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Object Detection with DETR! Python Practice!! - DETR을 활용한 객체 탐지! 파이썬 실습!!

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Object Detection with DETR! Python Practice!! - DETR을 활용한 객체 탐지! 파이썬 실습!!

(English) Object Detection with DETR! Python Practice!!

In the previous post we studied DETR!! Today, based on this DETR model, we will directly perform Object Detection!

detr_result

  • Let’s start with the conclusion again!!!
  • It finds and shows multiple detected objects in the image!!
  • It accurately displays many people and frisbees, along with their accuracy!!
  • Let’s explore the process together with Python code!!

1. Loading the DETR model from Hugging Face!!

Today’s DETR model will be loaded from Hugging Face, using the facebook/detr-resnet-50 model.

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import torch
import torchvision.transforms as T
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from PIL import Image
from transformers import DetrImageProcessor, DetrForObjectDetection


# 1️⃣ Set device (use CUDA if GPU is available)
device = "cuda" if torch.cuda.is_available() else "cpu"

# 2️⃣ Load DETR model and processor (pretrained model)
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50").to(device)
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")

processor : 🖼️ Image Processor (DetrImageProcessor)

Role: To preprocess the input image into a format that the DETR model can effectively understand and process.

Main Tasks:

  1. Image Resizing: Changes the size of the input image to a specific size required by the model.
  2. Image Normalization: Adjusts the pixel values of the image to a specific range to improve the stability of model training and inference.
  3. Tensor Conversion: Converts the image into a tensor format that can be used by deep learning frameworks such as PyTorch.
  4. Handling Model-Specific Requirements: Performs additional preprocessing tasks according to the model architecture (e.g., mask generation).

If we actually check the internal workings of the processor, we can see the preprocessing steps as below:

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DetrImageProcessor {
  "do_convert_annotations": true,
  "do_normalize": true,
  "do_pad": true,
  "do_rescale": true,
  "do_resize": true,
  "format": "coco_detection",
  "image_mean": [
    0.485,
    0.456,
    0.406
  ],
  "image_processor_type": "DetrImageProcessor",
  "image_std": [
    0.229,
    0.224,
    0.225
  ],
  "pad_size": null,
  "resample": 2,
  "rescale_factor": 0.00392156862745098,
  "size": {
    "longest_edge": 1333,
    "shortest_edge": 800
  }
}

model : 🤖 DETR Object Detection Model (DetrForObjectDetection)

Role: To perform object detection on the preprocessed image and predict the location and class of objects within the image. This is the core role.

Main Tasks:

  1. Feature Extraction: Extracts important visual features for object detection from the input image.
  2. Transformer Encoder-Decoder: Processes the extracted features through the Transformer structure to understand the relationships between objects in the image and learn information about each object.
  3. Object Prediction: Finally outputs the bounding box coordinates, the corresponding class labels, and the confidence scores of the detected objects in the image.

The DETR model is structured as shown below:

detr_model

2. Starting Object Detection with DETR!

It’s done with just a few lines of simple code!!!

I have prepared an image above where several people are playing with a frisbee! And then!

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import torch
import torchvision.transforms as T
from PIL import Image
from transformers import DetrImageProcessor, DetrForObjectDetection

# 1️⃣ Set device (use CUDA if GPU is available)
device = "cuda" if torch.cuda.is_available() else "cpu"

# 2️⃣ Load DETR model and processor (pretrained model)
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50").to(device)
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")

# 3️⃣ Load the bike.jpg image from the local directory
image_path = "catch_frisbee.jpg"
image = Image.open(image_path)

# 4️⃣ Preprocess the image (convert to DETR model input format)
inputs = processor(images=image, return_tensors="pt").to(device)

# 5️⃣ Model inference
with torch.no_grad():
    outputs = model(**inputs)

# 6️⃣ Post-process the results (convert Bounding Box & Labels)
target_sizes = torch.tensor([image.size[::-1]])  # (height, width) format
results = processor.post_process_object_detection(outputs, target_sizes=target_sizes)[0]

# 7️⃣ Output detected objects
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
    if score > 0.7:  # Output objects with confidence above 70%
        box = [round(i, 2) for i in box.tolist()]
        print(f"Detected {model.config.id2label[label.item()]} with confidence {round(score.item(), 3)} at {box}")

If we briefly analyze the code above:

  • It loads the model.
  • It loads the catch_frisbee image!
  • It preprocesses it through the processor.
  • It puts it into the model and performs inference!
  • It prints the detected content from results!

Then the output is! As shown below! It tells us the detected objects, their accuracy (confidence), and finally the bounding box coordinates!

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Detected person with confidence 0.783 at [12.91, 355.33, 32.23, 383.66]
Detected person with confidence 0.999 at [279.08, 255.76, 365.66, 423.82]
Detected person with confidence 0.995 at [533.57, 280.23, 584.71, 401.82]
Detected umbrella with confidence 0.744 at [459.41, 324.56, 496.24, 340.89]
Detected person with confidence 0.933 at [488.93, 340.06, 510.23, 376.37]
Detected person with confidence 0.835 at [0.01, 355.79, 11.03, 384.31]
Detected person with confidence 0.906 at [261.05, 346.35, 284.02, 378.22]
Detected person with confidence 0.99 at [574.15, 301.1, 605.79, 395.45]
Detected person with confidence 0.713 at [244.5, 349.68, 262.29, 378.9]
Detected person with confidence 0.997 at [132.21, 31.6, 310.32, 329.97]
Detected person with confidence 0.732 at [349.66, 352.63, 365.67, 378.28]
Detected person with confidence 0.796 at [209.17, 326.9, 232.89, 355.65]
Detected person with confidence 0.777 at [149.0, 347.84, 169.28, 381.43]
Detected person with confidence 0.991 at [163.45, 299.99, 206.14, 399.0]
Detected frisbee with confidence 1.0 at [181.55, 139.33, 225.96, 161.49]
Detected person with confidence 0.734 at [200.95, 350.37, 229.14, 380.88]
Detected person with confidence 0.737 at [467.46, 347.11, 483.07, 376.49]
Detected person with confidence 0.978 at [413.58, 253.38, 465.11, 416.57]
Detected person with confidence 0.73 at [597.38, 342.37, 613.34, 380.89]
Detected person with confidence 0.998 at [304.64, 70.92, 538.5, 410.45]

3. Visualization of Object Detection Results!!

Instead of simple text detection, let’s display bounding boxes on the image!

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import torch
import torchvision.transforms as T
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from PIL import Image
from transformers import DetrImageProcessor, DetrForObjectDetection


# 1️⃣ Set device (use CUDA if GPU is available)
device = "cuda" if torch.cuda.is_available() else "cpu"

# 2️⃣ Load DETR model and processor (pretrained model)
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50").to(device)
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")

# 3️⃣ Load the bike.jpg image from the local directory
image_path = "catch_frisbee.jpg"
image = Image.open(image_path)

# 4️⃣ Preprocess the image (convert to DETR model input format)
inputs = processor(images=image, return_tensors="pt").to(device)

# 5️⃣ Model inference
with torch.no_grad():
    outputs = model(**inputs)

# 6️⃣ Post-process the results (convert Bounding Box & Labels)
target_sizes = torch.tensor([image.size[::-1]])  # (height, width) format
results = processor.post_process_object_detection(outputs, target_sizes=target_sizes)[0]


# 7️⃣ Visualize detected objects with Bounding Boxes on the image
fig, ax = plt.subplots(1, figsize=(10, 6))
ax.imshow(image)

# Draw Bounding Boxes
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
    if score > 0.7:  # 🔹 Visualize objects with confidence above 70%
        box = [round(i, 2) for i in box.tolist()]
        x, y, w, h = box
        rect = patches.Rectangle((x, y), w - x, h - y, linewidth=2, edgecolor='r', facecolor='none')
        ax.add_patch(rect)
        ax.text(x, y, f"{model.config.id2label[label.item()]}: {round(score.item(), 2)}",
                fontsize=12, bbox=dict(facecolor='yellow', alpha=0.5))

# 8️⃣ Save the result
output_path = "detr_output.jpg"  # 🔹 Filename to save
plt.axis("off")  # 🔹 Remove axes
plt.savefig(output_path, bbox_inches="tight")
plt.show()

print(f"Detection result saved as {output_path}")

Through the code above,
The detected objects are visualized,
And saved as detr_output.jpg!!

detr_result

Object detection, it’s really easy, right?
However, it takes 8.5 seconds to detect objects in a single image… it’s still a bit slow!

(한국어) DETR을 활용한 객체 탐지! 파이썬 실습!!

지난 포스팅 에서 공부해보았던 DETR!!
오늘은 이 DETR 모델을 바탕으로 직접 객체 탐지(Object Detection)을 진행해 알아보겠습니다~!

detr_result

  • 오늘도도 결론부터!!!
  • 이미지에서 탐지된 여러 객체들을 찾아서 보여줍니다!!
  • 많은 사람들과 프리스비 등 객체를 정확도와 함께 보여줍니다!!
  • 함께, 파이썬 코드로 그 과정을 알아보아요!!

1. huggingface에서 DETR 모델 받기!!

오늘의 DETR 모델은 Huggingface로부터, facebook/detr-resnet-50 모델을 받아 진행해보겠습니다.

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import torch
import torchvision.transforms as T
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from PIL import Image
from transformers import DetrImageProcessor, DetrForObjectDetection


# 1️⃣ 디바이스 설정 (GPU 사용 가능하면 CUDA로 설정)
device = "cuda" if torch.cuda.is_available() else "cpu"

# 2️⃣ DETR 모델 및 프로세서 로드 (사전 학습된 모델)
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50").to(device)
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")

위 코드를 보면, 사전 학습된 facebook/detr-resnet-50의 Model과 Processor 를 로드하는데요~!
각각의 역할을 알아보자면!

processor : 🖼️ 이미지 프로세서 (DetrImageProcessor)

역할: 입력 이미지를 DETR 모델이 효과적으로 이해하고 처리할 수 있는 형태로 전처리(Preprocessing)하는 역할

주요 작업:

  1. 이미지 크기 조정 (Resizing): 입력 이미지의 크기를 모델이 요구하는 특정 크기로 변경
  2. 이미지 정규화 (Normalization): 이미지 픽셀 값을 특정 범위로 조정하여 모델 학습 및 추론 안정성 향상
  3. 텐서 변환 (Tensor Conversion): 이미지를 파이토치(PyTorch)와 같은 딥러닝 프레임워크에서 사용할 수 있는 텐서(Tensor) 형태 변환
  4. 모델별 요구 사항 처리: 모델 아키텍처에 따라 추가적인 전처리 작업 (예: 마스크 생성 등)을 수행

실제로 processor 를 내부를 확인해보면 아래와 같이 전처리 과정을 볼수 있습니다~

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DetrImageProcessor {
  "do_convert_annotations": true,
  "do_normalize": true,
  "do_pad": true,
  "do_rescale": true,
  "do_resize": true,
  "format": "coco_detection",
  "image_mean": [
    0.485,
    0.456,
    0.406
  ],
  "image_processor_type": "DetrImageProcessor",
  "image_std": [
    0.229,
    0.224,
    0.225
  ],
  "pad_size": null,
  "resample": 2,
  "rescale_factor": 0.00392156862745098,
  "size": {
    "longest_edge": 1333,
    "shortest_edge": 800
  }
}

model : 🤖 DETR 객체 감지 모델 (DetrForObjectDetection)

역할: 전처리된 이미지를 입력받아 이미지 내의 객체를 감지(Object Detection)하고, 해당 객체의 위치와 클래스를 예측하는 핵심적인 역할 수행

주요 작업:

  1. 이미지 특징 추출 (Feature Extraction): 입력 이미지에서 객체 감지에 중요한 시각적 특징들을 추출
  2. 트랜스포머 인코더-디코더 (Transformer Encoder-Decoder): 추출된 특징들을 트랜스포머 구조를 통해 처리하여 이미지 내 객체 간의 관계를 파악하고, 각 객체의 정보를 학습
  3. 객체 예측 (Object Prediction): 최종적으로 이미지 내에 존재하는 객체들의 바운딩 박스 좌표, 해당 객체의 클래스 레이블, 그리고 예측의 신뢰도 점수 출력

아래와 같이 DETR의 모델로 구성됨을 볼수 있습니다!!

detr_model

2. DETR로 객체탐지 시작!

간단한 코드 몇줄이면 끝!!!

Image

위와 같이 여러사람들이 프리스비로 놀고있는 이미지를 준비해보았습니다!! 그리고@!

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import torch
import torchvision.transforms as T
from PIL import Image
from transformers import DetrImageProcessor, DetrForObjectDetection

# 1️⃣ 디바이스 설정 (GPU 사용 가능하면 CUDA로 설정)
device = "cuda" if torch.cuda.is_available() else "cpu"

# 2️⃣ DETR 모델 및 프로세서 로드 (사전 학습된 모델)
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50").to(device)
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")

# 3️⃣ 로컬 디렉토리의 bike.jpg 이미지 로드
image_path = "catch_frisbee.jpg"
image = Image.open(image_path)

# 4️⃣ 이미지 전처리 (DETR 모델 입력 형태로 변환)
inputs = processor(images=image, return_tensors="pt").to(device)

# 5️⃣ 모델 추론
with torch.no_grad():
    outputs = model(**inputs)

# 6️⃣ 결과 후처리 (Bounding Box & Labels 변환)
target_sizes = torch.tensor([image.size[::-1]])  # (height, width) 형식
results = processor.post_process_object_detection(outputs, target_sizes=target_sizes)[0]

# 7️⃣ 감지된 객체 출력
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
    if score > 0.7:  # 신뢰도 70% 이상인 객체만 출력
        box = [round(i, 2) for i in box.tolist()]
        print(f"Detected {model.config.id2label[label.item()]} with confidence {round(score.item(), 3)} at {box}")

위의 코드를 간단하게 분석해보면,

  • 모델을 로드하고
  • catch_frisbee 이미지를 로드하고!
  • processor 를 통해 전처리하고,
  • model에 넣어서!! 추론한 뒤.
  • results 에서 탐지된 내용 print 하기!!

그럼 그 output은!!
아래와 같이!! 탐지된 객체와, 그 정확도(confidence), 마지막으로 바운딩 박스 좌표를 알려줍니다@!

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Detected person with confidence 0.783 at [12.91, 355.33, 32.23, 383.66]
Detected person with confidence 0.999 at [279.08, 255.76, 365.66, 423.82]
Detected person with confidence 0.995 at [533.57, 280.23, 584.71, 401.82]
Detected umbrella with confidence 0.744 at [459.41, 324.56, 496.24, 340.89]
Detected person with confidence 0.933 at [488.93, 340.06, 510.23, 376.37]
Detected person with confidence 0.835 at [0.01, 355.79, 11.03, 384.31]
Detected person with confidence 0.906 at [261.05, 346.35, 284.02, 378.22]
Detected person with confidence 0.99 at [574.15, 301.1, 605.79, 395.45]
Detected person with confidence 0.713 at [244.5, 349.68, 262.29, 378.9]
Detected person with confidence 0.997 at [132.21, 31.6, 310.32, 329.97]
Detected person with confidence 0.732 at [349.66, 352.63, 365.67, 378.28]
Detected person with confidence 0.796 at [209.17, 326.9, 232.89, 355.65]
Detected person with confidence 0.777 at [149.0, 347.84, 169.28, 381.43]
Detected person with confidence 0.991 at [163.45, 299.99, 206.14, 399.0]
Detected frisbee with confidence 1.0 at [181.55, 139.33, 225.96, 161.49]
Detected person with confidence 0.734 at [200.95, 350.37, 229.14, 380.88]
Detected person with confidence 0.737 at [467.46, 347.11, 483.07, 376.49]
Detected person with confidence 0.978 at [413.58, 253.38, 465.11, 416.57]
Detected person with confidence 0.73 at [597.38, 342.37, 613.34, 380.89]
Detected person with confidence 0.998 at [304.64, 70.92, 538.5, 410.45]

3. 객체 탐지결과물의 시각화!!

단순 텍스트 탐지가 아니라 그림에 바운딩박스로 표시해봅니다!@

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import torch
import torchvision.transforms as T
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from PIL import Image
from transformers import DetrImageProcessor, DetrForObjectDetection


# 1️⃣ 디바이스 설정 (GPU 사용 가능하면 CUDA로 설정)
device = "cuda" if torch.cuda.is_available() else "cpu"

# 2️⃣ DETR 모델 및 프로세서 로드 (사전 학습된 모델)
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50").to(device)
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")

# 3️⃣ 로컬 디렉토리의 bike.jpg 이미지 로드
image_path = "catch_frisbee.jpg"
image = Image.open(image_path)

# 4️⃣ 이미지 전처리 (DETR 모델 입력 형태로 변환)
inputs = processor(images=image, return_tensors="pt").to(device)

# 5️⃣ 모델 추론
with torch.no_grad():
    outputs = model(**inputs)

# 6️⃣ 결과 후처리 (Bounding Box & Labels 변환)
target_sizes = torch.tensor([image.size[::-1]])  # (height, width) 형식
results = processor.post_process_object_detection(outputs, target_sizes=target_sizes)[0]


# 7️⃣ 감지된 객체를 이미지에 Bounding Box로 시각화
fig, ax = plt.subplots(1, figsize=(10, 6))
ax.imshow(image)

# Bounding Box 그리기
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
    if score > 0.7:  # 🔹 신뢰도 70% 이상인 객체만 시각화
        box = [round(i, 2) for i in box.tolist()]
        x, y, w, h = box
        rect = patches.Rectangle((x, y), w-x, h-y, linewidth=2, edgecolor='r', facecolor='none')
        ax.add_patch(rect)
        ax.text(x, y, f"{model.config.id2label[label.item()]}: {round(score.item(), 2)}",
                fontsize=12, bbox=dict(facecolor='yellow', alpha=0.5))

# 8️⃣ 결과 저장
output_path = "detr_output.jpg"  # 🔹 저장할 파일명
plt.axis("off")  # 🔹 축 제거
plt.savefig(output_path, bbox_inches="tight")
plt.show()

print(f"Detection result saved as {output_path}")

위 코드를 통하여,
감지된 객체를 시각화하고
detr_output.jpg 로도 저장하게됩니다~!!

detr_result

객체 탐지, 참 쉽죠~?
다만, 1개 이미지에서 객체 탐지에 시간이 8.5초가 소요,, 역시 좀 오래걸리네요!

AI, Experiment
CVPR CVPR 2020 Python Detr 객체 탐지 Object Detection Huggingface detr-resnet-50
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