Datasets:

Sayali9141
/

traffic_signal_images

	# -- coding: utf-8 --
	"""traffic_object_detection.ipynb

	Automatically generated by Colaboratory.

	Original file is located at
	https://colab.research.google.com/drive/1B7DIM9ABIA6RRhA8tL_3rcxL9M1iIP7D
	"""

	!pip install datasets

	from datasets import load_dataset

	dataset = load_dataset("Sayali9141/traffic_signal_images")

	next(iter(dataset['train']))

	import matplotlib.pyplot as plt
	from IPython.display import display
	from PIL import Image

	"""Trying out hugging face YOLO

	"""

	from transformers import AutoFeatureExtractor

	feature_extractor = AutoFeatureExtractor.from_pretrained("hustvl/yolos-small")

	from transformers import YolosForObjectDetection

	model = YolosForObjectDetection.from_pretrained("hustvl/yolos-small")

	"""This code shows how to get image from the url"""

	device = 'cuda'

	model = model.to(device)

	from PIL import Image
	import requests
	import base64
	from io import BytesIO
	from time import time
	import matplotlib.pyplot as plt
	import torch
	# colors for visualization
	COLORS = [[0.000, 0.447, 0.741], [0.850, 0.325, 0.098], [0.929, 0.694, 0.125],
	[0.494, 0.184, 0.556], [0.466, 0.674, 0.188], [0.301, 0.745, 0.933]]

	def plot_results(pil_img, prob, boxes):
	count=0
	plt.figure(figsize=(16,10))
	plt.imshow(pil_img)
	ax = plt.gca()
	colors = COLORS * 100
	for p, (xmin, ymin, xmax, ymax), c in zip(prob, boxes.tolist(), colors):
	cl = p.argmax()
	if model.config.id2label[cl.item()] in ['car', 'truck'] :
	ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin,
	fill=False, color=c, linewidth=3))
	text = f'{model.config.id2label[cl.item()]}: {p[cl]:0.2f}'
	ax.text(xmin, ymin, text, fontsize=15,
	bbox=dict(facecolor='yellow', alpha=0.5))
	count+=1
	plt.axis('off')
	plt.show()
	# print(count)
	return(count)

	all_counts = []
	for i in range (22000, 22005):
	row = dataset['train'][i]
	start= time()
	pixel_values = feature_extractor(row['image_url'], return_tensors="pt").pixel_values
	pixel_values = pixel_values.to(device)
	# pixel_values.shape
	with torch.no_grad():
	outputs = model(pixel_values, output_attentions=True)
	probas = outputs.logits.softmax(-1)[0, :, :-1]
	keep = probas.max(-1).values > 0.8
	target_sizes = torch.tensor(row['image_url'].size[::-1]).unsqueeze(0)
	postprocessed_outputs = feature_extractor.post_process(outputs, target_sizes)
	bboxes_scaled = postprocessed_outputs[0]['boxes']
	plot_results(row['image_url'], probas[keep], bboxes_scaled[keep])
	count = 0

	for p, boxes in zip(probas[keep], bboxes_scaled[keep]):
	cl = p.argmax()
	if model.config.id2label[cl.item()] in ['car', 'truck']:
	count += 1
	all_counts.append(count)
	print(time()-start)

	# def select_columns(example):
	# return {key: example[key] for key in ['timestamp', 'camera_id', 'latitude', 'longitude']}

	# subset_dataset = dataset['train'].map(select_columns[dataset['train']])

	# data_yolo= subset_dataset.to_pandas()

	# data_yolo['box_count'][22000:22004]= [x for x in all_counts]

	#create interactive map
	#create interactive map using latitude and longitude of counts column
	# import folium
	# from folium import plugins

	# # Create a map object and center it to the avarage coordinates to m
	# m = folium.Map(location=[df['latitude'].mean(), df['longitude'].mean()], zoom_start=10)

	# # Add marker for each row in the data
	# for i in range(0,len(df)):
	# folium.Marker([df.iloc[i]['latitude'], df.iloc[i]['longitude']], popup=df.iloc[i]['counts']).add_to(m)

	# # Display the map
	# m.save('map.html')
	# m