def detect_markers(img, area_thresh=100):
"""
This is the main function for detecting markers in an image.
Input:
img: a color or grayscale image that may or may not contain a marker.
Output:
a list of found markers. If no markers are found, then it is an empty list.
"""
#cv2.namedWindow('watcher', cv2.WINDOW_NORMAL)
if len(img.shape) > 2:
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = img.copy()
width, height = img.shape
img = cv2.Canny(img, 100, 255)
#M = np.ones((2, 2), np.uint8)
#img = cv2.dilate(img, M, iterations=1)
contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)[-2:]
# We only keep the long enough contours
min_contour_length = width * height / area_thresh
markers_list = []
for contour in contours:
rect = cv2.minAreaRect(contour)
if rect[1][0] * rect[1][1] <= min_contour_length:
continue
box = cv2.boxPoints(rect)
box = np.int0(box)
warped_gray = four_point_transform(gray, box)
umbral = 120
_, warped_bin = cv2.threshold(warped_gray, umbral, 255,
cv2.THRESH_BINARY)
marker = cv2.resize(warped_bin, (7, 7), interpolation=cv2.INTER_LINEAR)
#cv2.imshow('watcher', marker)
#cv2.waitKey(0)
marker[marker < 255] = 0
marker[marker == 255] = 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=box))
except ValueError:
continue
return markers_list
def detect_markers_integrated(img, contours, area_thresh=100):
"""
This is the main function for detecting markers in an image.
Input:
img: a color or grayscale image that may or may not contain a marker.
Output:
a list of found markers. If no markers are found, then it is an empty list.
"""
if len(img.shape) > 2:
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
else:
gray = img.copy()
width, height = gray.shape
# We only keep the long enough contours
min_contour_length = width * height / area_thresh
markers_list = []
new_contours = []
for contour in contours:
rect = cv2.minAreaRect(contour)
if rect[1][0] * rect[1][1] <= min_contour_length:
new_contours.append(contour)
continue
box = cv2.boxPoints(rect)
box = np.int0(box)
if np.min(box) < 0 or np.max(box[:, 0]) > gray.shape[1] or np.max(
box[:, 1]) > gray.shape[0]:
new_contours.append(contour)
continue
warped_gray = four_point_transform(gray, box)
umbral = 110
_, warped_bin = cv2.threshold(warped_gray, umbral, 255,
cv2.THRESH_BINARY)
marker = cv2.resize(warped_bin, (7, 7), interpolation=cv2.INTER_LINEAR)
marker[marker < 255] = 0
marker[marker == 255] = 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=box))
except ValueError:
new_contours.append(contour)
continue
return [markers_list, new_contours]
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):
"""
This is the main function for detecting markers in an image.
Input:
img: a color or grayscale image that may or may not contain a marker.
Output:
a list of found markers. If no markers are found, then it is an empty list.
"""
if len(img.shape) > 2:
width, height, _ = img.shape
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
else:
width, height = img.shape
gray = img
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))
# do i really need to convert twice?
if len(warped_img.shape) > 2:
warped_gray = cv2.cvtColor(warped_img, cv2.COLOR_BGR2GRAY)
else:
warped_gray = warped_img
_, 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
global marker_id
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