OpenCV Python 강좌 - 동영상의 배경 제거하고 움직이는 물체 검출 하기 ( Background Subtraction )

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