diff --git a/Code/Ball_Detection/PyTorch_with_ESPCAM/imageTread.py b/Code/Ball_Detection/PyTorch_with_ESPCAM/imageTread.py
index 2720416411e39f25b3195d750333581ff2b05469..8795251b8d37923f2a40f4a4c5db49d189a0d311 100644
--- a/Code/Ball_Detection/PyTorch_with_ESPCAM/imageTread.py
+++ b/Code/Ball_Detection/PyTorch_with_ESPCAM/imageTread.py
@@ -6,31 +6,31 @@ def nothing(x):
pass
+if __name__ == "__main__":
+ #change the IP address below according to the
+ #IP shown in the Serial monitor of Arduino code
+ # url='http://192.168.4.1/cam-hi.jpg'
+ # url='http://192.168.1.107/cam-hi.jpg'
+ url='http://192.168.4.1/cam-mid.jpg'
-#change the IP address below according to the
-#IP shown in the Serial monitor of Arduino code
-url='http://192.168.4.1/cam-hi.jpg'
-# url='http://192.168.1.107/cam-hi.jpg'
-# url='http://192.168.1.107/cam-mid.jpg'
+ # cv2.namedWindow("live transmission", cv2.WINDOW_AUTOSIZE)
-# cv2.namedWindow("live transmission", cv2.WINDOW_AUTOSIZE)
+ cv2.namedWindow("live transmission", cv2.WINDOW_NORMAL)
-cv2.namedWindow("live transmission", cv2.WINDOW_NORMAL)
+ while True:
+ header = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/92.0.4515.159 Safari/537.36."}
+ req = Request(url, headers=header)
+ img_resp = urlopen(req, timeout=60)
+ imgnp=np.array(bytearray(img_resp.read()),dtype=np.uint8)
+ frame=cv2.imdecode(imgnp,-1)
-while True:
- header = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/92.0.4515.159 Safari/537.36."}
- req = Request(url, headers=header)
- img_resp = urlopen(req, timeout=60)
- imgnp=np.array(bytearray(img_resp.read()),dtype=np.uint8)
- frame=cv2.imdecode(imgnp,-1)
+ cv2.imshow("live transmission", frame)
+ h,w,_ = frame.shape
+ print("with:{},high:{}".format(w,h))
+ key=cv2.waitKey(5)
+ if key==ord('q'):
+ break
- cv2.imshow("live transmission", frame)
- h,w,_ = frame.shape
- print("with:{},high:{}".format(w,h))
- key=cv2.waitKey(5)
- if key==ord('q'):
- break
-
-cv2.destroyAllWindows()
+ cv2.destroyAllWindows()
diff --git a/Code/Control/Feather Code - Aaron/Motor_Basic_Test.ino b/Code/Control/Feather Code - Aaron/Motor_Basic_Test/Motor_Basic_Test.ino
similarity index 96%
rename from Code/Control/Feather Code - Aaron/Motor_Basic_Test.ino
rename to Code/Control/Feather Code - Aaron/Motor_Basic_Test/Motor_Basic_Test.ino
index 920859034d44b67a062877a99f5b17e3b755cb19..d00e1d346785f97d53d2eec735b01c9362f1151a 100644
--- a/Code/Control/Feather Code - Aaron/Motor_Basic_Test.ino
+++ b/Code/Control/Feather Code - Aaron/Motor_Basic_Test/Motor_Basic_Test.ino
@@ -3,8 +3,8 @@
#include <Wire.h>
#include <Adafruit_MotorShield.h>
-int largeVal = 255;
-int smallVal = 127;
+int largeVal = 100;
+int smallVal = 50;
Adafruit_MotorShield AFMS = Adafruit_MotorShield();
Adafruit_DCMotor *motorVL = AFMS.getMotor(1);
diff --git a/Code/OpenMV Code/Test_code/blue_main.py b/Code/OpenMV Code/Test_code/blue_main.py
new file mode 100644
index 0000000000000000000000000000000000000000..b5deee3de1a5859612111662975d7da4c3832aa0
--- /dev/null
+++ b/Code/OpenMV Code/Test_code/blue_main.py
@@ -0,0 +1,214 @@
+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)
+ sensor.set_auto_gain(False)
+ sensor.set_auto_whitebal(False)
+ 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:
+ red_led.on()
+ green_led.off()
+ 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.off()
+ red_led.off()
+ blue_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()
+ blue_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()