packages from imutils.video import VideoStream from imutils.video import FPS from multiprocessing import Process from multiprocessing import Queue import numpy as np import argparse import imutils import time import cv2 from pylepton import Lepton

setup Lepton #setup the Lepton image buffer

buffer def capture(device = "/dev/spidev0.0"): with Lepton() as l: a,_ = l.capture() #grab the buffer cv2.normalize(a, a, 0, 65535, cv2.NORM_MINMAX) # extend contrast np.right_shift(a, 8, a) # fit data into 8 bits return np.uint8(a)

np.uint8(a) def applyCustomColorMap(im_gray) : #this colormap is called Ironblack lut = np.zeros((256, 1, 3), dtype=np.uint8) lut[:, 0, 2] = [0, 253, 251, 249, 247, 245, 243, 241, 239, 237, 235, 233, 231, 229, 227, 225, 223, 221, 219, 217, 215, 213, 211, 209, 207, 205, 203, 201, 199, 197, 195, 193, 191, 189, 187, 185, 183, 181, 179, 177, 175, 173, 171, 169, 167, 165, 163, 161, 159, 157, 155, 153, 151, 149, 147, 145, 143, 141, 139, 137, 135, 133, 131, 129, 126, 124, 122, 120, 118, 116, 114, 112, 110, 108, 106, 104, 102, 100, 98, 96, 94, 92, 90, 88, 86, 84, 82, 80, 78, 76, 74, 72, 70, 68, 66, 64, 62, 60, 58, 56, 54, 52, 50, 48, 46, 44, 42, 40, 38, 36, 34, 32, 30, 28, 26, 24, 22, 20, 18, 16, 14, 12, 10, 8, 6, 4, 2, 0, 0, 2, 4, 6, 8, 10, 12, 14, 17, 19, 21, 23, 25, 27, 29, 31, 36, 41, 46, 51, 56, 61, 66, 71, 76, 81, 86, 91, 96, 101, 106, 111, 116, 121, 125, 130, 135, 139, 144, 149, 153, 158, 163, 167, 172, 177, 181, 186, 189, 191, 194, 196, 198, 200, 203, 205, 207, 209, 212, 214, 216, 218, 221, 223, 224, 225, 226, 227, 228, 229, 230, 231, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 240, 241, 241, 242, 242, 243, 243, 244, 244, 245, 245, 246, 246, 247, 247, 248, 248, 249, 249, 250, 250, 251, 251, 252, 252, 253, 253, 254, 254, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255] lut[:, 0, 1] = [0, 253, 251, 249, 247, 245, 243, 241, 239, 237, 235, 233, 231, 229, 227, 225, 223, 221, 219, 217, 215, 213, 211, 209, 207, 205, 203, 201, 199, 197, 195, 193, 191, 189, 187, 185, 183, 181, 179, 177, 175, 173, 171, 169, 167, 165, 163, 161, 159, 157, 155, 153, 151, 149, 147, 145, 143, 141, 139, 137, 135, 133, 131, 129, 126, 124, 122, 120, 118, 116, 114, 112, 110, 108, 106, 104, 102, 100, 98, 96, 94, 92, 90, 88, 86, 84, 82, 80, 78, 76, 74, 72, 70, 68, 66, 64, 62, 60, 58, 56, 54, 52, 50, 48, 46, 44, 42, 40, 38, 36, 34, 32, 30, 28, 26, 24, 22, 20, 18, 16, 14, 12, 10, 8, 6, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 5, 5, 6, 6, 7, 7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 37, 40, 43, 46, 49, 52, 55, 57, 60, 64, 67, 71, 74, 78, 81, 85, 88, 92, 95, 99, 102, 106, 109, 112, 116, 119, 123, 127, 130, 134, 138, 141, 145, 149, 152, 156, 160, 163, 167, 171, 175, 178, 182, 185, 189, 192, 196, 199, 203, 206, 210, 213, 217, 220, 224, 227, 229, 231, 233, 234, 236, 238, 240, 242, 244, 246, 248, 249, 251, 253, 255] lut[:, 0, 0] = [0, 253, 251, 249, 247, 245, 243, 241, 239, 237, 235, 233, 231, 229, 227, 225, 223, 221, 219, 217, 215, 213, 211, 209, 207, 205, 203, 201, 199, 197, 195, 193, 191, 189, 187, 185, 183, 181, 179, 177, 175, 173, 171, 169, 167, 165, 163, 161, 159, 157, 155, 153, 151, 149, 147, 145, 143, 141, 139, 137, 135, 133, 131, 129, 126, 124, 122, 120, 118, 116, 114, 112, 110, 108, 106, 104, 102, 100, 98, 96, 94, 92, 90, 88, 86, 84, 82, 80, 78, 76, 74, 72, 70, 68, 66, 64, 62, 60, 58, 56, 54, 52, 50, 48, 46, 44, 42, 40, 38, 36, 34, 32, 30, 28, 26, 24, 22, 20, 18, 16, 14, 12, 10, 8, 6, 4, 2, 0, 9, 16, 24, 31, 38, 45, 53, 60, 67, 74, 82, 89, 96, 103, 111, 118, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 136, 137, 137, 137, 138, 138, 138, 139, 139, 139, 140, 140, 140, 141, 141, 137, 132, 127, 121, 116, 111, 106, 101, 95, 90, 85, 80, 75, 69, 64, 59, 49, 47, 44, 42, 39, 37, 34, 32, 29, 27, 24, 22, 19, 17, 14, 12, 12, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 39, 53, 67, 81, 95, 109, 123, 137, 151, 165, 179, 193, 207, 221, 235, 24] im_color = cv2.LUT(im_gray, lut) return im_color;

im_color; def classify_frame(net, inputQueue, outputQueue): # keep looping while True: # check to see if there is a frame in our input queue if not inputQueue.empty(): # grab the frame from the input queue, resize it, and # construct a blob from it frame = inputQueue.get() frame = cv2.resize(frame, (300, 300)) blob = cv2.dnn.blobFromImage(frame, 0.007843, (300, 300), 127.5)

127.5)

            # set the blob as input to our deep learning object
         # detector and obtain the detections
         net.setInput(blob)
         detections = net.forward()

         # write the detections to the output queue
         outputQueue.put(detections)

# construct the argument parse and parse the arguments

arguments ap = argparse.ArgumentParser() ap.add_argument("-p", "--prototxt", required=True, help="path to Caffe 'deploy' prototxt file") ap.add_argument("-m", "--model", required=True, help="path to Caffe pre-trained model") ap.add_argument("-c", "--confidence", type=float, default=0.2, help="minimum probability to filter weak detections") args = vars(ap.parse_args())

vars(ap.parse_args()) # initialize the list of class labels MobileNet SSD was trained to

to # detect, then generate a set of bounding box colors for each class

class CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"] COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))

3)) # load our serialized model from disk

disk print("[INFO] loading model...")

model...") #net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"]) net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])

net = cv2.imread(args["model"],cv2.IMREAD_GRAYSCALE)

cv2.imread(args["model"],cv2.IMREAD_GRAYSCALE) # initialize the input queue (frames), output queue (detections),

(detections), # and the list of actual detections returned by the child process

process inputQueue = Queue(maxsize=1) outputQueue = Queue(maxsize=1) detections = None

None # construct a child process indepedent *indepedent* from our main process of

execution

of # execution print("[INFO] starting process...") p = Process(target=classify_frame, args=(net, inputQueue, outputQueue,)) p.daemon = True p.start()

p.start() # initialize the video stream, allow the cammera sensor to warmup,

warmup, # and initialize the FPS counter

counter print("[INFO] starting video stream...") vs = capture()

capture() # vs = VideoStream(usePiCamera=True).start()

VideoStream(usePiCamera=True).start() time.sleep(2.0) fps = FPS().start() thresh = cv2.getTrackbarPos('thresh','Lepton')

cv2.getTrackbarPos('thresh','Lepton') # loop over the frames from the video stream

stream while True: # grab the frame from the threaded video stream, resize it, and # grab its imensions frame = vs frame = imutils.resize(frame, width=400) (fH, fW) = frame.shape[:2]

frame.shape[:2]

    # if the input queue *is* empty, give the current frame to
 # classify
 if inputQueue.empty():
     inputQueue.put(frame)

 # if the output queue *is not* empty, grab the detections
 if not outputQueue.empty():
     detections = outputQueue.get()

 # check to see if our detectios are not None (and if so, we'll
 # draw the detections on the frame)
 if detections is not None:
     # loop over the detections
     for i in np.arange(0, detections.shape[2]):
         # extract the confidence (i.e., probability) associated
         # with the prediction
         confidence = detections[0, 0, i, 2]

         # filter out weak detections by ensuring the `confidence`
         # is greater than the minimum confidence
         if confidence < args["confidence"]:
             continue

         # otherwise, extract the index of the class label from
         # the `detections`, then compute the (x, y)-coordinates
         # of the bounding box for the object
         idx = int(detections[0, 0, i, 1])
         dims = np.array([fW, fH, fW, fH])
         box = detections[0, 0, i, 3:7] * dims
         (startX, startY, endX, endY) = box.astype("int")
         _ ,frame = cv2.threshold(img,thresh,255,cv2.THRESH_TOZERO)
         frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR);
         frame = applyCustomColorMap(frame);
         # draw the prediction on the frame
         label = "{}: {:.2f}%".format(CLASSES[idx],
             confidence * 100)
         cv2.rectangle(frame, (startX, startY), (endX, endY),
             COLORS[idx], 2)
         y = startY - 15 if startY - 15 > 15 else startY + 15
         cv2.putText(frame, label, (startX, y),
             cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)

 # show the output frame
 cv2.imshow("Frame", frame)
 key = cv2.waitKey(1) & 0xFF

 # if the `q` key was pressed, break from the loop
 if key == ord("q"):
     break

 # update the FPS counter
 fps.update()

# stop the timer and display FPS information

information fps.stop() print("[INFO] elapsed time: {:.2f}".format(fps.elapsed())) print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))

{:.2f}".format(fps.fps())) # do a bit of cleanup

cleanup cv2.destroyAllWindows() vs.stop()