init

.gitignore

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+
+\.vscode/

face_detectio.py

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+import face_recognition
+import cv2
+
+# This is a demo of blurring faces in video.
+
+# PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam.
+# OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this
+# specific demo. If you have trouble installing it, try any of the other demos that don't require it instead.
+
+# Get a reference to webcam #0 (the default one)
+video_capture = cv2.VideoCapture("http://192.168.178.53:8000/stream.mjpg")
+
+# Initialize some variables
+face_locations = []
+
+while True:
+    # Grab a single frame of video
+    ret, frame = video_capture.read()
+
+    # Resize frame of video to 1/4 size for faster face detection processing
+    small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
+
+    # Find all the faces and face encodings in the current frame of video
+    face_locations = face_recognition.face_locations(small_frame, model="cnn")
+
+    # Display the results
+    for top, right, bottom, left in face_locations:
+        # Scale back up face locations since the frame we detected in was scaled to 1/4 size
+        top *= 4
+        right *= 4
+        bottom *= 4
+        left *= 4
+
+        # Extract the region of the image that contains the face
+        face_image = frame[top:bottom, left:right]
+
+        # Blur the face image
+        face_image = cv2.GaussianBlur(face_image, (99, 99), 30)
+
+        # Put the blurred face region back into the frame image
+        frame[top:bottom, left:right] = face_image
+
+    # Display the resulting image
+    cv2.imshow('Video', frame)
+
+    # Hit 'q' on the keyboard to quit!
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+# Release handle to the webcam
+video_capture.release()
+cv2.destroyAllWindows()

motion_detector.py

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+# USAGE
+# python motion_detector.py
+# python motion_detector.py --video videos/example_01.mp4
+
+# import the necessary packages
+from imutils.video import VideoStream
+import argparse
+import datetime
+import imutils
+import time
+import cv2
+
+
+
+
+def increase_brightness(img, value=30):
+    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+    h, s, v = cv2.split(hsv)
+
+    lim = 255 - value
+    v[v > lim] = 255
+    v[v <= lim] += value
+
+    final_hsv = cv2.merge((h, s, v))
+    img = cv2.cvtColor(final_hsv, cv2.COLOR_HSV2BGR)
+
+    return img
+
+# construct the argument parser and parse the arguments
+ap = argparse.ArgumentParser()
+ap.add_argument("-v", "--video", help="path to the video file")
+ap.add_argument("-amin", "--min-area", type=int, default=3000, help="minimum area size")
+ap.add_argument("-amax", "--max-area", type=int, default=10000, help="minimum area size")
+args = vars(ap.parse_args())
+
+time.sleep(5)
+
+# if the video argument is None, then we are reading from webcam
+args["video"] = "http://192.168.178.53:8000/stream.mjpg"
+#args["video"] = "./videos/example_02.mp4"
+vs = cv2.VideoCapture(args["video"])
+counter = 0
+threashold = 50
+delay = 2
+framerate = 30
+
+# initialize the first frame in the video stream
+firstFrame = None
+
+# loop over the frames of the video
+while True:
+	# grab the current frame and initialize the occupied/unoccupied
+	# text
+	frame = vs.read()
+	frame = frame if args.get("video", None) is None else frame[1]
+	text = "Unoccupied"
+
+	# if the frame could not be grabbed, then we have reached the end
+	# of the video
+	if frame is None:
+		break
+
+	# resize the frame, convert it to grayscale, and blur it
+	frame = imutils.resize(frame, width=500)
+	#frame = increase_brightness(frame, value=50)
+	gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+	gray = cv2.GaussianBlur(gray, (31, 31), 0)
+
+	# if the first frame is None, initialize it
+	if firstFrame is None:
+		firstFrame = gray
+		continue
+
+	# compute the absolute difference between the current frame and
+	# first frame
+	frameDelta = cv2.absdiff(gray, firstFrame)
+
+	thresh = cv2.threshold(frameDelta, threashold, 255, cv2.THRESH_BINARY)[1]
+
+	# dilate the thresholded image to fill in holes, then find contours
+	# on thresholded image
+	thresh = cv2.dilate(thresh, None, iterations=2)
+	cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+	cnts = imutils.grab_contours(cnts)
+
+	# loop over the contours
+	for c in cnts:
+		# if the contour is too small, ignore it
+		if cv2.contourArea(c) < args["min_area"]:
+			continue
+		if cv2.contourArea(c) > args["max_area"]:
+			continue
+		# compute the bounding box for the contour, draw it on the frame,
+		# and update the text
+
+		print(cv2.contourArea(c))
+
+		(x, y, w, h) = cv2.boundingRect(c)
+		cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
+		text = "Occupied"
+
+	# draw the text and timestamp on the frame
+	cv2.putText(frame, "Room Status: {}".format(text), (10, 20),
+		cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
+	cv2.putText(frame, datetime.datetime.now().strftime("%A %d %B %Y %I:%M:%S%p"),
+		(10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 0, 255), 1)
+
+	# show the frame and record if the user presses a key
+	cv2.imshow("Security Feed", frame)
+	cv2.imshow("Thresh", thresh)
+	cv2.imshow("Frame Delta", frameDelta)
+
+	img_yuv = cv2.cvtColor(frame, cv2.COLOR_BGR2YUV)
+	# equalize the histogram of the Y channel
+	img_yuv[:,:,0] = cv2.equalizeHist(img_yuv[:,:,0])
+	# convert the YUV image back to RGB format
+	img_output = cv2.cvtColor(img_yuv, cv2.COLOR_YUV2BGR)
+
+	cv2.imshow("equalized", img_output)
+
+	key = cv2.waitKey(1) & 0xFF
+
+	# if the `q` key is pressed, break from the lop
+	if key == ord("q"):
+		break
+	counter+=1
+	#if counter % (framerate * delay) == 0:
+	#	firstFrame = gray
+
+# cleanup the camera and close any open windows
+vs.stop() if args.get("video", None) is None else vs.release()
+cv2.destroyAllWindows()