Merge pull request #2 from Jordan-Fielding/Development

test

Test.py

@@ -0,0 +1,134 @@
+# 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]
+    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

@@ -9,25 +9,23 @@ GPIO.setup(22, GPIO.IN)         #Read output from PIR motion sensor
 GPIO.setup(27, GPIO.OUT)         #LED output pin
 
 i=GPIO.input(22)
-try:
-    time.sleep(2) # to stabilize sensor
-    while True:
-        ts = time.time()
-        st = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d_%H:%M:%S')
-        if i==0:                 #When output from motion sensor is LOW
-            print ("No bugs detected",i)
-            GPIO.output(3, 0)  #Turn OFF LED
-            time.sleep(0.1)
-        if i==1:               #When output from motion sensor is HIGH
-            print ("bugs detected",i)
-            GPIO.output(3, 1)  #Turn ON LED
-            camera.capture('image_Time_{}.jpg'.format(st))
-            os.system('libcamera-jpeg -o /home/pi/Pictures/image_Time_{}.jpg'.format(st))
-            camera.close()  #Capture an Image
-            time.sleep(0.1)
 
-except:
-    GPIO.cleanup()
+time.sleep(2) # to stabilize sensor
+while True:
+    ts = time.time()
+    st = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d_%H:%M:%S')
+    if i==0:                 #When output from motion sensor is LOW
+        print ("No bugs detected",i)
+        GPIO.output(3, 0)  #Turn OFF LED
+        time.sleep(0.1)
+    if i==1:               #When output from motion sensor is HIGH
+        print ("bugs detected",i)
+        GPIO.output(3, 1)  #Turn ON LED
+        camera.capture('image_Time_{}.jpg'.format(st))
+        os.system('libcamera-jpeg -o /home/pi/Pictures/image_Time_{}.jpg'.format(st))
+        camera.close()  #Capture an Image
+        time.sleep(0.1)
+