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		Upload traffic_object_detection.py
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        traffic_object_detection.py
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            # -*- coding: utf-8 -*-
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            """traffic_object_detection.ipynb
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            Automatically generated by Colaboratory.
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            Original file is located at
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                https://colab.research.google.com/drive/1B7DIM9ABIA6RRhA8tL_3rcxL9M1iIP7D
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            """
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            !pip install datasets
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            from datasets import load_dataset
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            dataset = load_dataset("Sayali9141/traffic_signal_images")
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            next(iter(dataset['train']))
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            import matplotlib.pyplot as plt
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            from IPython.display import display
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            from PIL import Image
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            """Trying out hugging face YOLO
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            """
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            from transformers import AutoFeatureExtractor
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            feature_extractor = AutoFeatureExtractor.from_pretrained("hustvl/yolos-small")
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            from transformers import YolosForObjectDetection
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            model = YolosForObjectDetection.from_pretrained("hustvl/yolos-small")
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            """This code shows how to get image from the url"""
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            device = 'cuda'
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            model = model.to(device)
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            from PIL import Image
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            import requests
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            import base64
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            from io import BytesIO
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            from time import time
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            import matplotlib.pyplot as plt
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            import torch
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            # colors for visualization
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            COLORS = [[0.000, 0.447, 0.741], [0.850, 0.325, 0.098], [0.929, 0.694, 0.125],
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                      [0.494, 0.184, 0.556], [0.466, 0.674, 0.188], [0.301, 0.745, 0.933]]
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            def plot_results(pil_img, prob, boxes):
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                count=0
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                plt.figure(figsize=(16,10))
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                plt.imshow(pil_img)
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                ax = plt.gca()
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                colors = COLORS * 100
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                for p, (xmin, ymin, xmax, ymax), c in zip(prob, boxes.tolist(), colors):
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                  cl = p.argmax()
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                  if model.config.id2label[cl.item()] in ['car', 'truck'] :
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                      ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin,
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                                                fill=False, color=c, linewidth=3))
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                      text = f'{model.config.id2label[cl.item()]}: {p[cl]:0.2f}'
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                      ax.text(xmin, ymin, text, fontsize=15,
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                              bbox=dict(facecolor='yellow', alpha=0.5))
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                      count+=1
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                plt.axis('off')
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                plt.show()
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                # print(count)
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                return(count)
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            all_counts = []
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            for i in range (22000, 22005):
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              row = dataset['train'][i]
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              start= time()
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              pixel_values = feature_extractor(row['image_url'], return_tensors="pt").pixel_values
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              pixel_values = pixel_values.to(device)
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              # pixel_values.shape
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              with torch.no_grad():
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                outputs = model(pixel_values, output_attentions=True)
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              probas = outputs.logits.softmax(-1)[0, :, :-1]
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              keep = probas.max(-1).values > 0.8
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              target_sizes = torch.tensor(row['image_url'].size[::-1]).unsqueeze(0)
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              postprocessed_outputs = feature_extractor.post_process(outputs, target_sizes)
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              bboxes_scaled = postprocessed_outputs[0]['boxes']
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              plot_results(row['image_url'], probas[keep], bboxes_scaled[keep])
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              count = 0
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              for p, boxes in zip(probas[keep], bboxes_scaled[keep]):
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                cl = p.argmax()
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                if model.config.id2label[cl.item()] in ['car', 'truck']:
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                  count += 1
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              all_counts.append(count)
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              print(time()-start)
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            # def select_columns(example):
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            #     return {key: example[key] for key in ['timestamp', 'camera_id', 'latitude', 'longitude']}
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            # subset_dataset = dataset['train'].map(select_columns[dataset['train']])
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            # data_yolo= subset_dataset.to_pandas()
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            # data_yolo['box_count'][22000:22004]= [x for x in all_counts]
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            #create interactive map
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            #create interactive map using latitude and longitude of counts column
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            # import folium
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            # from folium import plugins
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            # # Create a map object and center it to the avarage coordinates to m
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            # m = folium.Map(location=[df['latitude'].mean(), df['longitude'].mean()], zoom_start=10)
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            # # Add marker for each row in the data
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            # for i in range(0,len(df)):
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            #     folium.Marker([df.iloc[i]['latitude'], df.iloc[i]['longitude']], popup=df.iloc[i]['counts']).add_to(m)
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            # # Display the map
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            # m.save('map.html')
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            # m
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