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동영상의 배경을 제거하고 움직이는 물체를 검출하는 데 사용할 수 있는 Background Subtraction 예제 입니다.

OpenCV에서 제공하는 Background Subtraction 알고리즘 중 하나인 createBackgroundSubtractorKNN를 사용하여 테스트를 진행해봤습니다. 



2018. 9. 22 최초작성

2023. 12. 10 최종작성 - 박스를 하나로 묶어주는 코드 추가



OpenCV Python - Background Subtraction 테스트 영상

https://youtu.be/_nUCgbOCUPw

 




import cv2
import numpy as np
import os, time


def get_points(box):
    x, y, w, h = box
    return [(x, y), (x + w, y), (x, y + h), (x + w, y + h), ((2*x + w)/2, y), (x, (2*y + h)/2), ((2*x + w)/2, y + h), (x + w, (2*y + h)/2)]

def combine_boxes(bounding_boxes, groups):
    combined_boxes = []
    for group in groups:
        min_x = min([box[0] for box in group])
        min_y = min([box[1] for box in group])
        max_x = max([box[0] + box[2] for box in group])
        max_y = max([box[1] + box[3] for box in group])
        combined_boxes.append((min_x, min_y, max_x - min_x, max_y - min_y))
    return combined_boxes

def is_adjacent_or_overlap(box1, box2):
    x1, y1, w1, h1 = box1
    x2, y2, w2, h2 = box2
    if x1 <= x2 + w2 and x1 + w1 >= x2 and y1 <= y2 + h2 and y1 + h1 >= y2:
        return True
    elif x2 <= x1 + w1 and x2 + w2 >= x1 and y2 <= y1 + h1 and y2 + h2 >= y1:
        return True
    return False

def remove_overlaps_or_adjacent(bounding_boxes):
    removed_boxes = bounding_boxes.copy()
    for box1 in bounding_boxes:
        for box2 in bounding_boxes:
            if box1 != box2 and is_adjacent_or_overlap(box1, box2):
                if (box1[2] * box1[3]) < (box2[2] * box2[3]):
                    if box1 in removed_boxes:
                        removed_boxes.remove(box1)
                else:
                    if box2 in removed_boxes:
                        removed_boxes.remove(box2)
    return removed_boxes

def group_boxes(bounding_boxes, eps):
    groups = []
    grouped = set() # to keep track of boxes already grouped
    for box in bounding_boxes:
        if box not in grouped: # ensure the box is not already grouped
            added = False
            for group in groups:
                for existing_box in group:
                    if np.any([np.linalg.norm(np.array(point1)-np.array(point2)) <= eps
                            for point1 in get_points(box) for point2 in get_points(existing_box)]):
                        group.append(box)
                        grouped.add(box) # mark the box as grouped
                        added = True
                        break
                if added:
                    break
            if not added:
                groups.append([box])
                grouped.add(box) # mark the box as grouped
    return groups

def process_boxes(bounding_boxes, eps):
    groups = group_boxes(bounding_boxes, eps)
    combined_boxes = combine_boxes(bounding_boxes, groups)
    return remove_overlaps_or_adjacent(combined_boxes)



# 테스트 동영상 출처 - https://github.com/intel-iot-devkit/sample-videos/blob/master/people-detection.mp4
cap = cv2.VideoCapture("people-detection.mp4")


# 웹캠에서 fps 값 획득
fps = cap.get(cv2.CAP_PROP_FPS)
print('fps', fps)

if fps == 0.0:
    fps = 30.0

time_per_frame_video = 1/fps
last_time = time.perf_counter()


width = 1920#int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) *2
height = 1080#int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))

fourcc = cv2.VideoWriter_fourcc(*'XVID')
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
writer = cv2.VideoWriter(BASE_DIR + '/' + 'result.avi', fourcc, fps, (width, height))


# 옵션 설명 https://stackoverflow.com/a/43636256
fgbg = cv2.createBackgroundSubtractorKNN(history=5, dist2Threshold=400, detectShadows=0)

fhd = np.zeros((1080,1920,3), dtype=np.uint8)

while(1):
    ret, frame = cap.read()

    if ret==False:
        break

    # width = frame.shape[1]
    # height = frame.shape[0]


    fgmask = fgbg.apply(frame)



    nlabels, labels, stats, centroids = cv2.connectedComponentsWithStats(fgmask)


    bbox_list = []

    for index, centroid in enumerate(centroids):
        if stats[index][0] == 0 and stats[index][1] == 0:
            continue
        if np.any(np.isnan(centroid)):
            continue


        x, y, w, h, area = stats[index]
        centerX, centerY = int(centroid[0]), int(centroid[1])

        if area > 100:
            # cv2.circle(frame, (centerX, centerY), 1, (0, 255, 0), 2)
            # cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255))

            bbox_list.append((x,y,w,h))

    # 그룹 생성
    processed_boxes = process_boxes(bbox_list, 50)

    for i, box in enumerate(processed_boxes):
        x,y,w,h = box
        cv2.putText(frame, str(i), (x,y), cv2.FONT_HERSHEY_SIMPLEX, 2, (0,255,0), 2 )
        cv2.rectangle(frame, box, (255, 0, 0), 2)

    fgmask = cv2.cvtColor(fgmask, cv2.COLOR_GRAY2BGR)
    result = cv2.hconcat([fgmask, frame])

    h,w,_=result.shape

    center_y=(height-h)//2
    center_x=(width-w)//2
    fhd[center_y:center_y+h, center_x:center_x+w] = result

    cv2.imshow('result',result)
    writer.write(fhd)

    k = cv2.waitKey(30) & 0xff
    if k == 27:
        break

cap.release()
writer.release()
cv2.destroyAllWindows()



참고

 

https://docs.opencv.org/3.4.3/db/d5c/tutorial_py_bg_subtraction.html











문제 발생시 지나치지 마시고 댓글 남겨주시면 가능한 빨리 답장드립니다.


제가 쓴 책도 한번 검토해보세요 ^^

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