def find_shapes(image):
img = image
blobs = extract_blobs(img)
blobs2 = []
h, w = img.shape
y = h / 10
x = w / 10
for blob in blobs:
#print('before removing '+ str(len(blob)))
min_value = blob[0]
max_value = blob[-1]
if max_value[1] - min_value[1] < x * 5 and max_value[0] - min_value[
0] < y * 5: #and (len(blob) > 80):
blobs2.append(blob)
#for blob2 in blobs2:
#print('after removing '+str(len(blob2)))
blobs3 = coordinates(blobs2)
for blob3 in blobs3:
min_value = blob3[0]
max_value = blob3[-1]
#print('Min Value: '+str(min_value))
#print('Max Value: ' + str(max_value))
yield (min_value, max_value)
def conversion(image):
image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
cv2.imshow("image", image)
lower_red = np.array([160, 80, 80])
upper_red = np.array([190, 255, 255])
#lower_green = np.array([20, 100, 40]); upper_green = np.array([80, 255, 255])
mask_red = cv2.inRange(image, lower_red, upper_red)
cv2.imshow("mask", mask_red)
#mask_green = cv2.inRange(image, lower_green, upper_green)
blob_red = extract_blobs(mask_red)
#blob_green = extract_blobs(mask_green)
correct_red = []
#correct_green = []
print("red")
for red in blob_red:
print(len(red))
if len(red) > 4000:
correct_red.append(red)
"""
print("green")
for green in blob_green:
print(len(green))
if len(green) > 1000:
correct_green.append(green)
"""
red_positions = coordinates(correct_red)
#green_positions = coordinates(correct_green)
print(red_positions)
return red_positions
def findBlobs(image):
img = image
blobs = extract_blobs(img)
blobs2 = []
for blob in blobs:
if len(blob) > 4000:
blobs2.append(blob)
def calibration(image_to_crop):
# Blur image
blurred_image = cv2.cvtColor(image_to_crop, cv2.COLOR_BGR2GRAY)
blurred_image = cv2.GaussianBlur(blurred_image, (21, 21),
cv2.BORDER_DEFAULT)
# Threshold and mask white
#lower_white = np.array([90, 90, 90]); upper_white = np.array([255, 255, 255])
binary_image = cv2.inRange(blurred_image, 130, 255)
#cv2.imshow("gray", blurred_image)
#cv2.imshow("mask", binary_image)
cv2.imwrite("Evaluation/Grid_1/blobs.jpg", binary_image)
# Find blob
image_blobs = extract_blobs(binary_image)
correct_blobs = []
for blob in image_blobs:
if len(blob) > 13000:
correct_blobs.append(blob)
coord = coordinates(correct_blobs)
# Find placement grids
player1_placement = (coord[0][0], coord[0][-1])
#ratiox = coord[0][-1][0] - coord[0][0][0]
#ratioy = coord[0][-1][1] - coord[0][0][1]
player2_placement = (coord[-1][0], coord[-1][-1])
#player2_placement = (coord[-1][0], (coord[-1][0][0] + ratiox, coord[-1][0][1] + ratioy))
# Find attack grids
player1_attack = (coord[1][0], coord[1][-1])
#player1_attack = (coord[1][0], (coord[1][0][0] + ratiox, coord[1][0][1] + ratioy))
player2_attack = (coord[2][0], coord[2][-1])
#player2_attack = (coord[2][0], (coord[2][0][0] + ratiox, coord[2][0][1] + ratioy))
# Return the coordinates to the function, hence turning the function into these values
# These values can be accessed by refering to them as an array
return ((player1_placement, player2_placement), (player1_attack,
player2_attack))
def conversion(image):
image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
lower_red = np.array([140, 80, 80])
upper_red = np.array([190, 255, 255])
mask_red = cv2.inRange(image, lower_red, upper_red)
#cv2.imshow(".", mask_red)
blob_red = extract_blobs(mask_red)
correct_red = []
print("red")
for red in blob_red:
print(len(red))
if len(red) > 4000:
correct_red.append(red)
red_positions = coordinates(correct_red)
#green_positions = coordinates(correct_green)
print(red_positions)
return red_positions
binary_image[y_temp - 1, x_temp] = 0
queue.append([y_temp - 1, x_temp])
blob_pixels.append(queue.pop(0))
blobs.append(blob_pixels)
return blobs
back = cv2.imread('Pictures/empty.png')
curent = cv2.imread('Pictures/barrage.png')
cv2.imshow('smth', back)
cv2.imshow('smth2', curent)
w, h, c = back.shape
current_grey = cv2.cvtColor(curent, cv2.COLOR_BGR2GRAY)
current_greyB = cv2.GaussianBlur(current_grey, (7, 7), 0)
back_grey = cv2.cvtColor(back, cv2.COLOR_BGR2GRAY)
back_greyB = cv2.GaussianBlur(back_grey, (7, 7), 0)
difference = cv2.absdiff(back_greyB, current_greyB)
cv2.imshow('smth2', difference)
kernel = np.ones((5, 5), np.uint8)
img_binary = cv2.threshold(difference, 16, 255, cv2.THRESH_BINARY)[1]
erosion = cv2.erode(img_binary, kernel, iterations=2)
cv2.imshow('smth', erosion)
for r in extract_blobs(erosion):
min_value = r[0]
max_value = r[-1]
print(min_value, max_value)
for MinMax in shape_positions(r, w, h):
print(MinMax)
cv2.waitKey(0)
def findBlobs(image):
blobs = extract_blobs(image)
for blob in blobs:
print(blob[0], blob[-1])