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Posted Nov 22, 2023
By syshin
views 47 min read

정리:

Non-maximum Suppression (NMS)

설명:

  • 목적: 객체 검출(Object Detection)에서 여러 겹쳐진 경계 상자(Bounding Boxes) 중 최적의 상자를 선택하기 위함.
  • 방법:
    • 각 경계 상자에 대해 감지 확률을 평가.
    • 가장 높은 점수를 가진 상자를 선택하고, 다른 상자들과의 중첩(Overlap)을 계산.
    • 특정 임계값(IoU: Intersection over Union)보다 높은 중첩을 가진 상자는 제거.
  • 결과: 각 객체에 대해 단일, 최적의 경계 상자를 얻음.

설명:

  • 목적: R-CNN에서 사용, 이미지 내 잠재적인 관심 영역(Regions of Interest, RoIs)을 식별하기 위함.
  • 방법:
    • 세분화(Segmentation) 기법을 사용하여 이미지를 여러 영역으로 나눔.
    • 유사한 특징(색상, 질감 등)을 가진 인접 영역을 병합.
    • 다양한 크기와 모양의 후보 영역 생성.
  • 결과: 후보 영역들이 객체 검출 알고리즘의 입력으로 사용됨.

R-CNN, Fast R-CNN, Faster R-CNN

R-CNN (Regions with CNN features)

  • 구조:
    • Selective Search로 후보 영역 추출
    • 각 영역을 CNN에 입력하여 특징 추출
    • 추출된 특징을 SVM(Classifier)에 입력하여 객체 분류
  • 단점: 계산 비효율성 (각 영역마다 CNN 통과)

Fast R-CNN

  • 개선점:
    • 전체 이미지를 한 번에 CNN에 통과시키고, RoI Pooling Layer를 사용하여 특징 맵에서 후보 영역의 특징 추출
    • 속도와 효율성 향상
  • 구조: 전체 이미지 -> CNN -> RoI Pooling -> 분류 및 경계 상자 회귀

Faster R-CNN

  • 개선점:
    • Selective Search 대신 Region Proposal Network(RPN) 사용하여 후보 영역 생성.
    • 더 빠르고 정확한 객체 검출 가능.
  • 구조: RPN -> RoI Pooling -> 분류 및 경계 상자 회귀.

Anchor Boxes

기본 개념

  • 객체 검출(Object Detection) 알고리즘에서 사용되는, 미리 정의된 여러 크기와 비율의 가상의 경계 상자
  • 특정 영역에서 다양한 형태와 크기를 가진 객체들을 동시에 감지하는 데 도움을 줌

동작 방식

  1. 정의: 알고리즘은 다양한 크기와 비율의 Anchor Boxes를 미리 정의
    • 작은 크기, 중간 크기, 큰 크기 및 각기 다른 비율(정사각형, 세로로 긴 직사각형, 가로로 긴 직사각형 등)의 상자들을 정의하는데 주로 9개로 함
  2. 배치: 이미지의 각 위치에 이 Anchor Boxes를 배치
    • 일반적으로 특징 맵(Feature Map) 위에 수행
    • 특징 맵의 각 위치에서 모든 Anchor Boxes는 객체의 잠재적 위치로 간주
  3. 예측: 각 Anchor Box에 대해 두 가지 주요 예측을 수행
    • 클래스 예측: 이 Box에 객체가 있는지 없는지(배경 포함), 그리고 객체가 있다면 어떤 클래스에 속하는지
    • 경계 상자 조정: Box의 크기와 위치를 조정하여 실제 객체에 더 잘 맞도록

중요성

  • 다양한 형태 처리: 한 위치에서 여러 형태의 객체를 감지할 수 있게 해주므로, 객체의 형태와 크기가 다양한 경우에 유용
  • 중복 감지 방지: 동일한 객체에 대해 여러 Anchor Boxes가 반응할 수 있으므로, 이를 관리하기 위해 Non-Maximum Suppression(NMS)과 같은 기술 사용

예시

  • 이미지에 개와 고양이가 있을 때, 작은 Anchor Box는 멀리 있는 고양이를, 큰 Anchor Box는 가까이 있는 개를 감지
  • 비율이 다른 Box는 세로로 긴 개체(예: 서 있는 사람) 또는 가로로 긴 개체(예: 누워있는 사람)를 감지하는 데 유용

응용

  • Faster R-CNN과 YOLO와 같은 고급 객체 검출 알고리즘에서는 Anchor Boxes가 핵심 구성 요소로 사용
  • 이를 통해 이러한 알고리즘은 이미지 내의 다양한 객체를 효율적으로 식별하고 분류

Region Proposal Network (RPN) 상세 설명

기본 개념

  • Region Proposal Network (RPN)는 Faster R-CNN 알고리즘의 핵심 구성 요소입니다.
  • RPN의 주요 역할은 객체가 있을 가능성이 높은 영역, 즉 “Region Proposals”을 빠르고 효율적으로 식별하는 것입니다.

동작 방식

  1. 특징 맵 생성: RPN은 먼저 기본 CNN 네트워크를 통해 입력 이미지에서 특징 맵(Feature Map)을 생성합니다.
  2. 슬라이딩 윈도우: 이 특징 맵 위에서 RPN은 슬라이딩 윈도우 방식을 사용합니다. 각 윈도우 위치에서, RPN은 여러 Anchor Boxes에 대한 객체 존재 여부와 위치 조정을 예측합니다.
  3. Anchor Boxes: RPN은 다양한 크기와 비율의 Anchor Boxes를 사용하여 각 위치에서 여러 영역을 동시에 고려합니다.
  4. 예측: 각 Anchor Box에 대해 RPN은 두 가지를 예측합니다:
    • 객체 점수(Objectness Score): Anchor Box 내에 객체가 있는지 여부를 나타냅니다.
    • 경계 상자 조정: Anchor Box의 위치와 크기를 조정하여 실제 객체에 더 잘 맞도록 합니다.

핵심 요소

  • 속도와 정확도: RPN은 Selective Search 방법보다 훨씬 빠르면서도 정확하게 영역 제안을 생성합니다.
  • 통합 구조: RPN은 Faster R-CNN의 나머지 부분과 긴밀하게 통합되어 있으며, 객체 검출 파이프라인의 효율성과 정확도를 향상시킵니다.
  • 학습 가능: RPN은 역전파를 통해 학습될 수 있으며, 이미지에 대한 객체의 위치와 크기에 대해 더 정확한 예측을 하도록 최적화됩니다.

응용

  • Faster R-CNN: RPN은 Faster R-CNN에서 객체의 위치를 신속하게 제안하고, 이후의 네트워크가 이 영역들을 자세히 분석하여 객체를 분류하고 정확한 경계 상자를 생성합니다.

RPN의 도입은 객체 검출 분야에서 중요한 혁신으로, 이전 방법들에 비해 더 빠르고 정확한 영역 제안을 가능하게 합니다. 이는 객체 검출 알고리즘의 전체 성능을 향상시키는 중요한 요소입니다.

수업:

Positive Anchor Box, Negative Anchor Box

Ground Truth Bounding Box 겹치는 IOU 값에 따라 Anchor Box를 분류

  • IOU가 가장 높은 Positive Anchor Box
  • IOU가 0.7 이상이면 Positive Anchor Box
  • IOU가 0.3보다 낮으면 Negative Anchor Box
  • Classification에 비유하자면, 개&고양이 구별하는 모델이 있을 때, 개일 확률이 높은것만 가르키는것이 아니라, 고양이가 아닐 확률 또한 가르키는 방법.

Anchor Box 기반 BB regression

예측 bounding box와 positive anchor box와의 좌표 차이는 ground truth와 positive anchor box와의 좌표 차이와 최대한 동일하게 되게 regression 학습

Anchor Box 기반 RPN Output

  • k: Anchor Box 수

RPN Loss 함수

앞의 L 과 뒤의 L 이 다름

Ground Truth를 찾는 방법을 훈련시키다기 보다 Anchor Box를 찾는 훈련을 한다

실습

OpenCV DNN 패키지를 이용하여 Faster R-CNN 기반의 Object Detection 수행

  • Tensorflow 에서 Pretrained 된 모델 파일을 OpenCV에서 로드하여 이미지와 영상에 대한 Object Detection 수행.

입력 이미지로 사용될 이미지 다운로드/보기

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!mkdir /content/data
!wget -O ./data/beatles01.jpg https://raw.githubusercontent.com/chulminkw/DLCV/master/data/image/beatles01.jpg

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mkdir: cannot create directory ‘/content/data’: File exists
--2023-11-22 06:21:14--  https://raw.githubusercontent.com/chulminkw/DLCV/master/data/image/beatles01.jpg
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.108.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 383118 (374K) [image/jpeg]
Saving to: ‘./data/beatles01.jpg’

./data/beatles01.jp 100%[===================>] 374.14K  --.-KB/s    in 0.05s   

2023-11-22 06:21:14 (7.31 MB/s) - ‘./data/beatles01.jpg’ saved [383118/383118]

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import cv2
import matplotlib.pyplot as plt
%matplotlib inline

img = cv2.imread('./data/beatles01.jpg')
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

print('image shape:', img.shape)
plt.figure(figsize=(12, 12))
plt.imshow(img_rgb)

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image shape: (633, 806, 3)





<matplotlib.image.AxesImage at 0x7ec0aaf2e680>

Tensorflow에서 Pretrained 된 Inference모델(Frozen graph)와 환경파일을 다운로드 받은 후 이를 이용해 OpenCV에서 Inference 모델 생성

  • https://github.com/opencv/opencv/wiki/TensorFlow-Object-Detection-API 에 다운로드 URL 있음.
  • pretrained 모델은 http://download.tensorflow.org/models/object_detection/faster_rcnn_resnet50_coco_2018_01_28.tar.gz 에서 다운로드 후 압축 해제
  • pretrained 모델을 위한 환경 파일은 https://github.com/opencv/opencv_extra/blob/master/testdata/dnn/faster_rcnn_resnet50_coco_2018_01_28.pbtxt 에서 다운로드
  • download된 모델 파일과 config 파일을 인자로 하여 inference 모델을 DNN에서 로딩함.
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!mkdir ./pretrained
!wget -O ./pretrained/faster_rcnn_resnet50_coco_2018_01_28.tar.gz http://download.tensorflow.org/models/object_detection/faster_rcnn_resnet50_coco_2018_01_28.tar.gz
!wget -O ./pretrained/config_graph.pbtxt https://raw.githubusercontent.com/opencv/opencv_extra/master/testdata/dnn/faster_rcnn_resnet50_coco_2018_01_28.pbtxt

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--2023-11-22 06:21:15--  http://download.tensorflow.org/models/object_detection/faster_rcnn_resnet50_coco_2018_01_28.tar.gz
Resolving download.tensorflow.org (download.tensorflow.org)... 173.194.215.207, 173.194.216.207, 173.194.218.207, ...
Connecting to download.tensorflow.org (download.tensorflow.org)|173.194.215.207|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 381355771 (364M) [application/x-tar]
Saving to: ‘./pretrained/faster_rcnn_resnet50_coco_2018_01_28.tar.gz’

./pretrained/faster 100%[===================>] 363.69M  47.4MB/s    in 3.8s    

2023-11-22 06:21:19 (95.0 MB/s) - ‘./pretrained/faster_rcnn_resnet50_coco_2018_01_28.tar.gz’ saved [381355771/381355771]

--2023-11-22 06:21:19--  https://raw.githubusercontent.com/opencv/opencv_extra/master/testdata/dnn/faster_rcnn_resnet50_coco_2018_01_28.pbtxt
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.108.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 90893 (89K) [text/plain]
Saving to: ‘./pretrained/config_graph.pbtxt’

./pretrained/config 100%[===================>]  88.76K  --.-KB/s    in 0.03s   

2023-11-22 06:21:19 (3.22 MB/s) - ‘./pretrained/config_graph.pbtxt’ saved [90893/90893]

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!tar -xvf ./pretrained/faster*.tar.gz -C ./pretrained

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faster_rcnn_resnet50_coco_2018_01_28/
faster_rcnn_resnet50_coco_2018_01_28/model.ckpt.index
faster_rcnn_resnet50_coco_2018_01_28/checkpoint
faster_rcnn_resnet50_coco_2018_01_28/pipeline.config
faster_rcnn_resnet50_coco_2018_01_28/model.ckpt.data-00000-of-00001
faster_rcnn_resnet50_coco_2018_01_28/model.ckpt.meta
faster_rcnn_resnet50_coco_2018_01_28/saved_model/
faster_rcnn_resnet50_coco_2018_01_28/saved_model/saved_model.pb
faster_rcnn_resnet50_coco_2018_01_28/saved_model/variables/
faster_rcnn_resnet50_coco_2018_01_28/frozen_inference_graph.pb

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!pwd
!ls -lia ./pretrained/faster_rcnn_resnet50_coco_2018_01_28

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/content
total 296076
4980851 drwxr-xr-x 3 345018 5000      4096 Feb  1  2018 .
4980848 drwxr-xr-x 3 root   root      4096 Nov 22 06:21 ..
4980853 -rw-r--r-- 1 345018 5000        77 Feb  1  2018 checkpoint
4980860 -rw-r--r-- 1 345018 5000 120549957 Feb  1  2018 frozen_inference_graph.pb
4980855 -rw-r--r-- 1 345018 5000 176914228 Feb  1  2018 model.ckpt.data-00000-of-00001
4980852 -rw-r--r-- 1 345018 5000     14460 Feb  1  2018 model.ckpt.index
4980856 -rw-r--r-- 1 345018 5000   5675175 Feb  1  2018 model.ckpt.meta
4980854 -rw-r--r-- 1 345018 5000      3240 Feb  1  2018 pipeline.config
4980857 drwxr-xr-x 3 345018 5000      4096 Feb  1  2018 saved_model

dnn에서 readNetFromTensorflow()로 tensorflow inference 모델을 로딩

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cv_net = cv2.dnn.readNetFromTensorflow('./pretrained/faster_rcnn_resnet50_coco_2018_01_28/frozen_inference_graph.pb',
                                     './pretrained/config_graph.pbtxt')

coco 데이터 세트의 클래스id별 클래스명 지정.

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# OpenCV Yolo용
labels_to_names_seq = {0:'person',1:'bicycle',2:'car',3:'motorbike',4:'aeroplane',5:'bus',6:'train',7:'truck',8:'boat',9:'traffic light',10:'fire hydrant',
                        11:'stop sign',12:'parking meter',13:'bench',14:'bird',15:'cat',16:'dog',17:'horse',18:'sheep',19:'cow',20:'elephant',
                        21:'bear',22:'zebra',23:'giraffe',24:'backpack',25:'umbrella',26:'handbag',27:'tie',28:'suitcase',29:'frisbee',30:'skis',
                        31:'snowboard',32:'sports ball',33:'kite',34:'baseball bat',35:'baseball glove',36:'skateboard',37:'surfboard',38:'tennis racket',39:'bottle',40:'wine glass',
                        41:'cup',42:'fork',43:'knife',44:'spoon',45:'bowl',46:'banana',47:'apple',48:'sandwich',49:'orange',50:'broccoli',
                        51:'carrot',52:'hot dog',53:'pizza',54:'donut',55:'cake',56:'chair',57:'sofa',58:'pottedplant',59:'bed',60:'diningtable',
                        61:'toilet',62:'tvmonitor',63:'laptop',64:'mouse',65:'remote',66:'keyboard',67:'cell phone',68:'microwave',69:'oven',70:'toaster',
                        71:'sink',72:'refrigerator',73:'book',74:'clock',75:'vase',76:'scissors',77:'teddy bear',78:'hair drier',79:'toothbrush' }

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# OpenCV Tensorflow Faster-RCNN용
labels_to_names_0 = {0:'person',1:'bicycle',2:'car',3:'motorcycle',4:'airplane',5:'bus',6:'train',7:'truck',8:'boat',9:'traffic light',
                    10:'fire hydrant',11:'street sign',12:'stop sign',13:'parking meter',14:'bench',15:'bird',16:'cat',17:'dog',18:'horse',19:'sheep',
                    20:'cow',21:'elephant',22:'bear',23:'zebra',24:'giraffe',25:'hat',26:'backpack',27:'umbrella',28:'shoe',29:'eye glasses',
                    30:'handbag',31:'tie',32:'suitcase',33:'frisbee',34:'skis',35:'snowboard',36:'sports ball',37:'kite',38:'baseball bat',39:'baseball glove',
                    40:'skateboard',41:'surfboard',42:'tennis racket',43:'bottle',44:'plate',45:'wine glass',46:'cup',47:'fork',48:'knife',49:'spoon',
                    50:'bowl',51:'banana',52:'apple',53:'sandwich',54:'orange',55:'broccoli',56:'carrot',57:'hot dog',58:'pizza',59:'donut',
                    60:'cake',61:'chair',62:'couch',63:'potted plant',64:'bed',65:'mirror',66:'dining table',67:'window',68:'desk',69:'toilet',
                    70:'door',71:'tv',72:'laptop',73:'mouse',74:'remote',75:'keyboard',76:'cell phone',77:'microwave',78:'oven',79:'toaster',
                    80:'sink',81:'refrigerator',82:'blender',83:'book',84:'clock',85:'vase',86:'scissors',87:'teddy bear',88:'hair drier',89:'toothbrush',
                    90:'hair brush'}

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labels_to_names = {1:'person',2:'bicycle',3:'car',4:'motorcycle',5:'airplane',6:'bus',7:'train',8:'truck',9:'boat',10:'traffic light',
                    11:'fire hydrant',12:'street sign',13:'stop sign',14:'parking meter',15:'bench',16:'bird',17:'cat',18:'dog',19:'horse',20:'sheep',
                    21:'cow',22:'elephant',23:'bear',24:'zebra',25:'giraffe',26:'hat',27:'backpack',28:'umbrella',29:'shoe',30:'eye glasses',
                    31:'handbag',32:'tie',33:'suitcase',34:'frisbee',35:'skis',36:'snowboard',37:'sports ball',38:'kite',39:'baseball bat',40:'baseball glove',
                    41:'skateboard',42:'surfboard',43:'tennis racket',44:'bottle',45:'plate',46:'wine glass',47:'cup',48:'fork',49:'knife',50:'spoon',
                    51:'bowl',52:'banana',53:'apple',54:'sandwich',55:'orange',56:'broccoli',57:'carrot',58:'hot dog',59:'pizza',60:'donut',
                    61:'cake',62:'chair',63:'couch',64:'potted plant',65:'bed',66:'mirror',67:'dining table',68:'window',69:'desk',70:'toilet',
                    71:'door',72:'tv',73:'laptop',74:'mouse',75:'remote',76:'keyboard',77:'cell phone',78:'microwave',79:'oven',80:'toaster',
                    81:'sink',82:'refrigerator',83:'blender',84:'book',85:'clock',86:'vase',87:'scissors',88:'teddy bear',89:'hair drier',90:'toothbrush',
                    91:'hair brush'}

이미지를 preprocessing 수행하여 Network에 입력하고 Object Detection 수행 후 결과를 이미지에 시각화

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img.shape

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(633, 806, 3)

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# 원본 이미지가 Faster RCNN기반 네트웍으로 입력 시 resize됨.
# scaling된 이미지 기반으로 bounding box 위치가 예측 되므로 이를 다시 원복하기 위해 원본 이미지 shape정보 필요
rows = img.shape[0]
cols = img.shape[1]
# cv2의 rectangle()은 인자로 들어온 이미지 배열에 직접 사각형을 업데이트 하므로 그림 표현을 위한 별도의 이미지 배열 생성.
draw_img = img.copy()

# 원본 이미지 배열 BGR을 RGB로 변환하여 배열 입력. Tensorflow Faster RCNN은 마지막 classification layer가 Dense가 아니여서 size를 고정할 필요는 없음.
cv_net.setInput(cv2.dnn.blobFromImage(img, swapRB=True, crop=False))

# Object Detection 수행하여 결과를 cvOut으로 반환
cv_out = cv_net.forward()
print(cv_out.shape)

# bounding box의 테두리와 caption 글자색 지정
green_color=(0, 255, 0)
red_color=(0, 0, 255)

# detected 된 object들을 iteration 하면서 정보 추출
for detection in cv_out[0,0,:,:]:
    score = float(detection[2])
    class_id = int(detection[1])
    # detected된 object들의 score가 0.5 이상만 추출
    if score > 0.5:
        # detected된 object들은 scale된 기준으로 예측되었으므로 다시 원본 이미지 비율로 계산
        left = detection[3] * cols
        top = detection[4] * rows
        right = detection[5] * cols
        bottom = detection[6] * rows
        # labels_to_names_seq 딕셔너리로 class_id값을 클래스명으로 변경.
        caption = "{}: {:.4f}".format(labels_to_names_0[class_id], score)
        print(caption)
        #cv2.rectangle()은 인자로 들어온 draw_img에 사각형을 그림. 위치 인자는 반드시 정수형.
        cv2.rectangle(draw_img, (int(left), int(top)), (int(right), int(bottom)), color=green_color, thickness=2)
        cv2.putText(draw_img, caption, (int(left), int(top - 5)), cv2.FONT_HERSHEY_SIMPLEX, 0.4, red_color, 1)

img_rgb = cv2.cvtColor(draw_img, cv2.COLOR_BGR2RGB)

plt.figure(figsize=(12, 12))
plt.imshow(img_rgb)

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(1, 1, 100, 7)
person: 0.9998
person: 0.9996
person: 0.9993
person: 0.9970
person: 0.8995
car: 0.8922
car: 0.7602
car: 0.7415
car: 0.6929
car: 0.6918
car: 0.6896
car: 0.6717
car: 0.6521
car: 0.5730
car: 0.5679
car: 0.5261
car: 0.5012





<matplotlib.image.AxesImage at 0x7ec0aae1f130>


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cv_out

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          6.23704851e-01, 3.60841870e-01, 7.15491116e-01,
          4.64615941e-01],
         [0.00000000e+00, 3.00000000e+01, 2.07235990e-03,
          7.61325955e-01, 4.97121066e-01, 8.57526898e-01,
          7.35164285e-01],
         [0.00000000e+00, 0.00000000e+00, 1.92974054e-03,
          5.69738805e-01, 3.60520869e-01, 5.87458193e-01,
          4.02149826e-01],
         [0.00000000e+00, 6.00000000e+00, 1.89443154e-03,
          6.19646788e-01, 3.58304083e-01, 7.22060442e-01,
          4.69268739e-01],
         [0.00000000e+00, 7.00000000e+00, 1.81595772e-03,
          2.94367850e-01, 3.67994905e-01, 9.78064239e-01,
          9.30691838e-01],
         [0.00000000e+00, 2.00000000e+00, 1.69707532e-03,
          3.67421329e-01, 3.52760375e-01, 3.88726890e-01,
          4.00401950e-01],
         [0.00000000e+00, 5.00000000e+00, 1.67935912e-03,
          1.83898658e-01, 3.96509379e-01, 3.14183682e-01,
          5.44656634e-01],
         [0.00000000e+00, 3.00000000e+01, 1.63409393e-03,
          7.44515300e-01, 5.09966493e-01, 8.59870315e-01,
          8.15174937e-01],
         [0.00000000e+00, 1.00000000e+00, 1.61926937e-03,
          1.97852850e-02, 6.16704047e-01, 2.86476463e-01,
          9.02087033e-01],
         [0.00000000e+00, 2.60000000e+01, 1.54944754e-03,
          7.88556397e-01, 3.88741016e-01, 8.25305283e-01,
          4.40299392e-01],
         [0.00000000e+00, 8.60000000e+01, 1.52406318e-03,
          6.01132929e-01, 7.01487243e-01, 7.45222032e-01,
          8.93119633e-01],
         [0.00000000e+00, 4.10000000e+01, 1.51528139e-03,
          1.27276778e-03, 5.96581340e-01, 2.87025869e-01,
          8.85573030e-01],
         [0.00000000e+00, 3.40000000e+01, 1.39231631e-03,
          1.54019147e-02, 5.85525215e-01, 2.69098401e-01,
          8.94322455e-01],
         [0.00000000e+00, 6.10000000e+01, 1.37677998e-03,
          7.26888597e-01, 4.86116469e-01, 8.74394596e-01,
          8.61851394e-01],
         [0.00000000e+00, 6.00000000e+00, 1.32521265e-03,
          2.50652373e-01, 3.20080727e-01, 9.70555842e-01,
          9.49614763e-01],
         [0.00000000e+00, 3.10000000e+01, 1.31424330e-03,
          5.97983420e-01, 7.21269786e-01, 7.30159581e-01,
          8.85436714e-01],
         [0.00000000e+00, 2.00000000e+01, 1.28290569e-03,
          7.85527527e-01, 3.84867609e-01, 8.27534974e-01,
          4.46104646e-01],
         [0.00000000e+00, 4.10000000e+01, 1.23773608e-03,
          5.96185803e-01, 7.16784298e-01, 7.41377950e-01,
          8.89733970e-01],
         [0.00000000e+00, 0.00000000e+00, 1.18559075e-03,
          1.10947818e-01, 4.59801197e-01, 8.95728469e-01,
          9.50373650e-01],
         [0.00000000e+00, 3.00000000e+00, 1.17662584e-03,
          4.76408273e-01, 3.60545278e-01, 4.97020155e-01,
          3.82405043e-01]]]], dtype=float32)

단일 이미지의 object detection을 함수로 생성

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import time

def get_detected_img(cv_net, img_array, score_threshold, use_copied_array=True, is_print=True):

    rows = img_array.shape[0]
    cols = img_array.shape[1]

    draw_img = None
    if use_copied_array:
        draw_img = img_array.copy()
    else:
        draw_img = img_array

    cv_net.setInput(cv2.dnn.blobFromImage(img_array, swapRB=True, crop=False))

    start = time.time()
    cv_out = cv_net.forward()

    green_color=(0, 255, 0)
    red_color=(0, 0, 255)

    # detected 된 object들을 iteration 하면서 정보 추출
    for detection in cv_out[0,0,:,:]:
        score = float(detection[2])
        class_id = int(detection[1])
        # detected된 object들의 score가 함수 인자로 들어온 score_threshold 이상만 추출
        if score > score_threshold:
            # detected된 object들은 scale된 기준으로 예측되었으므로 다시 원본 이미지 비율로 계산
            left = detection[3] * cols
            top = detection[4] * rows
            right = detection[5] * cols
            bottom = detection[6] * rows
            # labels_to_names 딕셔너리로 class_id값을 클래스명으로 변경. opencv에서는 class_id + 1로 매핑해야함.
            caption = "{}: {:.4f}".format(labels_to_names_0[class_id], score)
            print(caption)
            #cv2.rectangle()은 인자로 들어온 draw_img에 사각형을 그림. 위치 인자는 반드시 정수형.
            cv2.rectangle(draw_img, (int(left), int(top)), (int(right), int(bottom)), color=green_color, thickness=2)
            cv2.putText(draw_img, caption, (int(left), int(top - 5)), cv2.FONT_HERSHEY_SIMPLEX, 0.4, red_color, 1)
    if is_print:
        print('Detection 수행시간:',round(time.time() - start, 2),"초")

    return draw_img

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# image 로드
img = cv2.imread('./data/beatles01.jpg')
print('image shape:', img.shape)

# tensorflow inference 모델 로딩
cv_net = cv2.dnn.readNetFromTensorflow('./pretrained/faster_rcnn_resnet50_coco_2018_01_28/frozen_inference_graph.pb',
                                     './pretrained/config_graph.pbtxt')
# Object Detetion 수행 후 시각화
draw_img = get_detected_img(cv_net, img, score_threshold=0.5, use_copied_array=True, is_print=True)

img_rgb = cv2.cvtColor(draw_img, cv2.COLOR_BGR2RGB)

plt.figure(figsize=(12, 12))
plt.imshow(img_rgb)

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image shape: (633, 806, 3)
person: 0.9998
person: 0.9996
person: 0.9993
person: 0.9970
person: 0.8995
car: 0.8922
car: 0.7602
car: 0.7415
car: 0.6929
car: 0.6918
car: 0.6896
car: 0.6717
car: 0.6521
car: 0.5730
car: 0.5679
car: 0.5261
car: 0.5012
Detection 수행시간: 4.93 초





<matplotlib.image.AxesImage at 0x7ec0aae95000>


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# 다른 image 테스트
!wget -O ./data/baseball01.jpg https://raw.githubusercontent.com/chulminkw/DLCV/master/data/image/baseball01.jpg

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--2023-11-22 06:21:38--  https://raw.githubusercontent.com/chulminkw/DLCV/master/data/image/baseball01.jpg
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.108.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 76279 (74K) [image/jpeg]
Saving to: ‘./data/baseball01.jpg’

./data/baseball01.j 100%[===================>]  74.49K  --.-KB/s    in 0.02s   

2023-11-22 06:21:39 (2.98 MB/s) - ‘./data/baseball01.jpg’ saved [76279/76279]

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img = cv2.imread('./data/baseball01.jpg')
print('image shape:', img.shape)

# tensorflow inference 모델 로딩
cv_net = cv2.dnn.readNetFromTensorflow('./pretrained/faster_rcnn_resnet50_coco_2018_01_28/frozen_inference_graph.pb',
                                     './pretrained/config_graph.pbtxt')
# Object Detetion 수행 후 시각화
draw_img = get_detected_img(cv_net, img, score_threshold=0.5, use_copied_array=True, is_print=True)

img_rgb = cv2.cvtColor(draw_img, cv2.COLOR_BGR2RGB)

plt.figure(figsize=(12, 12))
plt.imshow(img_rgb)

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image shape: (476, 735, 3)
person: 0.9998
person: 0.9997
person: 0.9977
baseball glove: 0.9815
sports ball: 0.8867
baseball bat: 0.8420
Detection 수행시간: 4.8 초





<matplotlib.image.AxesImage at 0x7ec0aad40c10>

Video Object Detection 수행

원본 영상 보기

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!wget -O ./data/Jonh_Wick_small.mp4 https://github.com/chulminkw/DLCV/blob/master/data/video/John_Wick_small.mp4?raw=true

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--2023-11-22 06:21:45--  https://github.com/chulminkw/DLCV/blob/master/data/video/John_Wick_small.mp4?raw=true
Resolving github.com (github.com)... 140.82.114.4
Connecting to github.com (github.com)|140.82.114.4|:443... connected.
HTTP request sent, awaiting response... 302 Found
Location: https://github.com/chulminkw/DLCV/raw/master/data/video/John_Wick_small.mp4 [following]
--2023-11-22 06:21:45--  https://github.com/chulminkw/DLCV/raw/master/data/video/John_Wick_small.mp4
Reusing existing connection to github.com:443.
HTTP request sent, awaiting response... 302 Found
Location: https://raw.githubusercontent.com/chulminkw/DLCV/master/data/video/John_Wick_small.mp4 [following]
--2023-11-22 06:21:45--  https://raw.githubusercontent.com/chulminkw/DLCV/master/data/video/John_Wick_small.mp4
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.108.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 1794863 (1.7M) [application/octet-stream]
Saving to: ‘./data/Jonh_Wick_small.mp4’

./data/Jonh_Wick_sm 100%[===================>]   1.71M  --.-KB/s    in 0.08s   

2023-11-22 06:21:46 (22.3 MB/s) - ‘./data/Jonh_Wick_small.mp4’ saved [1794863/1794863]

VideoCapture와 VideoWriter 설정하기

  • VideoCapture를 이용하여 Video를 frame별로 capture 할 수 있도록 설정
  • VideoCapture의 속성을 이용하여 Video Frame의 크기 및 FPS 설정.
  • VideoWriter를 위한 인코딩 코덱 설정 및 영상 write를 위한 설정
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video_input_path = '/content/data/Jonh_Wick_small.mp4'

cap = cv2.VideoCapture(video_input_path)
frame_cnt = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
print('총 Frame 갯수:', frame_cnt)

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총 Frame 갯수: 58

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video_input_path = '/content/data/Jonh_Wick_small.mp4'
video_output_path = './data/John_Wick_small_cv01.mp4'

cap = cv2.VideoCapture(video_input_path)

codec = cv2.VideoWriter_fourcc(*'XVID')

vid_size = (round(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),round(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
vid_fps = cap.get(cv2.CAP_PROP_FPS )

vid_writer = cv2.VideoWriter(video_output_path, codec, vid_fps, vid_size)

frame_cnt = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
print('총 Frame 갯수:', frame_cnt)

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총 Frame 갯수: 58
총 Frame 별로 iteration 하면서 Object Detection 수행. 개별 frame별로 단일 이미지 Object Detection과 유사
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# bounding box의 테두리와 caption 글자색 지정
green_color=(0, 255, 0)
red_color=(0, 0, 255)

while True:

    hasFrame, img_frame = cap.read()
    if not hasFrame:
        print('더 이상 처리할 frame이 없습니다.')
        break

    rows = img_frame.shape[0]
    cols = img_frame.shape[1]
    # 원본 이미지 배열 BGR을 RGB로 변환하여 배열 입력
    cv_net.setInput(cv2.dnn.blobFromImage(img_frame,  swapRB=True, crop=False))

    start= time.time()
    # Object Detection 수행하여 결과를 cv_out으로 반환
    cv_out = cv_net.forward()
    frame_index = 0
    # detected 된 object들을 iteration 하면서 정보 추출
    for detection in cv_out[0,0,:,:]:
        score = float(detection[2])
        class_id = int(detection[1])
        # detected된 object들의 score가 0.5 이상만 추출
        if score > 0.5:
            # detected된 object들은 scale된 기준으로 예측되었으므로 다시 원본 이미지 비율로 계산
            left = detection[3] * cols
            top = detection[4] * rows
            right = detection[5] * cols
            bottom = detection[6] * rows
            # labels_to_names_0딕셔너리로 class_id값을 클래스명으로 변경.
            caption = "{}: {:.4f}".format(labels_to_names_0[class_id], score)
            #print(class_id, caption)
            #cv2.rectangle()은 인자로 들어온 draw_img에 사각형을 그림. 위치 인자는 반드시 정수형.
            cv2.rectangle(img_frame, (int(left), int(top)), (int(right), int(bottom)), color=green_color, thickness=2)
            cv2.putText(img_frame, caption, (int(left), int(top - 5)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, red_color, 1)
    print('Detection 수행 시간:', round(time.time()-start, 2),'초')
    vid_writer.write(img_frame)
# end of while loop

vid_writer.release()
cap.release()

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Detection 수행 시간: 5.02 초
Detection 수행 시간: 4.59 초
Detection 수행 시간: 5.85 초
Detection 수행 시간: 4.67 초
Detection 수행 시간: 4.53 초
Detection 수행 시간: 6.21 초
Detection 수행 시간: 5.82 초
Detection 수행 시간: 4.55 초
Detection 수행 시간: 5.97 초
Detection 수행 시간: 4.52 초
Detection 수행 시간: 4.46 초
Detection 수행 시간: 6.01 초
Detection 수행 시간: 4.43 초
Detection 수행 시간: 4.48 초
Detection 수행 시간: 5.88 초
Detection 수행 시간: 4.47 초
Detection 수행 시간: 4.45 초
Detection 수행 시간: 5.91 초
Detection 수행 시간: 4.51 초
Detection 수행 시간: 4.54 초
Detection 수행 시간: 5.83 초
Detection 수행 시간: 4.48 초
Detection 수행 시간: 4.83 초
Detection 수행 시간: 5.64 초
Detection 수행 시간: 4.56 초
Detection 수행 시간: 5.01 초
Detection 수행 시간: 5.41 초
Detection 수행 시간: 4.49 초
Detection 수행 시간: 5.31 초
Detection 수행 시간: 5.32 초
Detection 수행 시간: 4.62 초
Detection 수행 시간: 5.49 초
Detection 수행 시간: 5.12 초
Detection 수행 시간: 4.43 초
Detection 수행 시간: 5.4 초
Detection 수행 시간: 4.99 초
Detection 수행 시간: 4.49 초
Detection 수행 시간: 5.63 초
Detection 수행 시간: 4.77 초
Detection 수행 시간: 4.45 초
Detection 수행 시간: 5.71 초
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Detection 수행 시간: 4.5 초
Detection 수행 시간: 5.89 초
Detection 수행 시간: 4.58 초
Detection 수행 시간: 4.5 초
Detection 수행 시간: 6.01 초
Detection 수행 시간: 4.56 초
Detection 수행 시간: 4.48 초
Detection 수행 시간: 5.97 초
Detection 수행 시간: 4.46 초
Detection 수행 시간: 4.5 초
Detection 수행 시간: 5.86 초
Detection 수행 시간: 4.47 초
Detection 수행 시간: 4.48 초
Detection 수행 시간: 5.93 초
Detection 수행 시간: 4.45 초
Detection 수행 시간: 4.56 초
더 이상 처리할 frame이 없습니다.

video detection 전용 함수 생성.

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def do_detected_video(cv_net, input_path, output_path, score_threshold, is_print):

    cap = cv2.VideoCapture(input_path)

    codec = cv2.VideoWriter_fourcc(*'XVID')

    vid_size = (round(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),round(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
    vid_fps = cap.get(cv2.CAP_PROP_FPS)

    vid_writer = cv2.VideoWriter(output_path, codec, vid_fps, vid_size)

    frame_cnt = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    print('총 Frame 갯수:', frame_cnt)

    green_color=(0, 255, 0)
    red_color=(0, 0, 255)
    while True:
        hasFrame, img_frame = cap.read()
        if not hasFrame:
            print('더 이상 처리할 frame이 없습니다.')
            break

        img_frame = get_detected_img(cv_net, img_frame, score_threshold=score_threshold, use_copied_array=False, is_print=is_print)

        vid_writer.write(img_frame)
    # end of while loop

    vid_writer.release()
    cap.release()

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do_detected_video(cv_net, '/content/data/Jonh_Wick_small.mp4', './data/John_Wick_small_02.mp4', 0.2, False)

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총 Frame 갯수: 58
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person: 0.9632
horse: 0.9015
car: 0.8703
car: 0.7212
person: 0.6859
person: 0.6433
suitcase: 0.3733
car: 0.3009
person: 0.2986
tie: 0.2826
person: 0.2655
person: 0.2293
motorcycle: 0.2256
car: 0.9974
person: 0.9900
person: 0.9703
car: 0.9473
car: 0.9213
horse: 0.9101
tie: 0.8922
person: 0.8296
car: 0.8060
car: 0.6353
person: 0.6110
suitcase: 0.5954
car: 0.3395
car: 0.2918
person: 0.2778
motorcycle: 0.2572
car: 0.9979
person: 0.9566
car: 0.9483
car: 0.9346
person: 0.9068
car: 0.8925
horse: 0.8732
tie: 0.7236
person: 0.7002
person: 0.6212
car: 0.5415
car: 0.3162
person: 0.2832
person: 0.2831
motorcycle: 0.2528
person: 0.2501
person: 0.2033
car: 0.9982
person: 0.9635
car: 0.9531
person: 0.9457
horse: 0.9248
car: 0.8991
car: 0.8901
person: 0.7725
person: 0.6168
tie: 0.4165
person: 0.3911
car: 0.3438
person: 0.3160
person: 0.2786
motorcycle: 0.2582
car: 0.2200
person: 0.2133
car: 0.9983
person: 0.9781
car: 0.9625
horse: 0.9336
person: 0.9185
tie: 0.8509
person: 0.7062
car: 0.5896
car: 0.5503
car: 0.5202
car: 0.4539
person: 0.3827
person: 0.3132
bicycle: 0.2274
car: 0.2052
car: 0.9954
car: 0.9928
person: 0.9834
person: 0.9706
tie: 0.7990
horse: 0.7551
car: 0.7481
person: 0.5980
car: 0.2928
person: 0.2792
person: 0.2757
car: 0.2199
backpack: 0.2020
car: 0.9960
car: 0.9896
person: 0.9528
person: 0.9082
horse: 0.8085
car: 0.7858
person: 0.7171
car: 0.7106
car: 0.5665
car: 0.4249
car: 0.4122
person: 0.3954
tie: 0.3411
person: 0.2653
더 이상 처리할 frame이 없습니다.

1

  SeSAC, Computer-Vision
 This post is licensed under  CC BY 4.0  by the author.
Contents
 
Azure ML Classic
 
딥러닝 영상처리4