(title='Weather Prediction'),

+        #     win=prediction_win

+        # )

+        # time.sleep(1)

+    print(index)

+    # result = classifier(image_arnn['x'])

+    # result = result.to("cpu")

+    # _, predicted = torch.max(result.data, 1)

+    # if predicted == 0:

+    #     rain = False

+    # else:  # elif result == 1

+    #     rain = True

+    # return {

+    #     'rain': rain,

+    # }, 200

+

+if __name__ == "__main__":

+    process_image()(파일 끝에 줄바꿈 문자 없음)