diff --git a/YoloPython/darknet.py b/YoloPython/darknet.py
@@ -0,0 +1,190 @@
+from ctypes import *
+import math
+import random
+
+def sample(probs):
+    s = sum(probs)
+    probs = [a/s for a in probs]
+    r = random.uniform(0, 1)
+    for i in range(len(probs)):
+        r = r - probs[i]
+        if r <= 0:
+            return i
+    return len(probs)-1
+
+def c_array(ctype, values):
+    arr = (ctype*len(values))()
+    arr[:] = values
+    return arr
+
+class BOX(Structure):
+    _fields_ = [("x", c_float),
+                ("y", c_float),
+                ("w", c_float),
+                ("h", c_float)]
+
+class DETECTION(Structure):
+    _fields_ = [("bbox", BOX),
+                ("classes", c_int),
+                ("prob", POINTER(c_float)),
+                ("mask", POINTER(c_float)),
+                ("objectness", c_float),
+                ("sort_class", c_int)]
+
+
+class IMAGE(Structure):
+    _fields_ = [("w", c_int),
+                ("h", c_int),
+                ("c", c_int),
+                ("data", POINTER(c_float))]
+
+class METADATA(Structure):
+    _fields_ = [("classes", c_int),
+                ("names", POINTER(c_char_p))]
+
+
+
+#lib = CDLL("/home/pjreddie/documents/darknet/libdarknet.so", RTLD_GLOBAL)
+lib = CDLL("libdarknet.so", RTLD_GLOBAL)
+lib.network_width.argtypes = [c_void_p]
+lib.network_width.restype = c_int
+lib.network_height.argtypes = [c_void_p]
+lib.network_height.restype = c_int
+
+predict = lib.network_predict
+predict.argtypes = [c_void_p, POINTER(c_float)]
+predict.restype = POINTER(c_float)
+
+set_gpu = lib.cuda_set_device
+set_gpu.argtypes = [c_int]
+
+detect = lib.network_predict
+detect.argtypes = [c_void_p, IMAGE, c_float, c_float, c_float, POINTER(BOX), POINTER(POINTER(c_float))]
+
+make_image = lib.make_image
+make_image.argtypes = [c_int, c_int, c_int]
+make_image.restype = IMAGE
+
+get_network_boxes = lib.get_network_boxes
+get_network_boxes.argtypes = [c_void_p, c_int, c_int, c_float, c_float, POINTER(c_int), c_int, POINTER(c_int)]
+get_network_boxes.restype = POINTER(DETECTION)
+
+make_network_boxes = lib.make_network_boxes
+make_network_boxes.argtypes = [c_void_p]
+make_network_boxes.restype = POINTER(DETECTION)
+
+free_detections = lib.free_detections
+free_detections.argtypes = [POINTER(DETECTION), c_int]
+
+free_ptrs = lib.free_ptrs
+free_ptrs.argtypes = [POINTER(c_void_p), c_int]
+
+network_predict = lib.network_predict
+network_predict.argtypes = [c_void_p, POINTER(c_float)]
+
+reset_rnn = lib.reset_rnn
+reset_rnn.argtypes = [c_void_p]
+
+load_net = lib.load_network
+load_net.argtypes = [c_char_p, c_char_p, c_int]
+load_net.restype = c_void_p
+
+do_nms_obj = lib.do_nms_obj
+do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]
+
+do_nms_sort = lib.do_nms_sort
+do_nms_sort.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]
+
+free_image = lib.free_image
+free_image.argtypes = [IMAGE]
+
+letterbox_image = lib.letterbox_image
+letterbox_image.argtypes = [IMAGE, c_int, c_int]
+letterbox_image.restype = IMAGE
+
+load_meta = lib.get_metadata
+lib.get_metadata.argtypes = [c_char_p]
+lib.get_metadata.restype = METADATA
+
+load_image = lib.load_image_color
+load_image.argtypes = [c_char_p, c_int, c_int]
+load_image.restype = IMAGE
+
+ndarray_image = lib.ndarray_to_image
+ndarray_image.argtypes = [POINTER(c_ubyte), POINTER(c_long), POINTER(c_long)]
+ndarray_image.restype = IMAGE
+
+rgbgr_image = lib.rgbgr_image
+rgbgr_image.argtypes = [IMAGE]
+
+predict_image = lib.network_predict_image
+predict_image.argtypes = [c_void_p, IMAGE]
+predict_image.restype = POINTER(c_float)
+
+def classify(net, meta, im):
+    out = predict_image(net, im)
+    res = []
+    for i in range(meta.classes):
+        res.append((meta.names[i], out[i]))
+    res = sorted(res, key=lambda x: -x[1])
+    return res
+
+def nparray_to_image(img):
+
+    data = img.ctypes.data_as(POINTER(c_ubyte))
+    image = ndarray_image(data, img.ctypes.shape, img.ctypes.strides)
+
+    return image
+
+def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45):
+    im = load_image(image, 0, 0)
+    num = c_int(0)
+    pnum = pointer(num)
+    predict_image(net, im)
+    dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum)
+    num = pnum[0]
+    if (nms): do_nms_obj(dets, num, meta.classes, nms);
+
+    res = []
+    for j in range(num):
+        for i in range(meta.classes):
+            if dets[j].prob[i] > 0:
+                b = dets[j].bbox
+                res.append((meta.names[i], dets[j].prob[i], (b.x, b.y, b.w, b.h)))
+    res = sorted(res, key=lambda x: -x[1])
+    free_image(im)
+    free_detections(dets, num)
+    return res
+
+def detect_np(net, meta, np_img, thresh=.5, hier_thresh=.5, nms=.45):
+    im = nparray_to_image(np_img)
+    num = c_int(0)
+    pnum = pointer(num)
+    predict_image(net, im)
+    dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum)
+    num = pnum[0]
+    if (nms): do_nms_obj(dets, num, meta.classes, nms);
+
+    res = []
+    for j in range(num):
+        for i in range(meta.classes):
+            if dets[j].prob[i] > 0:
+                b = dets[j].bbox
+                res.append((meta.names[i], dets[j].prob[i], (b.x, b.y, b.w, b.h)))
+    res = sorted(res, key=lambda x: -x[1])
+    free_image(im)
+    free_detections(dets, num)
+    return res
+
+if __name__ == "__main__":
+    #net = load_net("cfg/densenet201.cfg", "/home/pjreddie/trained/densenet201.weights", 0)
+    #im = load_image("data/wolf.jpg", 0, 0)
+    #meta = load_meta("cfg/imagenet1k.data")
+    #r = classify(net, meta, im)
+    #print r[:10]
+    net = load_net("cfg/tiny-yolo.cfg", "tiny-yolo.weights", 0)
+    meta = load_meta("cfg/coco.data")
+    r = detect(net, meta, "data/dog.jpg")
+    print r
+
+
diff --git a/YoloPython/libdarknet.so b/YoloPython/libdarknet.so
diff --git a/YoloPython/test.data b/YoloPython/test.data
@@ -0,0 +1,6 @@
+classes= 20
+train  = /home/mechatronics/darknet/6_15_test/train.txt
+valid = /home/mechatronics/darknet/6_15_test/test.txt
+names = test.names
+backup = /home/mechatronics/darknet/6_15_test/weights/
+
diff --git a/YoloPython/test.names b/YoloPython/test.names
@@ -0,0 +1,21 @@
+None
+Dice 1
+Dice 2
+Dice 3
+Dice 4
+Dice 5
+Dice 6
+Roulette
+Cherry
+Banana
+Grape
+Full Board
+Top Left Hole
+Top Right Hole
+Bottom Hole
+Qualification Gate Arm
+Qualificaiton Gate Top
+Entry Gate Arm
+Entry Gate Top
+Orange Path
+
diff --git a/YoloPython/yoloPython.py b/YoloPython/yoloPython.py
@@ -0,0 +1,36 @@
+import darknet as dn
+import sys
+import os
+import PyCapture2 as pc2
+import cv2
+import numpy as np
+import time
+
+dn.set_gpu(0)
+
+net = dn.load_net("6_15_test.cfg", "6_15_test_40000.weights", 0)
+meta = dn.load_meta("test.data")
+
+bus = pc2.BusManager()
+cam = pc2.Camera()
+cam.connect(bus.getCameraFromIndex(0))
+cam.startCapture()
+startTime = time.time()
+
+while True:
+	print "Time was", time.time() - startTime
+	startTime = time.time()
+	image = cam.retrieveBuffer()
+	image = image.convert(pc2.PIXEL_FORMAT.BGR)
+	img = np.array(image.getData(), dtype="uint8").reshape((image.getRows(), image.getCols(), 3))
+	yoloImage = dn.IMAGE()
+	detections = dn.detect_np(net, meta, img)
+	for detection in detections:
+		loc = detection[2]
+		cv2.rectangle(img, (int(loc[0]-(.5 * loc[2])), int(loc[1]- (.5 * loc[3]))), (int(loc[0] + (.5*loc[2])), int(loc[1] + (.5*loc[3]))), (0,0,255))
+	cv2.imshow("Test", img)
+	key = cv2.waitKey(1)
+	if key == ord("q"):
+		break
+
+