Finishing Main

Test.py

@@ -1,135 +0,0 @@
-# Author: Addison Sears-Collins
-# Description: This algorithm detects objects in a video stream
-#   using the Absolute Difference Method. The idea behind this
-#   algorithm is that we first take a snapshot of the background.
-#   We then identify changes by taking the absolute difference
-#   between the current video frame and that original
-#   snapshot of the background (i.e. first frame).
-
-# import the necessary packages
-from picamera.array import PiRGBArray # Generates a 3D RGB array
-from picamera import PiCamera # Provides a Python interface for the RPi Camera Module
-import time # Provides time-related functions
-import cv2 # OpenCV library
-import numpy as np # Import NumPy library
-
-# Initialize the camera
-camera = PiCamera()
-
-# Set the camera resolution
-camera.resolution = (640, 480)
-
-# Set the number of frames per second
-camera.framerate = 30
-
-# Generates a 3D RGB array and stores it in rawCapture
-raw_capture = PiRGBArray(camera, size=(640, 480))
-
-# Wait a certain number of seconds to allow the camera time to warmup
-time.sleep(0.1)
-
-# Initialize the first frame of the video stream
-first_frame = None
-
-# Create kernel for morphological operation. You can tweak
-# the dimensions of the kernel.
-# e.g. instead of 20, 20, you can try 30, 30
-kernel = np.ones((20,20),np.uint8)
-
-# Capture frames continuously from the camera
-for frame in camera.capture_continuous(raw_capture, format="bgr", use_video_port=True):
-
-    # Grab the raw NumPy array representing the image
-    image = frame.array
-
-    # Convert the image to grayscale
-    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-
-    # Close gaps using closing
-    gray = cv2.morphologyEx(gray,cv2.MORPH_CLOSE,kernel)
-
-    # Remove salt and pepper noise with a median filter
-    gray = cv2.medianBlur(gray,5)
-
-    # If first frame, we need to initialize it.
-    if first_frame is None:
-
-        first_frame = gray
-
-        # Clear the stream in preparation for the next frame
-        raw_capture.truncate(0)
-
-        # Go to top of for loop
-        continue
-
-    # Calculate the absolute difference between the current frame
-    # and the first frame
-    absolute_difference = cv2.absdiff(first_frame, gray)
-
-    # If a pixel is less than ##, it is considered black (background).
-    # Otherwise, it is white (foreground). 255 is upper limit.
-    # Modify the number after absolute_difference as you see fit.
-    _, absolute_difference = cv2.threshold(absolute_difference, 100, 255, cv2.THRESH_BINARY)
-
-    # Find the contours of the object inside the binary image
-    contours, hierarchy = cv2.findContours(absolute_difference,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)[-2:]
-    areas = [cv2.contourArea(c) for c in contours]
-
-    # If there are no countours
-    if len(areas) < 1:
-
-        # Display the resulting frame
-        cv2.imshow('Frame',image)
-
-        # Wait for keyPress for 1 millisecond
-        key = cv2.waitKey(1) & 0xFF
-
-        # Clear the stream in preparation for the next frame
-        raw_capture.truncate(0)
-
-        # If "q" is pressed on the keyboard,
-        # exit this loop
-        if key == ord("q"):
-            break
-
-        # Go to the top of the for loop
-        continue
-
-    else:
-
-        # Find the largest moving object in the image
-        # max_index = np.argmax(areas)
-
-    # Draw the bounding box
-    # cnt = contours[max_index]
-    print(absolute_difference)
-    # x,y,w,h = cv2.boundingRect(cnt)
-    # cv2.rectangle(image,(x,y),(x+w,y+h),(0,255,0),3)
-    #
-    # # Draw circle in the center of the bounding box
-    # x2 = x + int(w/2)
-    # y2 = y + int(h/2)
-    # cv2.circle(image,(x2,y2),4,(0,255,0),-1)
-    #
-    # # Print the centroid coordinates (we'll use the center of the
-    # # bounding box) on the image
-    # text = "x: " + str(x2) + ", y: " + str(y2)
-    # cv2.putText(image, text, (x2 - 10, y2 - 10),
-    #             cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
-
-    # Display the resulting frame
-    cv2.imshow("Frame",image)
-
-    # Wait for keyPress for 1 millisecond
-    key = cv2.waitKey(1) & 0xFF
-
-    # Clear the stream in preparation for the next frame
-    raw_capture.truncate(0)
-
-    # If "q" is pressed on the keyboard,
-    # exit this loop
-    if key == ord("q"):
-        break
-
-# Close down windows
-cv2.destroyAllWindows()

main.py

@@ -10,18 +10,36 @@ GPIO.setmode(GPIO.BCM) #Setting the GPIO Mode
 GPIO.setup(22, GPIO.OUT)                #LED output pin
 
 
-
+#To stabilize sensor and Camera
-time.sleep(2) # to stabilize sensor and Camera
+time.sleep(2)
 while True:
 
+    #Waits for Motion from PIR Sensor
     pir.wait_for_motion()
+
     print("Bug detected")
+
+    #Turns on the LED Flash
     GPIO.output(22, 1)
-    file_name = "/home/qldcomp/Pictures/img_" + str(time.time()) + ".jpg"
+
-    camera.capture_sequence(file_name)
+    #Sets Img path and filename, Saves to USB Connected
+    file_name = "/media/Capture_" + str(time.time()) + ".jpg"
+
+    #Used to wait for 0.1 Seconds for Camera to be ready
+    time.sleep(0.1)
+
+    #Saves File
+    camera.capture(file_name)
+
+    #Sets PIR Sensor back to waiting for motion
     pir.wait_for_no_motion()
-    time.sleep(1)
+
+    #Used to turn off the LED after 0.2 Secs
+    time.sleep(0.2)
+
+    #Used to turn off the LED
     GPIO.output(22, 0)
+
     print("Bug Not Detected")