def find_rects(image: np.ndarray) -> List[np.ndarray]:
"""Detects rectangular contours in an image
@returns list containing arrays of rect corners
"""
gray = cv.cvtColor(image.copy(), cv.COLOR_RGB2GRAY)
gray = cv.GaussianBlur(gray, (5, 5), 0)
#edged = cv.Canny(gray, 70, 180) # this numbers is hand picked guess from a few photos
edged = auto_canny(gray)
contours = cv.findContours(edged.copy(), cv.RETR_LIST,
cv.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
contours.sort(key=cv.contourArea, reverse=True)
contours = [cnt for cnt in contours if cv.contourArea(cnt) > 15
] # 15px contour area, basically cnt>=4x4
rects = list(map(cv.minAreaRect, contours))
boxes = list(map(lambda r: Rect.from_cvrect(*r[0], *r[1]), rects))
boxes = Rect.nms_merge(boxes)
return boxes or list()
def find_rects_white(image: np.ndarray) -> List[np.ndarray]:
"""Detects rectangular contours in an image
@returns list containing arrays of rect corners
"""
raise NotImplementedError()
#gray = norm_color_test(image.copy())
gray = cv.cvtColor(image.copy(), cv.COLOR_RGB2GRAY)
gray = cv.GaussianBlur(gray, (5, 5), 0)
#edged = cv.Canny(gray, 70, 180) # this numbers is hand picked guess from a few photos
edged = auto_canny(
gray) # this numbers is hand picked guess from a few photos
# split to HSV, then pick up rouhly any white color zeroing out the rest
hsv = cv.cvtColor(image.copy(), cv.COLOR_RGB2HSV)
h, s, v = cv.split(hsv)
h[h < 145] = 0
h[h > 165] = 0
#h = cv.GaussianBlur(h, (5, 5), 0)
normed = cv.normalize(h, None, 0, 255, cv.NORM_MINMAX, cv.CV_8UC1)
kernel = cv.getStructuringElement(shape=cv.MORPH_ELLIPSE, ksize=(5, 5))
opened = cv.morphologyEx(normed, cv.MORPH_OPEN, kernel)
# now find white regions contours
whites = cv.findContours(opened, cv.RETR_LIST, cv.CHAIN_APPROX_SIMPLE)[0]
whites.sort(key=cv.contourArea, reverse=True)
whites = [cnt for cnt in whites if cv.contourArea(cnt) > 15
] # 15px contour area, basically cnt>=4x4
whiterects = []
for i in whites:
rect = cv.minAreaRect(i)
w, h = rect[1]
if w * h > 150: # 150px area, or rougly 12x12 pixels
whiterects.append(rect)
#cv.drawContours(image, whites, -1, COLORS[2 % len(COLORS)], 2)
#cv.imshow('test', image)
#cv.waitKey()
whites = list(map(lambda r: Rect.from_cvrect(*r[0], *r[1]), whiterects))
#cv.imshow('test', edged)
#cv.waitKey()
contours = cv.findContours(edged.copy(), cv.RETR_LIST,
cv.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
contours.sort(key=cv.contourArea, reverse=True)
contours = [cnt for cnt in contours if cv.contourArea(cnt) > 15
] # 15px contour area, basically cnt>=4x4
rects = list(map(cv.minAreaRect, contours))
boxes = list(map(lambda r: Rect.from_cvrect(*r[0], *r[1]), rects))
# filter non overlapping contours
for i in reversed(range(len(boxes))):
overlaps = False
for wbox in whites:
if wbox.overlaps(boxes[i]):
overlaps = True
break
if not overlaps:
boxes.pop(i)
boxes = Rect.nms_merge(boxes)
for i in range(len(contours)):
#peri = cv.arcLength(contours[i], True)
#approx = cv.approxPolyDP(contours[i], 0.02 * peri, True)
rect = cv.minAreaRect(contours[i])
box = cv.boxPoints(rect)
box = np.int0(box)
#cv.drawContours(image, [box], -1, COLORS[i % len(COLORS)], 2)
#cv.putText(image, f'{i}: {cv.contourArea(contours[i])}px', (int(rect[0][0]), int(rect[0][1])), cv.FONT_HERSHEY_SIMPLEX, 0.6, COLORS[i % len(COLORS)], 1)
#cv.drawContours(image, contours, -1, COLORS[1], 2)
for b in boxes:
cv.line(image, (int(b.xmin), int(b.ymin)), (int(b.xmax), int(b.ymin)),
(0, 255, 255), 2)
cv.line(image, (int(b.xmin), int(b.ymax)), (int(b.xmax), int(b.ymax)),
(0, 255, 255), 2)
cv.line(image, (int(b.xmin), int(b.ymin)), (int(b.xmin), int(b.ymax)),
(0, 255, 255), 2)
cv.line(image, (int(b.xmax), int(b.ymin)), (int(b.xmax), int(b.ymax)),
(0, 255, 255), 2)
stacked = np.hstack((cv.cvtColor(edged, cv.COLOR_GRAY2RGB),
cv.cvtColor(opened, cv.COLOR_GRAY2RGB), image))
cv.namedWindow('test', 0)
cv.imshow('test', stacked)
cv.waitKey()
cv.imwrite('dump.jpg', stacked)
return boxes or list()