def adjust_left(processed_seg, seg, verbose=False):
print(" SEG ID ", seg.id)
black_count = 0
margin_l = 10
i = 0
while black_count < 90:
i += 1
if black_count == 0:
margin_l += 10
else:
margin_l += 5
print(" margin_l ", margin_l, " i ", i, " SEG ID ", seg.id)
if seg.id != 10:
left_seg = processed_seg[35:, 10:margin_l]
else:
left_seg = processed_seg[55:, 10:margin_l]
black_count = np.array(left_seg == 0).sum()
print(" Black count ", black_count)
if verbose and margin_l > 10:
name_wondiow = "Left Margin{} , segID {}".format(i, seg.id)
left_seg = processed_seg[35:, 10:margin_l]
show_img(name_wondiow, left_seg, divisor=1)
print("margin_l F before rectif ", margin_l)
margin_l -= 5
seg.adjust_left_border(margin_l)
processed_seg[:, :margin_l] = 255
return processed_seg
def adjust_right(processed_seg, seg, verbose=False):
black_count = 0
margin_r = -10
i = 0
while black_count < 90:
i += 1
if black_count == 0:
margin_r += 10
else:
margin_r += 5
right_seg = processed_seg[20:, processed_seg.shape[1] - margin_r:]
black_count = np.array(right_seg == 0).sum()
print(" Black count R", black_count)
if verbose and margin_r > 0:
name_wondiow = "Right Margin{}".format(i)
right_seg = processed_seg[20:, processed_seg.shape[1] - margin_r:]
show_img(name_wondiow, right_seg, divisor=1)
print("margin_r ", margin_r,
processed_seg.shape[1] - (processed_seg.shape[1] - margin_r))
margin_r -= 15
print("margin_r RECTIF ", margin_r,
processed_seg.shape[1] - (processed_seg.shape[1] - margin_r))
seg.adjust_right_border(margin_r)
processed_seg[:, processed_seg.shape[1] - margin_r:] = 255
return processed_seg
def adjust_bottom(processed_seg, seg, verbose=False):
print("Bottom ")
black_count = 0
margin_b = -5
i = 0
while black_count < 90:
#print("margin_b ", margin_b, 'i ' , i)
i += 1
if black_count == 0:
margin_b += 10
else:
margin_b += 5
if verbose and margin_b > 0:
#print("i ", i)
name_wondiow = "Bottom Margin {}".format(i)
bottom_seg = processed_seg[processed_seg.shape[0] - margin_b:, :]
show_img(name_wondiow, bottom_seg, divisor=1)
bottom_seg = processed_seg[processed_seg.shape[0] - margin_b:, :]
black_count = np.array(bottom_seg == 0).sum()
print("BOTTOM black_count ", black_count)
print("margin_b ", margin_b)
margin_b -= 5
print("margin_b RECTIF ", margin_b)
seg.adjust_bottom_border(margin_b)
print("bottom seg ", seg.bottom_border)
processed_seg[processed_seg.shape[0] -
margin_b:processed_seg.shape[0], :] = 255
return processed_seg
def run_pipeline(self, file_path, show_io=False, create_rgb_img=False):
"""Run pipeline
This method is used by the backend to invoke the cv pipeline and
process one image taken by the Basler camera system.
:param file_path: Path to the file in the filesystem
:param show_io: For debugging purposes you can show the input and
output
:return: Returns segments for the evaluation using labeled data
"""
# reset image, segments and variables
self.reset_vars()
# load new image
self.set_img(file_path)
if create_rgb_img:
self.rgb_img = cv.cvtColor(self.gray_img, cv.COLOR_GRAY2BGR)
if show_io:
show_img("Input Image", self.rgb_img, divisor=4)
self.set_segs_fixed_bounding_boxes(use_polygons=True, verbose=True)
for seg in self.segments:
# 3. segment processing
self.process_segments(seg, verbose=False)
# # 4. outliers top
# self.filter_outliers_top(seg, y_range=25, threshold=4,
# verbose=False)
# # 5. outliers left right
# self.filter_outliers_left_right(seg, x_range=30, threshold=1,
# verbose=False)
# show detected diamonds
if show_io:
# draws detected diamonds on input image for frontend
for seg in self.segments:
# self.draw_detected_diamonds(seg)
for diamond in seg.diamonds:
draw_circle(self.rgb_img, diamond.x, diamond.y, 5,
consts.GREEN)
show_img("Final Output", self.rgb_img, divisor=4)
return self.segments
def process_segments(self,
seg,
inner_seg=None,
verbose=False,
verbose_seg=False):
"""Process detected segments
This method processes the detected segments using morphological
operations, multiotsu thresholding and DBSCAN.
:param seg: the segment that is being processed
:param verbose: Activate debug windows to show what's happening here
:return: No return
"""
log.debug("SEG ID = %i" % seg.id)
self.gray_seg = self.gray_img[seg.y:seg.y + seg.h, seg.x:seg.x + seg.w]
# f_gray_seg = self.gray_seg.flatten()
# plt.hist(f_gray_seg, bins =list(range(np.amin(f_gray_seg),np.amax(f_gray_seg),2 )))
# plt.title("histogram")
# plt.savefig("hist.png")
print("\n \n MIN ", np.amin(self.gray_seg), " MAX ",
np.amax(self.gray_seg))
if self.use_polygons:
# remove reflections
min_val_pixel = np.min(self.gray_seg)
# self.gray_seg[self.gray_seg == 255] = min_val_pixel
if min_val_pixel > 0:
self.gray_seg[self.gray_seg >= 120] = min_val_pixel
else:
self.gray_seg[self.gray_seg >= 120] = 1
if min_val_pixel > 0:
upper_gray = self.gray_seg[:round(self.gray_seg.shape[0] /
2), :]
upper_gray[upper_gray >= 80] = min_val_pixel
self.gray_seg[:round(self.gray_seg.shape[0] /
2), :] = upper_gray
else:
upper_gray = self.gray_seg[:round(self.gray_seg.shape[0] / 2)]
upper_gray[upper_gray >= 80] = 1
self.gray_seg[:round(self.gray_seg.shape[0] /
2), :] = upper_gray
if min_val_pixel > 0:
gray_23 = self.gray_seg[
round(self.gray_seg.shape[0] /
2):round((self.gray_seg.shape[0] / 3) * 2), :]
gray_23[gray_23 >= 100] = min_val_pixel
self.gray_seg[round(self.gray_seg.shape[0] /
2):round((self.gray_seg.shape[0] / 3) *
2), :] = gray_23
else:
gray_23 = self.gray_seg[
round(self.gray_seg.shape[0] /
2):round((self.gray_seg.shape[0] / 3) * 2), :]
gray_23[gray_23 >= 100] = 1
self.gray_seg[round(self.gray_seg.shape[0] /
2):round((self.gray_seg.shape[0] / 3) *
2), :] = gray_23
#show_img("00 gray_seg remove white pixel", self.gray_seg, 1)
# inner_seg.pts -= inner_seg.pts.min(axis=0)
# mask = np.zeros(self.gray_seg.shape[:2], dtype=np.uint8)
# show_img("mask", mask, 1)
# cv.drawContours(mask, [inner_seg.pts], -1, (255, 255, 255), -1,
# cv.LINE_AA)
# self.gray_seg[self.gray_seg >= 250] = 1
# self.gray_seg = cv.bitwise_and(self.gray_seg, self.gray_seg,
# mask=mask)
# show_img("0 gray_seg", self.gray_seg, 1)
# # self.gray_seg[self.gray_seg == 0] = 255
# bg = np.ones_like(self.gray_seg, dtype=np.uint8) * 255
# cv.bitwise_not(bg, bg, mask=mask)
# self.gray_seg = cv.bitwise_xor(self.gray_seg, bg)
# show_img("1 gray_seg", self.gray_seg, 1)
# show_img("bg", bg, 1)
#show_img("1 OUTER gray_seg", self.gray_seg, 1)
# self.gray_seg += bg
#
if verbose:
self.rgb_seg = cv.cvtColor(self.gray_seg, cv.COLOR_GRAY2BGR)
show_img("0: Input segment", self.gray_seg, divisor=1)
# remove reflections
# # ToDo moved this out of here
# min_val_pixel = np.min(self.gray_seg)
# # self.gray_seg[self.gray_seg == 255] = min_val_pixel
# self.gray_seg[self.gray_seg == 255] = min_val_pixel
# if verbose:
# show_img("1: Removed reflections", self.gray_seg, divisor=1)
# median blur
# processed_seg = cv.medianBlur(self.gray_seg, 11)
#processed_seg = cv.medianBlur(self.gray_seg, 11)
processed_seg = cv.medianBlur(self.gray_seg, 11)
upper_upper_blur = processed_seg[:12, :]
upper_upper_blur = cv.medianBlur(upper_upper_blur, 25)
processed_seg[0:12, :] = upper_upper_blur
upper_blur = processed_seg[:12, :]
upper_upper_blur = cv.medianBlur(upper_upper_blur, 25)
processed_seg[0:12, :] = upper_upper_blur
#cv.rectangle(processed_seg, (0 ,0),
# ( processed_seg.shape[1], 12 ),
# consts.YELLOW, thickness=1)
upper_blur = processed_seg[12:30, :]
upper_blur = cv.medianBlur(upper_blur, 3)
processed_seg[12:30, :] = upper_blur
# cv.rectangle(processed_seg, (0,12),
# (processed_seg.shape[1] , 60),
# consts.RED, thickness=1)
if verbose:
show_img("2: Blurred segment", processed_seg, divisor=1)
# # remove "salt" noise
# opening_kernel = cv.getStructuringElement(
# cv.MORPH_ELLIPSE,
# (self.seg_opening_kern_size, self.seg_opening_kern_size))
# processed_seg = cv.morphologyEx(self.gray_seg, cv.MORPH_OPEN,
# opening_kernel,
# iterations=3)
# if verbose:
# show_img("3: Opening after input", processed_seg,
# divisor=1)
# denoise
cv.fastNlMeansDenoising(processed_seg,
processed_seg,
h=5,
templateWindowSize=7,
searchWindowSize=21)
# test_seg2 = cv.fastNlMeansDenoising(processed_seg, None, h=10,
# templateWindowSize=5,
# searchWindowSize=21)
if verbose:
show_img("3: Denoised image", processed_seg, divisor=1)
# show_img("3.1: Denoised image high h", test_seg2, divisor=1)
# multi otsu thresholding
thresholds = threshold_multiotsu(processed_seg, classes=5)
#thresholds2 = threshold_multiotsu(processed_seg, classes=6)
print("\n \n thresholds : ", thresholds, thresholds)
# thresholds[0] -= 3
processed_seg = np.uint8(np.digitize(processed_seg, bins=thresholds))
processed_seg[processed_seg == 0] = 0
processed_seg[processed_seg == 1] = 49
processed_seg[processed_seg == 2] = 99
processed_seg[processed_seg == 3] = 255
processed_seg[processed_seg == 4] = 255
processed_seg[processed_seg == 5] = 255
processed_seg[processed_seg == 6] = 255
processed_seg = adjust_right(processed_seg, seg, False)
processed_seg = adjust_left(processed_seg, seg, False)
processed_seg = adjust_bottom(processed_seg, seg, False)
if verbose:
show_img("5: Multi Otsu", processed_seg, divisor=1)
# # dilate with radial kernel
# radial_kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE, (3, 3))
# # processed_seg = cv.dilate(processed_seg, radial_kernel,
# # iterations=1)
# processed_seg = cv.morphologyEx(processed_seg, cv.MORPH_OPEN,
# radial_kernel, iterations=3)
# if verbose:
# show_img("6: Openend output", processed_seg, divisor=1)
#
# closing_kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE, (3, 3))
# processed_seg = cv.morphologyEx(processed_seg, cv.MORPH_CLOSE,
# closing_kernel, iterations=4)
# if verbose:
# show_img("7: closing after otsu", processed_seg, divisor=1)
if verbose:
ths = [
i for i in range(self.bd_minThreshold, self.bd_maxThreshold,
self.bd_thresholdStep)
]
for th in ths:
_, img = cv.threshold(processed_seg, th, 255, cv.THRESH_BINARY)
show_img("threshold for %s" % str(th), img, divisor=1)
# # create border for diamonds close to segment edge
border = self.border # in pixels
if border:
processed_seg = cv.copyMakeBorder(processed_seg,
border,
border,
border,
border,
borderType=cv.BORDER_CONSTANT,
value=255)
if verbose:
show_img("7: With border", processed_seg, divisor=1)
# Blob detection
blob_keypts = self.blob_detector_seg.detect(processed_seg)
log.debug(" detected %i blobs" % len(blob_keypts))
for keypt in blob_keypts:
seg.diamonds.append(
Blob(int(keypt.pt[0] - border + seg.x),
int(keypt.pt[1] - border + seg.y), seg.id))
if verbose_seg:
print("SEG INFO / ", seg.id, seg.x, seg.y, seg.left_border,
seg.right_border)
cv.rectangle(self.rgb_img, (seg.x + seg.left_border, seg.y),
(seg.x + seg.w - seg.right_border, seg.y + seg.h),
consts.BLUE,
thickness=1)
if verbose:
for diamond in seg.diamonds:
draw_circle(self.rgb_img, diamond.x, diamond.y, 5,
consts.GREEN)
show_img("Current detection", self.rgb_img, divisor=5)
cv.waitKey()
def process_segments(self, seg, verbose=False):
"""Process detected segments
This method processes the detected segments using morphological
operations, multiotsu thresholding and DBSCAN.
:param seg: the segment that is being processed
:param verbose: Activate debug windows to show what's happening here
:return: No return
"""
log.debug("SEG ID = %i" % seg.id)
self.gray_seg = self.gray_img[seg.y:seg.y + seg.h, seg.x:seg.x + seg.w]
if self.use_polygons:
# remove reflections
min_val_pixel = np.min(self.gray_seg)
# self.gray_seg[self.gray_seg == 255] = min_val_pixel
if min_val_pixel > 0:
self.gray_seg[self.gray_seg >= 250] = min_val_pixel
else:
self.gray_seg[self.gray_seg >= 250] = 1
seg.pts -= seg.pts.min(axis=0)
mask = np.zeros(self.gray_seg.shape[:2], dtype=np.uint8)
show_img("mask", mask, 1)
cv.drawContours(mask, [seg.pts], -1, (255, 255, 255), -1,
cv.LINE_AA)
self.gray_seg = cv.bitwise_and(self.gray_seg,
self.gray_seg,
mask=mask)
show_img("gray_seg", self.gray_seg, 1)
# self.gray_seg[self.gray_seg == 0] = 255
bg = np.ones_like(self.gray_seg, dtype=np.uint8) * 255
cv.bitwise_not(bg, bg, mask=mask)
self.gray_seg = cv.bitwise_xor(self.gray_seg, bg)
show_img("gray_seg2", self.gray_seg, 1)
show_img("bg", bg, 1)
# self.gray_seg += bg
if verbose:
self.rgb_seg = cv.cvtColor(self.gray_seg, cv.COLOR_GRAY2BGR)
show_img("0: Input segment", self.gray_seg, divisor=1)
# remove reflections
# # ToDo moved this out of here
# min_val_pixel = np.min(self.gray_seg)
# # self.gray_seg[self.gray_seg == 255] = min_val_pixel
# self.gray_seg[self.gray_seg == 255] = min_val_pixel
# if verbose:
# show_img("1: Removed reflections", self.gray_seg, divisor=1)
# median blur
# processed_seg = cv.medianBlur(self.gray_seg, 11)
processed_seg = cv.medianBlur(self.gray_seg, 11)
# processed_seg = cv.GaussianBlur(self.gray_seg, (11,11), 5)
if verbose:
show_img("2: Blurred segment", processed_seg, divisor=1)
# # remove "salt" noise
# opening_kernel = cv.getStructuringElement(
# cv.MORPH_ELLIPSE,
# (self.seg_opening_kern_size, self.seg_opening_kern_size))
# processed_seg = cv.morphologyEx(self.gray_seg, cv.MORPH_OPEN,
# opening_kernel,
# iterations=3)
# if verbose:
# show_img("3: Opening after input", processed_seg,
# divisor=1)
# denoise
cv.fastNlMeansDenoising(processed_seg,
processed_seg,
h=5,
templateWindowSize=7,
searchWindowSize=21)
# test_seg2 = cv.fastNlMeansDenoising(processed_seg, None, h=10,
# templateWindowSize=5,
# searchWindowSize=21)
if verbose:
show_img("3: Denoised image", processed_seg, divisor=1)
# show_img("3.1: Denoised image high h", test_seg2, divisor=1)
# multi otsu thresholding
print("HERE")
thresholds = skimage.filters.threshold_multiotsu(processed_seg,
classes=5)
# thresholds[0] -= 3
processed_seg = np.uint8(np.digitize(processed_seg, bins=thresholds))
processed_seg[processed_seg == 0] = 0
processed_seg[processed_seg == 1] = 49
processed_seg[processed_seg == 2] = 99
processed_seg[processed_seg == 3] = 255
processed_seg[processed_seg == 4] = 255
processed_seg[processed_seg == 5] = 255
processed_seg[processed_seg == 6] = 255
if verbose:
show_img("5: Multi Otsu", processed_seg, divisor=1)
# # dilate with radial kernel
# radial_kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE, (3, 3))
# # processed_seg = cv.dilate(processed_seg, radial_kernel,
# # iterations=1)
# processed_seg = cv.morphologyEx(processed_seg, cv.MORPH_OPEN,
# radial_kernel, iterations=3)
# if verbose:
# show_img("6: Openend output", processed_seg, divisor=1)
#
# closing_kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE, (3, 3))
# processed_seg = cv.morphologyEx(processed_seg, cv.MORPH_CLOSE,
# closing_kernel, iterations=4)
# if verbose:
# show_img("7: closing after otsu", processed_seg, divisor=1)
if verbose:
ths = [
i for i in range(self.bd_minThreshold, self.bd_maxThreshold,
self.bd_thresholdStep)
]
for th in ths:
_, img = cv.threshold(processed_seg, th, 255, cv.THRESH_BINARY)
show_img("threshold for %s" % str(th), img, divisor=1)
# # create border for diamonds close to segment edge
border = self.border # in pixels
if border:
processed_seg = cv.copyMakeBorder(processed_seg,
border,
border,
border,
border,
borderType=cv.BORDER_CONSTANT,
value=255)
if verbose:
show_img("7: With border", processed_seg, divisor=1)
# Blob detection
blob_keypts = self.blob_detector_seg.detect(processed_seg)
log.debug(" detected %i blobs" % len(blob_keypts))
for keypt in blob_keypts:
seg.diamonds.append(
Blob(int(keypt.pt[0] - border + seg.x),
int(keypt.pt[1] - border + seg.y), seg.id))
if verbose:
for diamond in seg.diamonds:
draw_circle(self.rgb_img, diamond.x, diamond.y, 5,
consts.GREEN)
show_img("Current detection", self.rgb_img, divisor=5)
cv.waitKey()