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motion_detection.py
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motion_detection.py
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# import cv2
# background = cv2.imread("background2.png")
# background = cv2.cvtColor(background,cv2.COLOR_BGR2GRAY)
# background = cv2.GaussianBlur(background,(21,21),0)
# video = cv2.VideoCapture("test3.avi")
# while True:
# status, frame = video.read()
# gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
# gray = cv2.GaussianBlur(gray,(21,21), 0)
# diff = cv2.absdiff(background,gray)
# thresh = cv2.threshold(diff,30,255,cv2.THRESH_BINARY)[1]
# thresh = cv2.dilate(thresh, None, iterations = 2)
# cnts,res = cv2.findContours(thresh.copy(),
# cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# for contour in cnts:
# if cv2.contourArea(contour) < 10000 :
# continue
# (x,y,w,h) = cv2.boundingRect(contour)
# cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,0), 3)
# cv2.imshow("All Contours",frame)
# # cv2.imshow("Threshold Video",thresh)
# # cv2.imshow("Diff Video",diff)
# # cv2.imshow("Gray Video",gray)
# key = cv2.waitKey(1)
# if key == ord('q'):
# break
# video.release()
# cv2.destroyWindows()
# ##################################################################################################
# # Attempt 1.2
# import cv2
# video = cv2.VideoCapture("package_deliv.avi")
# # Background extraction
# background_frames = []
# background_frame_count = 2 # Number of frames for background estimation
# for _ in range(background_frame_count):
# status, frame = video.read()
# if not status:
# break
# gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# background_frames.append(gray)
# background = cv2.medianBlur(sum(background_frames) // len(background_frames), 21)
# while True:
# status, frame = video.read()
# if not status:
# break
# gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# gray = cv2.GaussianBlur(gray, (21, 21), 0)
# diff = cv2.absdiff(background, gray)
# thresh = cv2.threshold(diff, 30, 255, cv2.THRESH_BINARY)[1]
# thresh = cv2.dilate(thresh, None, iterations=2)
# cnts, res = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# for contour in cnts:
# if cv2.contourArea(contour) < 10000:
# continue
# (x, y, w, h) = cv2.boundingRect(contour)
# cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 3)
# cv2.imshow("All Contours", frame)
# key = cv2.waitKey(1)
# if key == ord('q'):
# break
# video.release()
# cv2.destroyAllWindows()
# ####################################################################################################
# # Attempt2
# import cv2
# video = cv2.VideoCapture("amazon_delivery2.avi")
# # Create a background subtractor
# bg_subtractor = cv2.createBackgroundSubtractorKNN()
# while True:
# status, frame = video.read()
# if not status:
# break
# # Apply background subtraction
# fg_mask = bg_subtractor.apply(frame)
# fg_mask = cv2.threshold(fg_mask, 30, 255, cv2.THRESH_BINARY)[1]
# fg_mask = cv2.dilate(fg_mask, None, iterations=2)
# # draw rectangles
# cnts, _ = cv2.findContours(fg_mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# for contour in cnts:
# if cv2.contourArea(contour) < 10000:
# continue
# (x, y, w, h) = cv2.boundingRect(contour)
# cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 3)
# cv2.imshow("Motion Detection", frame)
# key = cv2.waitKey(1)
# if key == ord('q'):
# break
# video.release()
# cv2.destroyAllWindows()
# #############################################################################################
# # Attempt3
# import numpy as np
# import cv2
# net = cv2.dnn.readNet("yolov3-tiny.weights", "yolov3-tiny.cfg")
# classes = []
# with open("coco.names", "r") as f:
# classes = f.read().strip().split("\n")
# video = cv2.VideoCapture("package_deliv.avi")
# while True:
# status, frame = video.read()
# if not status:
# break
# height, width, _ = frame.shape
# blob = cv2.dnn.blobFromImage(frame, 1/255.0, (416, 416), swapRB=True, crop=False)
# net.setInput(blob)
# outs = net.forward(net.getUnconnectedOutLayersNames())
# for out in outs:
# for detection in out:
# scores = detection[5:]
# class_id = int(detection[1])
# confidence = scores[class_id]
# if confidence > 0.5: # Change the confidence threshold as needed
# box = detection[0:4] * np.array([width, height, width, height])
# (x, y, w, h) = box.astype("int")
# cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
# cv2.putText(frame, classes[class_id], (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
# cv2.imshow("Object Detection", frame)
# key = cv2.waitKey(1)
# if key == ord('q'):
# break
# video.release()
# cv2.destroyAllWindows()