diff --git a/Code/OpenMV Code/Test_code/new_main.py b/Code/OpenMV Code/Test_code/new_main.py
new file mode 100644
index 0000000000000000000000000000000000000000..034ee9bf6c16c7e5679b4fdb532218543a20d083
--- /dev/null
+++ b/Code/OpenMV Code/Test_code/new_main.py	
@@ -0,0 +1,206 @@
+import image, network, math, rpc, sensor, struct, tf, ucollections, pyb
+import ubinascii
+sensor.reset()
+sensor.set_pixformat(sensor.RGB565)
+sensor.set_framesize(sensor.QVGA)
+sensor.set_auto_gain(False)
+sensor.set_auto_whitebal(False)
+sensor.skip_frames(time = 2000)
+red_led = pyb.LED(1)
+green_led = pyb.LED(2)
+blue_led = pyb.LED(3)
+red_led.off()
+green_led.off()
+blue_led.off()
+red_led.on()
+interface = rpc.rpc_i2c_slave(slave_addr=0x12)
+MAX_BLOBS = 4
+TAG_SIZE = 138
+MAX_TAGS = 2
+XRES = 320
+YRES = 240
+SIZE = 16.3
+f_x = (2.8 / 3.673) * XRES
+f_y = (2.8 / 2.738) * YRES
+c_x = XRES * 0.5
+c_y = YRES * 0.5
+def draw_detections(img, dects):
+	for d in dects:
+		c = d.corners()
+		l = len(c)
+		for i in range(l): img.draw_line(c[(i+0)%l] + c[(i+1)%l], color = (0, 255, 0))
+		img.draw_rectangle(d.rect(), color = (255, 0, 0))
+def face_detection(data):
+	sensor.set_pixformat(sensor.GRAYSCALE)
+	sensor.set_framesize(sensor.QVGA)
+	faces = sensor.snapshot().gamma_corr(contrast=1.5).find_features(image.HaarCascade("frontalface"))
+	if not faces: return struct.pack("<HHHH", 0, 0, 0, 0)
+	for f in faces: sensor.get_fb().draw_rectangle(f, color = (255, 255, 255))
+	out_face = max(faces, key = lambda f: f[2] * f[3])
+	return struct.pack("<HHHH", out_face[0], out_face[1], out_face[2], out_face[3])
+def find_position(box):
+	x = box[0]
+	y = box[1]
+	w = box[2]
+	h = box[3]
+	cx = x + w/2
+	cy = y + h/2
+	frame_cx = sensor.width()/2
+	frame_cy = sensor.height()/2
+	if cx < frame_cx:
+		if cy < frame_cy:
+			pos = "UL"
+		else:
+			pos = "LL"
+	else:
+		if cy < frame_cy:
+			pos = "UR"
+		else:
+			pos = "LR"
+	x_diff = cx - frame_cx
+	y_diff = cy - frame_cy
+	area = w*h
+	return pos
+def find_dist(box, w_actual, l_actual):
+	w_dist = w_actual*2.8/box[2]
+	l_dist = l_actual*2.8/box[3]
+	dist = (w_dist+l_dist)/2
+	return dist*100
+ignore_blue = (0, 0, sensor.width(), sensor.height())
+def color_detection(thresholds):
+	sensor.set_pixformat(sensor.RGB565)
+	sensor.set_framesize(sensor.QVGA)
+	sensor.set_auto_gain(False)
+	sensor.set_auto_whitebal(False)
+	thresholdss = struct.unpack("<bbbbbb", thresholds)
+	img = sensor.snapshot()
+	blobs = [0,0,0,0,0,0,0,0,0,0,0,0]
+	n_blobs = 0
+	for blob in img.find_blobs([thresholdss], pixels_threshold=200, area_threshold=200, merge=True):
+		img.draw_edges(blob.min_corners(), color=(0,0,255))
+		item = blob.rect()
+		area = item[2] * item[3]
+		if n_blobs < MAX_BLOBS:
+			blobs[0 + (n_blobs*3): 2 + (n_blobs*3)] = [int(blob.cx()), int(blob.cy()), int(area)]
+			n_blobs += 1
+	print(blobs)
+	return struct.pack("<hhhhhhhhh", blobs[0], blobs[1], blobs[2], blobs[3], blobs[4], blobs[5], blobs[6],
+					   blobs[7], blobs[8])
+def color_detection_single(data):
+	red_led.off()
+	green_led.on()
+	sensor.set_pixformat(sensor.RGB565)
+	sensor.set_framesize(sensor.QVGA)
+	thresholds = struct.unpack("<bbbbbb", data)
+	print(thresholds)
+	blobs = sensor.snapshot().find_blobs([thresholds],
+										 pixels_threshold=500,
+										 area_threshold=500,
+										 merge=True,
+										 margin=20)
+	if not blobs: return struct.pack("<HH", 0, 0)
+	for b in blobs:
+		sensor.get_fb().draw_rectangle(b.rect(), color = (255, 0, 0))
+		sensor.get_fb().draw_cross(b.cx(), b.cy(), color = (0, 255, 0))
+	out_blob = max(blobs, key = lambda b: b.density())
+	red_led.on()
+	green_led.off()
+	return struct.pack("<HH", out_blob.cx() , out_blob.cy())
+def recalibration():
+	img = sensor.snapshot()
+	for blob in img.find_blobs(thresholds[2], invert=True, pixels_threshold=200, area_threshold=200, merge=True):
+		img.draw_edges(blob.min_corners(), color=(0,0,255))
+		ignore_blue = (blob.x(), blob.y(), blob.w(), blob.h())
+def change_tag_size(size):
+	TAG_SIZE = size
+def degrees(radians):
+	return (180 * radians) / math.pi
+def dist_conversion(z):
+	z = z*100*2
+	scale = TAG_SIZE/138
+	res_scale = (240/X_RES + 240/Y_RES)/2
+	return z*scale*res_scale
+def xy_to_mm(tag1_cx, tag1_cy, tag2_cx, tag2_cy, tag3_cx, tag3_cy):
+	mm_distx = 590
+	mm_disty = 1
+	pixel_distx = tag1_cx - tag2_cx
+	pixel_disty = tag1_cy - tag2_cy
+	target_distx = tag1_cx - tag3_cx
+	target_disty = tag1_cy - tag3_cy
+	distx = target_distx*mm_distx/pixel_distx/2.8
+	disty = target_disty*mm_disty/pixel_disty/2.8
+	return (distx, disty)
+def apriltags(data):
+	red_led.off()
+	green_led.on()
+	sensor.set_pixformat(sensor.RGB565)
+	sensor.set_framesize(sensor.QQVGA)
+	sensor.set_auto_gain(False)
+	sensor.set_auto_whitebal(False)
+	LENS_MM = 2.8
+	LENS_TO_CAM_MM = 22
+	datas = struct.unpack("<bb", data)
+	id1 = datas[0]
+	tagsize1 = 138
+	if id1 == 0:
+		tagsize1 = 138
+	img = sensor.snapshot()
+	f_x = (LENS_MM / 3.984) * X_RES
+	f_y = (LENS_MM / 2.952) * Y_RES
+	c_x = X_RES * 0.5
+	c_y = Y_RES * 0.5
+	tags = [0, 0, 0]
+	for tag in img.find_apriltags(fx=f_x, fy=f_y, cx=c_x, cy=c_y):
+		img.draw_rectangle(tag.rect(), color = (255, 0, 0))
+		img.draw_cross(tag.cx(), tag.cy(), color = (0, 255, 0))
+		dist = dist_conversion(tag.z_translation())
+		img.draw_string(tag.cx(), tag.cy(), str(dist))
+		if tag.id() == id1:
+			print(tag.id())
+			TAG_SIZE = tagsize1
+			tags[0:2] = [int(tag.cx()), int(tag.cy()), int(degrees(tag.z_rotation()))]
+	red_led.on()
+	green_led.off()
+	return struct.pack("<hhh", tags[0], tags[1], tags[2])
+def qrcode_detection(data):
+	sensor.set_pixformat(sensor.RGB565)
+	sensor.set_framesize(sensor.VGA)
+	sensor.set_windowing((320, 240))
+	codes = sensor.snapshot().find_qrcodes()
+	if not codes: return bytes()
+	draw_detections(sensor.get_fb(), codes)
+	return str(codes).encode()
+def ATDistance(val,size):
+	new_val = -10*val*size/16.3
+	return new_val
+def goalfinder(data):
+	green_led.on()
+	sensor.set_pixformat(sensor.RGB565)
+	sensor.set_framesize(sensor.QVGA)
+	sensor.set_auto_gain(False)
+	sensor.set_auto_whitebal(False)
+	goal_id = struct.unpack("<bbb", data)
+	nearest_tag = [0,0,0,10000,0,0,0]
+	img = sensor.snapshot()
+	for tag in img.find_apriltags(fx=f_x, fy=f_y, cx=c_x, cy=c_y):
+		img.draw_rectangle(tag.rect(), color = (255, 0, 0))
+		img.draw_cross(tag.cx(), tag.cy(), color = (0, 255, 0))
+		for i in goal_id:
+			if tag.id() == i and ATDistance(tag.z_translation(),SIZE) < nearest_tag[3]:
+				nearest_tag[0] = int(tag.id())
+				nearest_tag[1] = int(tag.cx())
+				nearest_tag[2] = int(tag.cy())
+				nearest_tag[3] = int(ATDistance(tag.z_translation(),SIZE))
+				nearest_tag[4] = int(tag.x_rotation())
+				nearest_tag[5] = int(tag.y_rotation())
+				nearest_tag[6] = int(tag.z_rotation())
+	red_led.on()
+	green_led.off()
+	return struct.pack("<hhhhhhh", nearest_tag[0],nearest_tag[1],nearest_tag[2],nearest_tag[3],nearest_tag[4],nearest_tag[5],nearest_tag[6])
+interface.register_callback(face_detection)
+interface.register_callback(color_detection)
+interface.register_callback(apriltags)
+interface.register_callback(color_detection_single)
+interface.register_callback(recalibration)
+interface.register_callback(goalfinder)
+interface.loop()
\ No newline at end of file