def detect_markers(img):
width, height, _ = img.shape
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray, 10, 100)
contours, hierarchy = cv2.findContours(edges.copy(), cv2.RETR_TREE,
cv2.CHAIN_APPROX_NONE)[-2:]
# We only keep the long enough contours
min_contour_length = min(width, height) / 50
contours = [
contour for contour in contours if len(contour) > min_contour_length
]
warped_size = 49
canonical_marker_coords = array(
((0, 0), (warped_size - 1, 0), (warped_size - 1, warped_size - 1),
(0, warped_size - 1)),
dtype='float32')
markers_list = []
for contour in contours:
approx_curve = cv2.approxPolyDP(contour, len(contour) * 0.01, True)
if not (len(approx_curve) == 4 and cv2.isContourConvex(approx_curve)):
continue
sorted_curve = array(cv2.convexHull(approx_curve, clockwise=False),
dtype='float32')
persp_transf = cv2.getPerspectiveTransform(sorted_curve,
canonical_marker_coords)
warped_img = cv2.warpPerspective(img, persp_transf,
(warped_size, warped_size))
warped_gray = cv2.cvtColor(warped_img, cv2.COLOR_BGR2GRAY)
_, warped_bin = cv2.threshold(warped_gray, 127, 255, cv2.THRESH_BINARY)
marker = warped_bin.reshape([
MARKER_SIZE, warped_size / MARKER_SIZE, MARKER_SIZE,
warped_size / MARKER_SIZE
])
marker = marker.mean(axis=3).mean(axis=1)
marker[marker < 127] = 0
marker[marker >= 127] = 1
try:
marker = validate_and_turn(marker)
hamming_code = extract_hamming_code(marker)
marker_id = int(decode(hamming_code), 2)
markers_list.append(
HammingMarker(id=marker_id, contours=approx_curve))
except ValueError:
continue
return markers_list
def detect_markers(img):
width, height, _ = img.shape
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray, 10, 100)
contours, hierarchy = cv2.findContours(edges.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
# We only keep the long enough contours
min_contour_length = min(width, height) / 50
contours = [contour for contour in contours if len(contour) > min_contour_length]
warped_size = 49
canonical_marker_coords = array(((0, 0),
(warped_size - 1, 0),
(warped_size - 1, warped_size - 1),
(0, warped_size - 1)),
dtype='float32')
markers_list = []
for contour in contours:
approx_curve = cv2.approxPolyDP(contour, len(contour) * 0.01, True)
if not (len(approx_curve) == 4 and cv2.isContourConvex(approx_curve)):
continue
sorted_curve = array(cv2.convexHull(approx_curve, clockwise=False),
dtype='float32')
persp_transf = cv2.getPerspectiveTransform(sorted_curve, canonical_marker_coords)
warped_img = cv2.warpPerspective(img, persp_transf, (warped_size, warped_size))
warped_gray = cv2.cvtColor(warped_img, cv2.COLOR_BGR2GRAY)
_, warped_bin = cv2.threshold(warped_gray, 127, 255, cv2.THRESH_BINARY)
marker = warped_bin.reshape(
[MARKER_SIZE, warped_size / MARKER_SIZE, MARKER_SIZE, warped_size / MARKER_SIZE]
)
marker = marker.mean(axis=3).mean(axis=1)
marker[marker < 127] = 0
marker[marker >= 127] = 1
try:
marker = validate_and_turn(marker)
hamming_code = extract_hamming_code(marker)
marker_id = int(decode(hamming_code), 2)
markers_list.append(HammingMarker(id=marker_id, contours=approx_curve))
except ValueError:
continue
return markers_list
def detect_markers(img, marker_size, camK):
width, height, _ = img.shape
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
[img_min, img_max, minLoc, maxLoc] = cv2.minMaxLoc(gray)
# cv2.imshow("gray", gray)
edges = cv2.Canny(gray, 50, 100)
# cv2.imshow("edges", edges)
# cv2.waitKey(1)
contours, hierarchy = cv2.findContours(edges, cv2.RETR_TREE,
cv2.CHAIN_APPROX_NONE)[-2:]
# We only keep the long enough contours
min_contour_length = min(width, height) / 10
contours = [
contour for contour in contours if len(contour) > min_contour_length
]
canonical_marker_coords = array(
((0, 0), (WARPED_SIZE - 1, 0), (WARPED_SIZE - 1, WARPED_SIZE - 1),
(0, WARPED_SIZE - 1)),
dtype='float32')
# imgc = img.copy()
# #cv2.drawContours(imgc, contours, -1, (0,255,0), 3)
# for cidx in range(1, len(contours)):
# cv2.drawContours(imgc, contours, cidx, (randint(0,255), randint(0,255), randint(0,255)), 3)
# cv2.imshow("contours", imgc)
# cv2.waitKey(1)
markers_list = []
# polydtct_counters = []
for contour in contours:
approx_curve = cv2.approxPolyDP(contour, len(contour) * 0.05, True)
if not (len(approx_curve) == 4 and cv2.isContourConvex(approx_curve)):
continue
sorted_curve = array(cv2.convexHull(approx_curve, clockwise=False),
dtype='float32')
# polydtct_counters.append(cv2.convexHull(approx_curve, clockwise=False))
# wrap image
persp_transf = cv2.getPerspectiveTransform(sorted_curve,
canonical_marker_coords)
warped_img = cv2.warpPerspective(img, persp_transf,
(WARPED_SIZE, WARPED_SIZE))
warped_gray = cv2.cvtColor(warped_img, cv2.COLOR_BGR2GRAY)
# check max and min pixel value in the wrapped image
# reject common unified color areas
[warp_min, warp_max, minLoc, maxLoc] = cv2.minMaxLoc(warped_gray)
if (warp_max - warp_min) / (img_max - img_min) < 0.3:
continue
# get a good threshold for binary operation,
# average of all pixels
wraped_gray_avg = cv2.mean(warped_gray)[0]
# binary image
_, warped_bin = cv2.threshold(warped_gray, wraped_gray_avg, 255,
cv2.THRESH_BINARY)
# # reshape to one block per pixel
# marker = warped_bin.reshape(
# [MARKER_SIZE, WARPED_SIZE / MARKER_SIZE, MARKER_SIZE, WARPED_SIZE / MARKER_SIZE]
# )
# # binary reshaped image
# marker = marker.mean(axis=3).mean(axis=1)
# marker[marker < 127] = 0
# marker[marker >= 127] = 1
# get better coding from sampling not reshaping
patch_size = WARPED_SIZE // MARKER_SIZE
patch_kenrel = np.ones(
(patch_size, patch_size), np.float32) / (patch_size * patch_size)
wraped_bin_filter = cv2.filter2D(warped_bin,
-1,
patch_kenrel,
borderType=cv2.BORDER_REPLICATE)
kernel_accept_thresh = 0.4 # < 0.5
marker = np.zeros((MARKER_SIZE, MARKER_SIZE))
read_marker_success = True
for i in range(1, MARKER_SIZE):
for j in range(1, MARKER_SIZE):
# # single pixel sampling
# if warped_bin[(i+0.5)*patch_size, (j+0.5)*patch_size] > 0:
# marker[i,j] = 1
# kernel sampling method
if wraped_bin_filter[int((i + 0.5) * patch_size),
int((j + 0.5) * patch_size)] > 256 * (
1 - kernel_accept_thresh):
marker[i, j] = 1
elif wraped_bin_filter[int(
(i + 0.5) * patch_size
), int((j + 0.5) * patch_size)] > 256 * kernel_accept_thresh:
read_marker_success = False
break
if not read_marker_success:
continue
# cv2.imshow("bin", warped_bin)
# cv2.waitKey(50)
# cv2.imshow("warped_marker", rot90(warped_bin, k=0))
# cv2.waitKey(50)
try:
# rotate marker by checking which corner is white
turn_number = validate_and_get_turn_number(marker)
marker = rot90(marker, k=turn_number)
# get id
hamming_code = extract_hamming_code(marker)
marker_id = int(decode(hamming_code), 2)
except ValueError:
continue
# rotate corner list
rotated_contour = rotate_contour(sorted_curve, persp_transf,
turn_number)
detected_marker = HammingMarker(id=marker_id,
contours=rotated_contour,
size=marker_size)
# get pose and update detected marker
pose_results = get_marker_pose(detected_marker, detected_marker.size,
camK)
if pose_results[0]:
detected_marker.rvec = pose_results[1]
detected_marker.tvec = pose_results[2]
else:
# cannot find pose using contours
continue
markers_list.append(detected_marker)
# imgpoly = img.copy()
# for cidx in range(1, len(polydtct_counters)):
# cv2.drawContours(imgpoly, polydtct_counters, cidx, (randint(0,255), randint(0,255), randint(0,255)), 3)
# cv2.imshow("contours poly", imgpoly)
# cv2.waitKey(1)
return markers_list
def detect_markers(img, marker_size, camK):
width, height, _ = img.shape
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#normalize image, map brightest pixel to 255 and smalles pixel val to 0.
'''
[minVal, maxVal, minLoc, maxLoc] = cv2.minMaxLoc(gray)
mult = 255/(maxVal-minVal)
normalized_image = gray - minVal
normalized_image = np.uint8(mult * normalized_image)
'''
edges = cv2.Canny(gray, 10, 100)
#cv2.imshow("edges", edges)
#cv2.waitKey(1)
contours, hierarchy = cv2.findContours(edges.copy(), cv2.RETR_TREE,
cv2.CHAIN_APPROX_NONE)[-2:]
# We only keep the long enough contours
min_contour_length = min(width, height) / 50
contours = [
contour for contour in contours if len(contour) > min_contour_length
]
canonical_marker_coords = array(
((0, 0), (WARPED_SIZE - 1, 0), (WARPED_SIZE - 1, WARPED_SIZE - 1),
(0, WARPED_SIZE - 1)),
dtype='float32')
markers_list = []
for contour in contours:
approx_curve = cv2.approxPolyDP(contour, len(contour) * 0.01, True)
if not (len(approx_curve) == 4 and cv2.isContourConvex(approx_curve)):
continue
sorted_curve = array(cv2.convexHull(approx_curve, clockwise=False),
dtype='float32')
persp_transf = cv2.getPerspectiveTransform(sorted_curve,
canonical_marker_coords)
warped_img = cv2.warpPerspective(img, persp_transf,
(WARPED_SIZE, WARPED_SIZE))
warped_gray = cv2.cvtColor(warped_img, cv2.COLOR_BGR2GRAY)
_, warped_bin = cv2.threshold(warped_gray, 127, 255, cv2.THRESH_BINARY)
marker = warped_bin.reshape([
MARKER_SIZE, WARPED_SIZE // MARKER_SIZE, MARKER_SIZE,
WARPED_SIZE // MARKER_SIZE
])
marker = marker.mean(axis=3).mean(axis=1)
marker[marker < 127] = 0
marker[marker >= 127] = 1
try:
# rotate marker by checking which corner is white
turn_number = validate_and_get_turn_number(marker)
marker = rot90(marker, k=turn_number)
#cv2.imshow("bin", warped_bin)
#cv2.waitKey(10)
#cv2.imshow("warped_marker", rot90(warped_bin, k=turn_number))
#cv2.waitKey(10)
# get id
hamming_code = extract_hamming_code(marker)
marker_id = int(decode(hamming_code), 2)
except ValueError:
continue
# rotate corner list
rotated_contour = rotate_contour(sorted_curve, persp_transf,
turn_number)
detected_marker = HammingMarker(id=marker_id,
contours=rotated_contour,
size=marker_size)
# get pose and update detected marker
pose_results = get_marker_pose(detected_marker, detected_marker.size,
camK)
if pose_results[0]:
detected_marker.rvec = pose_results[1]
detected_marker.tvec = pose_results[2]
else:
# cannot find pose using contours
continue
markers_list.append(detected_marker)
return markers_list