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| import numpy as np | |
| import cv2 | |
| import sys | |
| TEXT_COLOR = (0, 255, 0) | |
| TRACKER_COLOR = (255, 0, 0) | |
| FONT = cv2.FONT_HERSHEY_SIMPLEX | |
| VIDEO_SOURCE = "videos/Animal_1.mp4" | |
| BGS_TYPES = ["GMG", "MOG", "MOG2", "KNN", "CNT"] | |
| BGS_TYPE = BGS_TYPES[2] | |
| def getKernel(KERNEL_TYPE): | |
| if KERNEL_TYPE == "dilation": | |
| kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)) | |
| if KERNEL_TYPE == "opening": | |
| kernel = np.ones((3, 3), np.uint8) | |
| if KERNEL_TYPE == "closing": | |
| kernel = np.ones((3, 3), np.uint8) | |
| return kernel | |
| def getFilter(img, filter): | |
| if filter == 'closing': | |
| return cv2.morphologyEx(img, cv2.MORPH_CLOSE, getKernel("closing"), iterations=2) | |
| if filter == 'opening': | |
| return cv2.morphologyEx(img, cv2.MORPH_OPEN, getKernel("opening"), iterations=2) | |
| if filter == 'dilation': | |
| return cv2.dilate(img, getKernel("dilation"), iterations=2) | |
| if filter == 'combine': | |
| closing = cv2.morphologyEx(img, cv2.MORPH_CLOSE, getKernel("closing"), iterations=2) | |
| opening = cv2.morphologyEx(closing, cv2.MORPH_OPEN, getKernel("opening"), iterations=2) | |
| dilation = cv2.dilate(opening, getKernel("dilation"), iterations=2) | |
| return dilation | |
| def getBGSubtractor(BGS_TYPE): | |
| if BGS_TYPE == "GMG": | |
| return cv2.bgsegm.createBackgroundSubtractorGMG() | |
| if BGS_TYPE == "MOG": | |
| return cv2.bgsegm.createBackgroundSubtractorMOG() | |
| if BGS_TYPE == "MOG2": | |
| return cv2.createBackgroundSubtractorMOG2() | |
| if BGS_TYPE == "KNN": | |
| return cv2.createBackgroundSubtractorKNN() | |
| if BGS_TYPE == "CNT": | |
| return cv2.bgsegm.createBackgroundSubtractorCNT() | |
| print("Invalid detector") | |
| sys.exit(1) | |
| cap = cv2.VideoCapture(VIDEO_SOURCE) | |
| bg_subtractor = getBGSubtractor(BGS_TYPE) | |
| minArea = 250 | |
| def main(): | |
| while (cap.isOpened): | |
| ok, frame = cap.read() | |
| if not ok: | |
| print("Finished processing the video") | |
| break | |
| frame = cv2.resize(frame, (0, 0), fx=0.50, fy=0.50) | |
| bg_mask = bg_subtractor.apply(frame) | |
| bg_mask = getFilter(bg_mask, 'combine') | |
| bg_mask = cv2.medianBlur(bg_mask, 5) | |
| (contours, hierarchy) = cv2.findContours(bg_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) | |
| #print(contours) | |
| for cnt in contours: | |
| area = cv2.contourArea(cnt) | |
| if area >= minArea: | |
| x, y, w, h = cv2.boundingRect(cnt) | |
| cv2.rectangle(frame, (10,30), (250,55), (255,0,0), -1) | |
| cv2.putText(frame, 'Motion detected!', (10,50), FONT, 0.8, TEXT_COLOR, 2, cv2.LINE_AA) | |
| #cv2.drawContours(frame, cnt, -1, TRACKER_COLOR, 3) | |
| #cv2.drawContours(frame, cnt, -1, (255,255,255), 1) | |
| #cv2.rectangle(frame, (x,y), (x+w, y+h), TRACKER_COLOR, 3) | |
| #cv2.rectangle(frame, (x, y), (x + w, y + h), (255,255,255), 1) | |
| # https://www.pyimagesearch.com/2016/03/07/transparent-overlays-with-opencv/ | |
| for alpha in np.arange(0.8, 1.1, 0.9)[::-1]: | |
| frame_copy = frame.copy() | |
| output = frame.copy() | |
| cv2.drawContours(frame_copy, [cnt], -1, TRACKER_COLOR, -1) | |
| frame = cv2.addWeighted(frame_copy, alpha, output, 1 - alpha, 0, output) | |
| result = cv2.bitwise_and(frame, frame, mask=bg_mask) | |
| cv2.imshow('Frame', frame) | |
| cv2.imshow('Mask', result) | |
| if cv2.waitKey(1) & 0xFF == ord("q"): | |
| break | |
| main() | |