Datasets:
Sayali9141
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Upload traffic_object_detection.py
Browse files- traffic_object_detection.py +118 -0
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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