search
HomeBackend DevelopmentPython TutorialCocoDetection in PyTorch (1)
CocoDetection in PyTorch (1)Jan 04, 2025 pm 12:26 PM

Buy Me a Coffee☕

*My post explains MS COCO.

CocoDetection() can use MS COCO dataset as shown below:

*Memos:

  • The 1st argument is root(Required-Type:str or pathlib.Path): *Memos:
    • It's the path to the images.
    • An absolute or relative path is possible.
  • The 2nd argument is annFile(Required-Type:str or pathlib.Path): *Memos:
    • It's the path to the annotations.
    • An absolute or relative path is possible.
  • The 3rd argument is transform(Optional-Default:None-Type:callable).
  • The 4th argument is target_transform(Optional-Default:None-Type:callable).
  • The 5th argument is transforms(Optional-Default:None-Type:callable).
from torchvision.datasets import CocoDetection

cap_train2014_data = CocoDetection(
    root="data/coco/imgs/train2014",
    annFile="data/coco/anns/trainval2014/captions_train2014.json"
)

cap_train2014_data = CocoDetection(
    root="data/coco/imgs/train2014",
    annFile="data/coco/anns/trainval2014/captions_train2014.json",
    transform=None,
    target_transform=None,
    transforms=None
)

ins_train2014_data = CocoDetection(
    root="data/coco/imgs/train2014",
    annFile="data/coco/anns/trainval2014/instances_train2014.json"
)

pk_train2014_data = CocoDetection(
    root="data/coco/imgs/train2014",
    annFile="data/coco/anns/trainval2014/person_keypoints_train2014.json"
)

len(cap_train2014_data), len(ins_train2014_data), len(pk_train2014_data)
# (82783, 82783, 82783)

cap_val2014_data = CocoDetection(
    root="data/coco/imgs/val2014",
    annFile="data/coco/anns/trainval2014/captions_val2014.json"
)

ins_val2014_data = CocoDetection(
    root="data/coco/imgs/val2014",
    annFile="data/coco/anns/trainval2014/instances_val2014.json"
)

pk_val2014_data = CocoDetection(
    root="data/coco/imgs/val2014",
    annFile="data/coco/anns/trainval2014/person_keypoints_val2014.json"
)

len(cap_val2014_data), len(ins_val2014_data), len(pk_val2014_data)
# (40504, 40504, 40504)

test2014_data = CocoDetection(
    root="data/coco/imgs/test2014",
    annFile="data/coco/anns/test2014/test2014.json"
)

test2015_data = CocoDetection(
    root="data/coco/imgs/test2015",
    annFile="data/coco/anns/test2015/test2015.json"
)

testdev2015_data = CocoDetection(
    root="data/coco/imgs/test2015",
    annFile="data/coco/anns/test2015/test-dev2015.json"
)

len(test2014_data), len(test2015_data), len(testdev2015_data)
# (40775, 81434, 20288)

cap_train2014_data
# Dataset CocoDetection
#     Number of datapoints: 82783
#     Root location: data/coco/imgs/train2014

cap_train2014_data.root
# 'data/coco/imgs/train2014'

print(cap_train2014_data.transform)
# None

print(cap_train2014_data.target_transform)
# None

print(cap_train2014_data.transforms)
# None

cap_train2014_data[0]
# (<pil.image.image image mode="RGB" size="640x480">,
#  [{'image_id': 9, 'id': 661611,
#    'caption': 'Closeup of bins of food that include broccoli and bread.'},
#   {'image_id': 9, 'id': 661977,
#    'caption': 'A meal is presented in brightly colored plastic trays.'},
#   {'image_id': 9, 'id': 663627,
#    'caption': 'there are containers filled with different kinds of foods'},
#   {'image_id': 9, 'id': 666765,
#    'caption': 'Colorful dishes holding meat, vegetables, fruit, and bread.'},
#   {'image_id': 9, 'id': 667602,
#    'caption': 'A bunch of trays that have different food.'}]) 

cap_train2014_data[1]
# (<pil.image.image image mode="RGB" size="640x426">,
#  [{'image_id': 25, 'id': 122312,
#    'caption': 'A giraffe eating food from the top of the tree.'},
#   {'image_id': 25, 'id': 127076,
#    'caption': 'A giraffe standing up nearby a tree '},
#   {'image_id': 25, 'id': 127238,
#    'caption': 'A giraffe mother with its baby in the forest.'},
#   {'image_id': 25, 'id': 133058,
#    'caption': 'Two giraffes standing in a tree filled area.'},
#   {'image_id': 25, 'id': 133676,
#    'caption': 'A giraffe standing next to a forest filled with trees.'}])

cap_train2014_data[2]
# (<pil.image.image image mode="RGB" size="640x428">,
#  [{'image_id': 30, 'id': 695774,
#    'caption': 'A flower vase is sitting on a porch stand.'},
#   {'image_id': 30, 'id': 696557,
#    'caption': 'White vase with different colored flowers sitting inside of it. '},
#   {'image_id': 30, 'id': 699041,
#    'caption': 'a white vase with many flowers on a stage'},
#   {'image_id': 30, 'id': 701216,
#    'caption': 'A white vase filled with different colored flowers.'},
#   {'image_id': 30, 'id': 702428,
#    'caption': 'A vase with red and white flowers outside on a sunny day.'}])

ins_train2014_data[0]
# (<pil.image.image image mode="RGB" size="640x480">,
#  [{'segmentation': [[500.49, 473.53, 599.73, ..., 20.49, 473.53]],
#    'area': 120057.13925, 'iscrowd': 0, 'image_id': 9,
#    'bbox': [1.08, 187.69, 611.59, 285.84], 'category_id': 51,
#    'id': 1038967},
#   {'segmentation': ..., 'category_id': 51, 'id': 1039564},
#   ...,
#   {'segmentation': ..., 'category_id': 55, 'id': 1914001}])

ins_train2014_data[1]
# (<pil.image.image image mode="RGB" size="640x426">,
#  [{'segmentation': [[437.52, 353.33, 437.87, ..., 437.87, 357.19]],
#    'area': 19686.597949999996, 'iscrowd': 0, 'image_id': 25,
#    'bbox': [385.53, 60.03, 214.97, 297.16], 'category_id': 25,
#    'id': 598548},
#  {'segmentation': [[99.26, 405.72, 133.57, ..., 97.77, 406.46]],
#   'area': 2785.8475500000004, 'iscrowd': 0, 'image_id': 25,
#   'bbox': [53.01, 356.49, 132.03, 55.19], 'category_id': 25,
#   'id': 599491}])

ins_train2014_data[2]
# (<pil.image.image image mode="RGB" size="640x428">,
#  [{'segmentation': [[267.38, 330.14, 281.81, ..., 269.3, 329.18]],
#    'area': 47675.66289999999, 'iscrowd': 0, 'image_id': 30,
#    'bbox': [204.86, 31.02, 254.88, 324.12], 'category_id': 64,
#    'id': 291613},
#   {'segmentation': [[394.34, 155.81, 403.96, ..., 393.38, 157.73]],
#    'area': 16202.798250000003, 'iscrowd': 0, 'image_id': 30,
#    'bbox': [237.56, 155.81, 166.4, 195.25], 'category_id': 86,
#    'id': 1155486}])

pk_train2014_data[0]
# (<pil.image.image image mode="RGB" size="640x480">, [])

pk_train2014_data[1]
# (<pil.image.image image mode="RGB" size="640x426">, [])

pk_train2014_data[2]
# (<pil.image.image image mode="RGB" size="640x428">, [])

cap_val2014_data[0]
# (<pil.image.image image mode="RGB" size="640x478">,
#  [{'image_id': 42, 'id': 641613,
#    'caption': 'This wire metal rack holds several pairs of shoes and sandals'},
#   {'image_id': 42, 'id': 645309,
#    'caption': 'A dog sleeping on a show rack in the shoes.'},
#   {'image_id': 42, 'id': 650217,
#    'caption': 'Various slides and other footwear rest in a metal basket outdoors.'},
#   {'image_id': 42,
#    'id': 650868,
#    'caption': 'A small dog is curled up on top of the shoes'},
#   {'image_id': 42,
#    'id': 652383,
#    'caption': 'a shoe rack with some shoes and a dog sleeping on them'}])

cap_val2014_data[1]
# (<pil.image.image image mode="RGB" size="565x640">,
#  [{'image_id': 73, 'id': 593422,
#    'caption': 'A motorcycle parked in a parking space next to another motorcycle.'},
#   {'image_id': 73, 'id': 746071,
#    'caption': 'An old motorcycle parked beside other motorcycles with a brown leather seat.'},
#   {'image_id': 73, 'id': 746170,
#    'caption': 'Motorcycle parked in the parking lot of asphalt.'},
#   {'image_id': 73, 'id': 746914,
#    'caption': 'A close up view of a motorized bicycle, sitting in a rack. '},
#   {'image_id': 73, 'id': 748185,
#    'caption': 'The back tire of an old style motorcycle is resting in a metal stand. '}])

cap_val2014_data[2]
# (<pil.image.image image mode="RGB" size="640x426">,
#  [{'image_id': 74, 'id': 145996,
#    'caption': 'A picture of a dog laying on the ground.'},
#   {'image_id': 74, 'id': 146710,
#    'caption': 'Dog snoozing by a bike on the edge of a cobblestone street'},
#   {'image_id': 74, 'id': 149398,
#    'caption': 'The white dog lays next to the bicycle on the sidewalk.'},
#   {'image_id': 74, 'id': 149638,
#    'caption': 'a white dog is sleeping on a street and a bicycle'},
#   {'image_id': 74, 'id': 150181,
#    'caption': 'A puppy rests on the street next to a bicycle.'}])

ins_val2014_data[0]
# (<pil.image.image image mode="RGB" size="640x478">,
#  [{'segmentation': [[382.48, 268.63, 330.24, ..., 394.09, 264.76]],
#    'area': 53481.5118, 'iscrowd': 0, 'image_id': 42,
#    'bbox': [214.15, 41.29, 348.26, 243.78], 'category_id': 18,
#    'id': 1817255}])

ins_val2014_data[1]
# (<pil.image.image image mode="RGB" size="565x640">,
#  [{'segmentation': [[134.36, 145.55, 117.02, ..., 138.69, 141.22]],
#    'area': 172022.43864999997, 'iscrowd': 0, 'image_id': 73,
#    'bbox': [13.0, 22.75, 535.98, 609.67], 'category_id': 4,
#    'id': 246920},
#   {'segmentation': [[202.28, 4.97, 210.57, 26.53, ..., 192.33, 3.32]],
#    'area': 52666.3402, 'iscrowd': 0, 'image_id': 73,
#    'bbox': [1.66, 3.32, 268.6, 271.91], 'category_id': 4,
#    'id': 2047387}])

ins_val2014_data[2]
# (<pil.image.image image mode="RGB" size="640x426">,
#  [{'segmentation': [[321.02, 321.0, 314.25, ..., 320.57, 322.86]],
#    'area': 18234.62355, 'iscrowd': 0, 'image_id': 74,
#    'bbox': [61.87, 276.25, 296.42, 103.18], 'category_id': 18,
#    'id': 1774},
#   {'segmentation': ..., 'category_id': 2, 'id': 128367},
#   ...
#   {'segmentation': ..., 'category_id': 1, 'id': 1751664}])

pk_val2014_data[0]
# (<pil.image.image image mode="RGB" size="640x478">, [])

pk_val2014_data[1]
# (<pil.image.image image mode="RGB" size="565x640">, [])

pk_val2014_data[2]
# (<pil.image.image image mode="RGB" size="640x426">,
#  [{'segmentation': [[301.32, 93.96, 305.72, ..., 299.67, 94.51]],
#    'num_keypoints': 0, 'area': 638.7158, 'iscrowd': 0,
#    'keypoints': [0, 0, 0, 0, ..., 0, 0], 'image_id': 74,
#    'bbox': [295.55, 93.96, 18.42, 58.83], 'category_id': 1,
#    'id': 195946},
#   {'segmentation': ..., 'category_id': 1, 'id': 253933},
#   ...
#   {'segmentation': ..., 'category_id': 1, 'id': 1751664}])

test2014_data[0]
# (<pil.image.image image mode="RGB" size="640x480">, [])

test2014_data[1]
# (<pil.image.image image mode="RGB" size="480x640">, [])

test2014_data[2]
# (<pil.image.image image mode="RGB" size="480x640">, [])

test2015_data[0]
# (<pil.image.image image mode="RGB" size="640x480">, [])

test2015_data[1]
# (<pil.image.image image mode="RGB" size="480x640">, [])

test2015_data[2]
# (<pil.image.image image mode="RGB" size="480x640">, [])

testdev2015_data[0]
# (<pil.image.image image mode="RGB" size="640x480">, [])

testdev2015_data[1]
# (<pil.image.image image mode="RGB" size="480x640">, [])

testdev2015_data[2]
# (<pil.image.image image mode="RGB" size="640x427">, [])

import matplotlib.pyplot as plt
from matplotlib.patches import Polygon, Rectangle
import torch

def show_images(data, main_title=None):
    file = data.root.split('/')[-1]
    if data[0][1] and "caption" in data[0][1][0]:
        if file == "train2014":
            plt.figure(figsize=(14, 5))
            plt.suptitle(t=main_title, y=0.9, fontsize=14)
            x_axis = 0.02
            x_axis_incr = 0.325
            fs = 10.5
        elif file == "val2014":
            plt.figure(figsize=(14, 6.5))
            plt.suptitle(t=main_title, y=0.94, fontsize=14)
            x_axis = 0.01
            x_axis_incr = 0.32
            fs = 9.4
        for i, (im, ann) in zip(range(1, 4), data):
            plt.subplot(1, 3, i)
            plt.imshow(X=im)
            plt.title(label=ann[0]["image_id"])
            y_axis = 0.0
            for j in range(0, 5):
                plt.figtext(x=x_axis, y=y_axis, fontsize=fs,
                            s=f'{ann[j]["id"]}:\n{ann[j]["caption"]}')
                if file == "train2014":
                    y_axis -= 0.1
                elif file == "val2014":
                    y_axis -= 0.07
            x_axis += x_axis_incr
            if i == 2 and file == "val2014":
                x_axis += 0.06
        plt.tight_layout()
        plt.show()
    elif data[0][1] and "segmentation" in data[0][1][0]:
        if file == "train2014":
            fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(14, 4))
        elif file == "val2014":
            fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(14, 5))
        fig.suptitle(t=main_title, y=1.0, fontsize=14)
        for (im, anns), axis in zip(data, axes.ravel()):
            for ann in anns:
                for seg in ann['segmentation']:
                    seg_tsors = torch.tensor(seg).split(2)
                    seg_lists = [seg_tsor.tolist() for seg_tsor in seg_tsors]
                    poly = Polygon(xy=seg_lists,
                                   facecolor="lightgreen", alpha=0.7)
                    axis.add_patch(p=poly)
                    px = []
                    py = []
                    for j, v in enumerate(seg):
                        if j%2 == 0:
                            px.append(v)
                        else:
                            py.append(v)
                    axis.plot(px, py, color='yellow')
                x, y, w, h = ann['bbox']
                rect = Rectangle(xy=(x, y), width=w, height=h,
                                 linewidth=3, edgecolor='r',
                                 facecolor='none', zorder=2)
                axis.add_patch(p=rect)
            axis.imshow(X=im)
            axis.set_title(label=anns[0]["image_id"])
        fig.tight_layout()
        plt.show()
    elif not data[0][1]:
        if file == "train2014":
            plt.figure(figsize=(14, 5))
            plt.suptitle(t=main_title, y=0.9, fontsize=14)
        elif file == "val2014":
            plt.figure(figsize=(14, 5))
            plt.suptitle(t=main_title, y=1.05, fontsize=14)
        elif file == "test2014" or "test2015":
            plt.figure(figsize=(14, 8))
            plt.suptitle(t=main_title, y=0.9, fontsize=14)
        for i, (im, _) in zip(range(1, 4), data):
            plt.subplot(1, 3, i)
            plt.imshow(X=im)
        plt.tight_layout()
        plt.show()

show_images(data=cap_train2014_data, main_title="cap_train2014_data")
show_images(data=ins_train2014_data, main_title="ins_train2014_data")
show_images(data=pk_train2014_data, main_title="pk_train2014_data")

show_images(data=cap_val2014_data, main_title="cap_val2014_data")
show_images(data=ins_val2014_data, main_title="ins_val2014_data")
show_images(data=pk_val2014_data, main_title="pk_val2014_data")

show_images(data=test2014_data, main_title="test2014_data")
show_images(data=test2015_data, main_title="test2015_data")
show_images(data=testdev2015_data, main_title="testdev2015_data")
</pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image></pil.image.image>

CocoDetection in PyTorch (1)

CocoDetection in PyTorch (1)

CocoDetection in PyTorch (1)

CocoDetection in PyTorch (1)

CocoDetection in PyTorch (1)

CocoDetection in PyTorch (1)

CocoDetection in PyTorch (1)

CocoDetection in PyTorch (1)

CocoDetection in PyTorch (1)

The above is the detailed content of CocoDetection in PyTorch (1). For more information, please follow other related articles on the PHP Chinese website!

Statement
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn
How to Use Python to Find the Zipf Distribution of a Text FileHow to Use Python to Find the Zipf Distribution of a Text FileMar 05, 2025 am 09:58 AM

This tutorial demonstrates how to use Python to process the statistical concept of Zipf's law and demonstrates the efficiency of Python's reading and sorting large text files when processing the law. You may be wondering what the term Zipf distribution means. To understand this term, we first need to define Zipf's law. Don't worry, I'll try to simplify the instructions. Zipf's Law Zipf's law simply means: in a large natural language corpus, the most frequently occurring words appear about twice as frequently as the second frequent words, three times as the third frequent words, four times as the fourth frequent words, and so on. Let's look at an example. If you look at the Brown corpus in American English, you will notice that the most frequent word is "th

How Do I Use Beautiful Soup to Parse HTML?How Do I Use Beautiful Soup to Parse HTML?Mar 10, 2025 pm 06:54 PM

This article explains how to use Beautiful Soup, a Python library, to parse HTML. It details common methods like find(), find_all(), select(), and get_text() for data extraction, handling of diverse HTML structures and errors, and alternatives (Sel

Image Filtering in PythonImage Filtering in PythonMar 03, 2025 am 09:44 AM

Dealing with noisy images is a common problem, especially with mobile phone or low-resolution camera photos. This tutorial explores image filtering techniques in Python using OpenCV to tackle this issue. Image Filtering: A Powerful Tool Image filter

Introduction to Parallel and Concurrent Programming in PythonIntroduction to Parallel and Concurrent Programming in PythonMar 03, 2025 am 10:32 AM

Python, a favorite for data science and processing, offers a rich ecosystem for high-performance computing. However, parallel programming in Python presents unique challenges. This tutorial explores these challenges, focusing on the Global Interprete

How to Perform Deep Learning with TensorFlow or PyTorch?How to Perform Deep Learning with TensorFlow or PyTorch?Mar 10, 2025 pm 06:52 PM

This article compares TensorFlow and PyTorch for deep learning. It details the steps involved: data preparation, model building, training, evaluation, and deployment. Key differences between the frameworks, particularly regarding computational grap

How to Implement Your Own Data Structure in PythonHow to Implement Your Own Data Structure in PythonMar 03, 2025 am 09:28 AM

This tutorial demonstrates creating a custom pipeline data structure in Python 3, leveraging classes and operator overloading for enhanced functionality. The pipeline's flexibility lies in its ability to apply a series of functions to a data set, ge

Serialization and Deserialization of Python Objects: Part 1Serialization and Deserialization of Python Objects: Part 1Mar 08, 2025 am 09:39 AM

Serialization and deserialization of Python objects are key aspects of any non-trivial program. If you save something to a Python file, you do object serialization and deserialization if you read the configuration file, or if you respond to an HTTP request. In a sense, serialization and deserialization are the most boring things in the world. Who cares about all these formats and protocols? You want to persist or stream some Python objects and retrieve them in full at a later time. This is a great way to see the world on a conceptual level. However, on a practical level, the serialization scheme, format or protocol you choose may determine the speed, security, freedom of maintenance status, and other aspects of the program

Mathematical Modules in Python: StatisticsMathematical Modules in Python: StatisticsMar 09, 2025 am 11:40 AM

Python's statistics module provides powerful data statistical analysis capabilities to help us quickly understand the overall characteristics of data, such as biostatistics and business analysis. Instead of looking at data points one by one, just look at statistics such as mean or variance to discover trends and features in the original data that may be ignored, and compare large datasets more easily and effectively. This tutorial will explain how to calculate the mean and measure the degree of dispersion of the dataset. Unless otherwise stated, all functions in this module support the calculation of the mean() function instead of simply summing the average. Floating point numbers can also be used. import random import statistics from fracti

See all articles

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

AI Hentai Generator

AI Hentai Generator

Generate AI Hentai for free.

Hot Article

R.E.P.O. Energy Crystals Explained and What They Do (Yellow Crystal)
2 weeks agoBy尊渡假赌尊渡假赌尊渡假赌
Repo: How To Revive Teammates
1 months agoBy尊渡假赌尊渡假赌尊渡假赌
Hello Kitty Island Adventure: How To Get Giant Seeds
4 weeks agoBy尊渡假赌尊渡假赌尊渡假赌

Hot Tools

WebStorm Mac version

WebStorm Mac version

Useful JavaScript development tools

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

DVWA

DVWA

Damn Vulnerable Web App (DVWA) is a PHP/MySQL web application that is very vulnerable. Its main goals are to be an aid for security professionals to test their skills and tools in a legal environment, to help web developers better understand the process of securing web applications, and to help teachers/students teach/learn in a classroom environment Web application security. The goal of DVWA is to practice some of the most common web vulnerabilities through a simple and straightforward interface, with varying degrees of difficulty. Please note that this software

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment