from detection.utils import *
def detect(input_file, config):
img = cv2.imread(input_file)
height, width, _ = img.shape
aspect_ratio = config["Aspect Ratio"]
height = int(width*aspect_ratio)
""" Resize image to limits in config file """
limits = np.array(config['Limits'])
transform_mat = cv2.getPerspectiveTransform(np.float32(limits), np.float32(
[[0, 0], [width, 0], [width, height], [0, height]]))
img = cv2.warpPerspective(img, transform_mat, (width, height))
""" Prepare upper and lower mask board """
upper_mask = np.zeros(img.shape[0:2], np.uint8)
lower_mask = np.zeros(img.shape[0:2], np.uint8)
upper_mask = cv2.rectangle(upper_mask, (0, 0), (width, int(height/2)), 255, thickness=cv2.FILLED)
lower_mask = cv2.rectangle(lower_mask, (0, int(height / 2)), (width, height), 255, thickness=cv2.FILLED)
""" Find blob """
sat = saturation(img)
# sum = mean_image([satA, satB])
blob_mask = find_blob(sat)
blob = cv2.bitwise_and(img, img, mask=blob_mask)
""" Print blob information """
print("Blob covering:")
print("\t{:.2f}% of the board.".format(mean_percent_value(blob_mask)))
print("\t{:.2f}% of the upper board.".format(
mean_percent_value(cv2.bitwise_and(blob_mask, blob_mask, mask=upper_mask), img_ratio=0.5)))
print("\t{:.2f}% of the lower board.".format(
mean_percent_value(cv2.bitwise_and(blob_mask, blob_mask, mask=lower_mask), img_ratio=0.5)))
""" Find food """
food_list, food_mask, food_img = find_food(img, config['Min Food Size'], config['Low Food Color'], config['High Food Color'])
""" Print food information """
print("Total food discovered: " + str(len(food_list)))
# for i, food in enumerate(food_list):
# print("\tFood N°" + str(i) + ": " + str(food))
return img, blob_mask, blob, food_mask, food_img
def print_results(orig, blob_mask, blob, food_mask, food, discrete, scale=1.0, filename=None, hide=False):
padding = 35
nbr_width = 2
nbr_height = 3
font = cv2.FONT_HERSHEY_SIMPLEX
fontsize = 0.45
thickness = 1
scaled_height = int(orig.shape[0]*scale)
scaled_width = int(orig.shape[1]*scale)
pad = np.zeros((scaled_height, padding, orig.shape[2]), dtype=np.uint8)
line_pad = np.zeros((padding, (scaled_width + padding) * nbr_width + padding, orig.shape[2]), dtype=np.uint8)
print_img = cv2.resize(orig, (scaled_width, scaled_height))
middle = ((0, int(scaled_height/2)), (scaled_width, int(scaled_height/2)))
cv2.line(print_img, middle[0], middle[1], (0, 255, 0), thickness=1)
cv2.putText(print_img, 'Mid Line', (middle[0][0] + 5, middle[0][1] - 5),
font, fontsize, (0, 255, 0), thickness, cv2.LINE_AA)
print_blob_mask = cv2.resize(cv2.cvtColor(blob_mask, cv2.COLOR_GRAY2BGR), (scaled_width, scaled_height))
print_blob = cv2.resize(blob, (scaled_width, scaled_height))
print_food_mask = cv2.resize(cv2.cvtColor(food_mask, cv2.COLOR_GRAY2BGR), (scaled_width, scaled_height))
print_food = cv2.resize(food, (scaled_width, scaled_height))
print_discrete = cv2.resize(discrete, (scaled_width, scaled_height))
concat_line1 = np.concatenate((pad, print_img, pad, print_discrete, pad), axis=1)
concat_line2 = np.concatenate((pad, print_blob_mask, pad, print_blob, pad), axis=1)
concat_line3 = np.concatenate((pad, print_food_mask, pad, print_food, pad), axis=1)
aggregate = np.concatenate((line_pad, concat_line1, line_pad, concat_line2, line_pad, concat_line3, line_pad))
cv2.putText(aggregate, 'Original:',
(0 * (scaled_width + padding) + padding + 5, 0 * (scaled_height + padding) + padding - 5),
font, fontsize, (255, 255, 255), thickness, cv2.LINE_AA)
cv2.putText(aggregate, 'Discrete:',
(1 * (scaled_width + padding) + padding + 5, 0 * (scaled_height + padding) + padding - 5),
font, fontsize, (255, 255, 255), thickness, cv2.LINE_AA)
cv2.putText(aggregate, 'Blob Mask:',
(0 * (scaled_width + padding) + padding + 5, 1 * (scaled_height + padding) + padding - 5),
font, fontsize, (255, 255, 255), thickness, cv2.LINE_AA)
cv2.putText(aggregate, 'Blob:',
(1 * (scaled_width + padding) + padding + 5, 1 * (scaled_height + padding) + padding - 5),
font, fontsize, (255, 255, 255), thickness, cv2.LINE_AA)
cv2.putText(aggregate, 'Food Mask:',
(0 * (scaled_width + padding) + padding + 5, 2 * (scaled_height + padding) + padding - 5),
font, fontsize, (255, 255, 255), thickness, cv2.LINE_AA)
cv2.putText(aggregate, 'Food Regions:',
(1 * (scaled_width + padding) + padding + 5, 2 * (scaled_height + padding) + padding - 5),
font, fontsize, (255, 255, 255), thickness, cv2.LINE_AA)
if filename is not None:
cv2.imwrite(filename + ".jpg", aggregate)
if not hide:
cv2.imshow("Results", aggregate)
print("\nPress any key...")
cv2.waitKey(0)
def discretize(blob_img, food_mask, width, height):
img_height, img_width, _ = blob_img.shape
discrete_blob = cv2.resize(blob_img, (width, height), interpolation=cv2.INTER_NEAREST)
discrete_food = cv2.resize(food_mask, (width, height), interpolation=cv2.INTER_NEAREST)
discrete_food_list = []
for x in range(height):
for y in range(width):
if discrete_food[x, y] != 0:
discrete_food_list.append((y, x))
height, width, _ = discrete_blob.shape
discrete_blob = cv2.cvtColor(discrete_blob, cv2.COLOR_BGR2GRAY)
# If discrete blob has to be connected, used this :
# contours, hierarchy = cv2.findContours(discrete_blob, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
# c = max(contours, key=cv2.contourArea)
# mask = np.zeros(discrete_blob.shape, np.uint8)
# cv2.drawContours(mask, [c], -1, 255, cv2.FILLED)
# discrete_blob = cv2.bitwise_and(discrete_blob, discrete_blob, mask=mask)
discrete_blob_bgr = cv2.cvtColor(discrete_blob, cv2.COLOR_GRAY2BGR)
discrete_img = cv2.resize(discrete_blob_bgr, (0, 0), fx=10, fy=10, interpolation=cv2.INTER_NEAREST)
for (x, y) in discrete_food_list:
cv2.rectangle(discrete_img, (x * 10, y * 10), ((x + 1) * 10, (y + 1) * 10), (0, 255, 0), thickness=cv2.FILLED)
if discrete_blob[y, x] != 0:
cv2.drawMarker(discrete_img, (x * 10 + 5, y * 10 + 5), (255, 255, 255), thickness=2, markerSize=9,
markerType=cv2.MARKER_TILTED_CROSS)
return discrete_img, discrete_blob, discrete_food_list