def blob_detection(img):
# 初始化参数
params = cv2.SimpleBlobDetector_Params()
params.minArea = 15
params.maxArea = 500
params.blobColor = 000
params.minCircularity = 10
params.maxCircularity = 10000
ver = (cv2.__version__).split('.')
if int(ver[0]) < 3:
detector = cv2.SimpleBlobDetector(params)
else:
detector = cv2.SimpleBlobDetector_create(params)
# detector = cv2.SimpleBlobDetector(params)
# 检测斑点
blobs = detector.detect(img)
blobs_num = len(blobs)
return blobs_num
def find_blobs(frame): params = cv2.SimpleBlobDetector_Params() params.filterByArea = True params.minArea = 150 params.filterByCircularity = False params.filterByConvexity = False params.filterByInertia = False detector = cv2.SimpleBlobDetector(params) keypoints = detector.detect(frame) counter = 0 for keyPoint in keypoints: counter = counter + 1 x = keyPoint.pt[0] y = keyPoint.pt[1] cv2.putText(frame,str(counter), (int(x),int(y+25)), cv2.FONT_HERSHEY_SIMPLEX, 1, 150) s = keyPoint.size #print x,y,s im_with_keypoints = cv2.drawKeypoints(frame, keypoints, np.array([]), (255,0,0), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS) return im_with_keypoints
def main():
camera = picamera.PiCamera()
camera.capture('img.jpg')
img = cv2.imread('img.jpg')
original = img.copy()
click_list = getXY(img)
cropped = crop_img(original, click_list)
display_image(cropped)
lower, upper = myStats(cropped)
print lower
print upper
f_image = color_in_image(original, [lower, upper])
detector = cv2.SimpleBlobDetector()
keypoints = detector.detect(f_image)
im_with_keypoints = cv2.drawKeypoints(
im, keypoints, np.array([]), (255, 255, 255),
cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
def deteccaoDeBlobs(img):
#img= cv2.imread('untitled.png',cv2.IMREAD_GRAYSCALE)
params = cv2.SimpleBlobDetector_Params()
#img = cv2.bitwise_not(img)
params.minDistBetweenBlobs = 10
params.filterByColor = True
params.blobColor = 255
params.filterByCircularity = False
params.filterByConvexity = False
params.filterByInertia = False
params.filterByArea = True #
params.minArea = 1 #
params.maxArea = 100000 #
ver = (cv2.__version__).split('.')
if int(ver[0]) < 3:
detector = cv2.SimpleBlobDetector(params)
else:
detector = cv2.SimpleBlobDetector_create(params)
keypoints = detector.detect(img)
print(type(keypoints))
keypoints = list(reversed(keypoints))
#np.invert(keypoints)
for i in keypoints:
im_with_keypoints = cv2.drawKeypoints(
img, [i], np.array([]), (0, 0, 255),
cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
print("################")
print(i.class_id)
print(i.pt)
objectCentroid = i.pt
print("################")
break
black = np.zeros((540, 960, 3))
#print(im_with_keypoints.size)
#im_with_keypoints = cv2.drawKeypoints(img, keypoints, np.array([]), (0,0,255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
cv2.imshow("Keypoints", im_with_keypoints)
cv2.waitKey(0)
def create_blob_detector(roi_size=(128, 128), blob_min_area=3,
blob_min_int=.5, blob_max_int=.95, blob_th_step=10):
params = cv2.SimpleBlobDetector_Params()
params.filterByArea = True
params.minArea = blob_min_area
params.maxArea = roi_size[0]*roi_size[1]
params.filterByCircularity = False
params.filterByColor = False
params.filterByConvexity = False
params.filterByInertia = False
# blob detection only works with "uint8" images.
params.minThreshold = int(blob_min_int*255)
params.maxThreshold = int(blob_max_int*255)
params.thresholdStep = blob_th_step
ver = (cv2.__version__).split(".")
if int(ver[0]) < 3:
return cv2.SimpleBlobDetector(params)
else:
return cv2.SimpleBlobDetector_create(params)
def getStarBlobs(im):
im = cv2.erode(im, np.ones((2, 2)))
height, width = im.shape[:2]
area = height * width
# Setup SimpleBlobDetector parameters.
params = cv2.SimpleBlobDetector_Params()
# Change thresholds
params.minThreshold = cv2.getTrackbarPos('minThr', 'image')
params.maxThreshold = cv2.getTrackbarPos('maxThr', 'image')
# Filter by Area.
params.filterByArea = True
#10000
#500
params.minArea = area / (cv2.getTrackbarPos('minAre', 'image') + 1)
params.maxArea = area / (cv2.getTrackbarPos('maxAre', 'image') + 1)
# Filter by Circularity
params.filterByCircularity = True
params.minCircularity = cv2.getTrackbarPos('minCir', 'image')
params.maxCircularity = cv2.getTrackbarPos('maxCir', 'image')
# Filter by Convexity
params.filterByConvexity = False
params.minConvexity = cv2.getTrackbarPos('minCon', 'image')
params.maxConvexity = cv2.getTrackbarPos('maxCon', 'image')
# Filter by Inertia
params.filterByInertia = True
params.minInertiaRatio = cv2.getTrackbarPos('minIne', 'image')
ver = (cv2.__version__).split('.')
if int(ver[0]) < 3:
detector = cv2.SimpleBlobDetector(params)
else:
detector = cv2.SimpleBlobDetector_create(params)
keypoints = detector.detect(im)
print("detected")
return keypoints #,im
def countcluster(picture):
im = cv2.cvtColor(picture, cv2.COLOR_RGB2GRAY)
kernel = np.ones((3, 5), np.uint8)
im = cv2.erode(im, kernel, iterations=2)
# Setup SimpleBlobDetector parameters.
params = cv2.SimpleBlobDetector_Params()
# filter by area
params.filterByArea = True
params.minArea = 2
# filter by color
params.filterByColor = True
params.blobColor = 255
# Filter by Convexity
params.filterByConvexity = True
params.minConvexity = 0.001
# Filter by Inertia
params.filterByInertia = True
params.minInertiaRatio = .001
# Create a detector with the parameters
ver = (cv2.__version__).split('.')
if int(ver[0]) < 3:
detector = cv2.SimpleBlobDetector(params)
else:
detector = cv2.SimpleBlobDetector_create(params)
# Detect blobs.
keypoints = detector.detect(im)
#dummy
#im_with_keypoints = cv2.drawKeypoints(im, keypoints, np.array([]), (0, 0, 255),
# cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
# Show keypoints
#cv2.imwrite("keypoints.jpg", im_with_keypoints)
#cv2.imshow("keypoint", im_with_keypoints)
#cv2.waitKey(0)
#print("Total of objects")
#print(len(keypoints))
# dummy
if len(keypoints) > 6:
return 1
else:
return 0
def detect_electrodes(self, kernel=None):
self._kernel = 15 if kernel is None else kernel
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,
(self._kernel, self._kernel))
mask_closed = cv2.morphologyEx(self._final_mask, cv2.MORPH_CLOSE,
kernel)
mask_clean = cv2.morphologyEx(mask_closed, cv2.MORPH_OPEN, kernel)
_, mask = self.find_electrodes(mask_clean)
self._overlay = self.overlay_mask(mask_clean)
params = self.some_parameters()
ver = cv2.__version__.split('.')
if int(ver[0]) < 3:
detector = cv2.SimpleBlobDetector(params)
else:
detector = cv2.SimpleBlobDetector_create(params)
masked_data = cv2.bitwise_and(self._overlay,
self._overlay,
mask=mask_clean)
key_points = detector.detect(masked_data)
# temp = [21, 23, 31, 38, 47, 56, 59, 63]
temp = [
3, 4, 5, 7, 12, 13, 15, 20, 23, 31, 39, 45, 46, 47, 48, 52, 54, 55,
56, 57, 62, 63
]
self._detected_electrodes = np.asarray(
[key_point.pt for key_point in key_points])
i, j = self._create_grid()
self._electrode_mesh = np.array((i.ravel(), j.ravel())).T
self._full_detected_electrodes = self._optimize(visualise=True)
final_grid = np.zeros((64, 2))
final_grid[:self._detected_electrodes.
shape[0]] = self._detected_electrodes
ct = 1
for pt in range(len(final_grid)):
if pt in temp:
final_grid[self._detected_electrodes.shape[0] - 1 +
ct] = self._full_detected_electrodes[pt]
ct += 1
return self._full_detected_electrodes, 0.0
def blob_detection(image):
"""
Takes color image and detects blobs
:param image: color image 1920x1080 from camera
:return: image with written blobs
"""
import cv2
import numpy as np
# Convert to Gray
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# DETECTOR PARAMETERS
params = cv2.SimpleBlobDetector_Params()
# Change threshold
params.minThreshold = 10;
params.maxThreshold = 200;
# Filter by Area
params.filterByArea = True
params.minArea = 1500
# Filter by Circularity
params.filterByCircularity = True
params.minCircularity = 0.1
# Filter by Convexity
params.filterByConvexity = True
params.minConvexity = 0.87
# Filter by Inertia
params.filterByInertia = True
params.minInertiaRatio = 0.01
# Set detector with custom parameters
ver = (cv2.__version__).split('.')
if int(ver[0]) < 3:
detector = cv2.SimpleBlobDetector(params)
else:
detector = cv2.SimpleBlobDetector_create(params)
# Detect blobs
keypoints = detector.detect(image)
# Draw blobs
image_with_blobs = cv2.drawKeypoints(image, keypoints, np.array([]), (0, 0, 255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
# Show blobs
cv2.imshow('Blobs detected', cv2.resize(image_with_blobs, (int(1920/3.5), int(1080/3.5))))
cv2.waitKey(4000)
cv2.destroyAllWindows()
return image_with_blobs
def refined_blob_detection(self, img):
# Setup SimpleBlobDetector parameters.
params = cv2.SimpleBlobDetector_Params()
# Change thresholds
params.minThreshold = 10;
params.maxThreshold = 200;
# Filter by Area.
params.filterByArea = True
# params.minArea = 1500
params.minArea = 2000
# Filter by Circularity
params.filterByCircularity = True
params.minCircularity = 1
# Filter by Convexity
params.filterByConvexity = True
params.minConvexity = 0.87
# Filter by Inertia
params.filterByInertia = True
params.minInertiaRatio = 0.01
# Create a detector with the parameters
ver = (cv2.__version__).split('.')
if int(ver[0]) < 3 :
detector = cv2.SimpleBlobDetector(params)
else :
detector = cv2.SimpleBlobDetector_create(params)
# Detect blobs.
keypoints = detector.detect(img)
# Draw detected blobs as red circles.
# cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures the size of the circle corresponds to the size of blob
im_with_keypoints = cv2.drawKeypoints(img, keypoints, np.array([]), (0,0,255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
# Show keypoints
cv2.imshow("Refined Blob Detection", im_with_keypoints)
cv2.waitKey(1)
def black_and_yellow_buoys(frame):
# define range of yellow in HSV
yellow_upper_hsv = np.array([55, 255, 255], dtype="uint8")
yellow_lower_hsv = np.array([30, 130, 150], dtype="uint8")
# define range of black in HSV
black_upper_hsv = np.array([10, 255, 255], dtype="uint8")
black_lower_hsv = np.array([0, 100, 100], dtype="uint8")
# Threshold the HSV image to get only input colors
ylw = cv2.inRange(hsv, yellow_lower_hsv, yellow_upper_hsv)
blk = cv2.inRange(hsv, black_lower_hsv, black_upper_hsv)
# Bitwise-OR yellow and black
result = cv2.bitwise_or(ylw, blk)
closing = cv2.morphologyEx(result, cv2.MORPH_CLOSE, kernel2)
BuoyBinary = closing # cv2.bitwise_not(closing)
####
white = np.array([0, 0, 255], dtype="uint8")
whiteClosing = cv2.morphologyEx(white, cv2.MORPH_CLOSE, kernel2)
whiteFinal = whiteClosing
####
# Set up the detector with default parameters.
detector = cv2.SimpleBlobDetector(params)
# Detect blobs.
keypoints = detector.detect(frame)
# Draw detected blobs as red circles (0, 0, 255)BGR
# cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures the size of the circle corresponds to the size of blob
im_with_keypoints = cv2.drawKeypoints(
BuoyBinary, keypoints, np.array([]), (0, 0, 255),
cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
cv2.imwrite("frame%d.jpg" % count, frame)
cv2.imwrite("binary_%d.jpg" % count, BuoyBinary)
cv2.imshow("binary_%d.jpg" % count, BuoyBinary)
cv2.imwrite("keypts_%d.jpg" % count, im_with_keypoints)
cv2.imshow('keypts', im_with_keypoints)
def blob_detection(img):
# Set up the detector with default parameters.
detector = cv2.SimpleBlobDetector()
# Detect blobs.
keypoints = detector.detect(img)
# Draw detected blobs as red circles.
# cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures the size of the circle corresponds to the size of blob
im_with_keypoints = cv2.drawKeypoints(
img, keypoints, np.array([]), (0, 0, 255),
cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
# Show keypoints
# cv2.imshow("Keypoints", im_with_keypoints)
# cv2.waitKey()
plt.imshow(im_with_keypoints)
plt.title("Blobs")
plt.show()
def __init__(self):
self.max_clusters = 8
self.threshold = 20
self.blubParams = cv2.SimpleBlobDetector_Params()
self.blubParams.minThreshold = 50
self.blubParams.maxThreshold = 255
self.blubParams.filterByArea = True
self.blubParams.minArea = 0
self.blubParams.filterByCircularity = True
self.blubParams.minCircularity = 0.3
self.blubParams.filterByConvexity = True
self.blubParams.minConvexity = 0.7
self.blubParams.filterByInertia = True
self.blubParams.minInertiaRatio = 0.1
self.blubParams.blobColor = 255
ver = (cv2.__version__).split('.')
if int(ver[0]) < 3:
self.blubDetector = cv2.SimpleBlobDetector(self.blubParams)
else:
self.blubDetector = cv2.SimpleBlobDetector_create(self.blubParams)
def coordinates_question(self, image):
try:
# Criando o detector baseado na versão do CV
is_cv3 = cv2.__version__.startswith("3.")
if is_cv3:
detector = cv2.SimpleBlobDetector_create()
else:
detector = cv2.SimpleBlobDetector()
# Detectando corpos
keypoints = detector.detect(image)
im_with_keypoints = cv2.drawKeypoints(
image, keypoints, np.array([]), (0, 0, 255),
cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
return keypoints
except:
print("ERROR 'coordinates_question' IMAGE")
self.cap.release()
cv2.destroyAllWindows()
exit(0)
def getContours(theframe):
params = cv2.SimpleBlobDetector_Params()
params.minThreshold = 245
params.maxThreshold = 255
params.filterByCircularity = True
params.minCircularity = .8
detector = cv2.SimpleBlobDetector(params)
imgray = cv2.cvtColor(theframe, cv2.COLOR_BGR2GRAY)
keypoints = detector.detect(imgray)
im_kp = cv2.drawKeypoints(imgray, keypoints, np.array([]), (0, 0, 255),
cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
ctrs, hier = cv2.findContours(im_kp, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(thresh, ctrs, -1, (255, 255, 255), 3)
cv2.imshow('kps', im_kp)
return ctrs
def findblobs(img): #finds all blobs in a given Image
# Setup SimpleBlobDetector parameters.
params = cv2.SimpleBlobDetector_Params()
# Change thresholds
params.minThreshold = 120
params.maxThreshold = 200
# Filter by Area.
params.filterByArea = True
params.minArea = 20
# Filter by Circularity
params.filterByCircularity = True
params.minCircularity = 0.5
# Filter by Convexity
params.filterByConvexity = True
params.minConvexity = 0.8
# Filter by Inertia
params.filterByInertia = True
params.minInertiaRatio = 0.01
# Create a detector with the parameters
ver = (cv2.__version__).split('.')
if int(ver[0]) < 3:
detector = cv2.SimpleBlobDetector(params)
else:
detector = cv2.SimpleBlobDetector_create(params)
image = threshholding(img)
# Detect blobs.
keypoints = detector.detect(image)
# Draw detected blobs as red circles.
# cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures the size of the circle corresponds to the size of blob
im_with_keypoints = cv2.drawKeypoints(
img, keypoints, np.array([]), (0, 0, 255),
cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
return im_with_keypoints, keypoints, keypoints.__len__()
def return_blobs(self, image, color_min, color_max):
img_BGR = cv2.imread(image)
# Setup default values for SimpleBlobDetector parameters.
params = cv2.SimpleBlobDetector_Params()
# These are just examples, tune your own if needed
# Change thresholds
params.minThreshold = 10
params.maxThreshold = 200
# Filter by Area
params.filterByArea = True
params.minArea = 200
params.maxArea = 10000
# Filter by Circularity
params.filterByCircularity = True
params.minCircularity = 0.1
# Filter by Color
params.filterByColor = False
# not directly color, but intensity on the channel input
# params.blobColor = 0
params.filterByConvexity = False
params.filterByInertia = False
mask_color = cv2.inRange(img_BGR, color_min, color_max)
# Create a detector with the parameters
ver = (cv2.__version__).split('.')
if int(ver[0]) < 3:
detector = cv2.SimpleBlobDetector(params)
else:
detector = cv2.SimpleBlobDetector_create(params)
# detector finds "dark" blobs by default, so invert image for results with same detector
keypoints_color = detector.detect(255-mask_color)
K = []
for k in keypoints_color:
K.append((k.pt[0], k.pt[1], k.size))
return K
def EnclosedRegion(line):
line = cv2.bitwise_not(line)
# Setup SimpleBlobDetector parameters.
params = cv2.SimpleBlobDetector_Params()
# Change thresholds
params.minThreshold = 10
params.maxThreshold = 200
# Filter by Area.
params.filterByArea = False
params.minArea = 50
# Filter by Circularity
params.filterByCircularity = True
params.minCircularity = 0.1
# Filter by Convexity
params.filterByConvexity = True
params.minConvexity = 0.5
# Filter by Inertia
params.filterByInertia = True
params.minInertiaRatio = 0.01
# Create a detector with the parameters
ver = cv2.__version__.split('.')
if int(ver[0]) < 3:
detector = cv2.SimpleBlobDetector(params)
else:
detector = cv2.SimpleBlobDetector_create(params)
# Detect blobs.
keypoints = detector.detect(line)
Diameter = np.mean([keypoint.size for keypoint in keypoints if keypoint.size > 0])
Length = math.pi * Diameter
Area = math.pi * (Diameter**2)/4
return Area
def main():
global blur_image, min_color, max_color, first_color, mask_image
color_image = cv2.imread('../start_img.jpg', cv2.IMREAD_COLOR)
blur_image = cv2.GaussianBlur(color_image, (5, 5), 0)
params = cv2.SimpleBlobDetector_Params()
params.minThreshold = 0
params.maxThreshold = 255
params.filterByArea = True
params.minArea = 50
params.maxArea = 256 * 256
params.filterByCircularity = False
params.minCircularity = 0.1
params.filterByConvexity = False
params.minConvexity = 0.9
params.filterByInertia = False
params.minInertiaRatio = 0.5
ver = (cv2.__version__).split('.')
if int(ver[0]) < 3:
detector = cv2.SimpleBlobDetector(params)
else:
detector = cv2.SimpleBlobDetector_create(params)
cv2.namedWindow('Blur Image')
cv2.setMouseCallback('Blur Image', mouseEvent)
cv2.imshow('Blur Image', blur_image)
while True:
if not first_color:
cv2.imshow('Mask', mask_image)
key = cv2.waitKey(1) & 0xFF
if key == ord('q'):
break;
elif key == ord(' '):
print 'Detecting blobs'
keypoints = detector.detect(255 - mask_image)
print 'length:', len(keypoints)
for x in keypoints:
print x.size, x.angle
kp_image = cv2.drawKeypoints(blur_image, keypoints, np.array([]), (0, 255, 0), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
cv2.imshow('Keypoints', kp_image)
def getStarBlobs(im):
im = cv2.erode(im, np.ones((2, 2)))
height, width = im.shape[:2]
area = height * width
# Setup SimpleBlobDetector parameters.
params = cv2.SimpleBlobDetector_Params()
# Change thresholds
params.minThreshold = 4
params.maxThreshold = 255
# Filter by Area.
params.filterByArea = True
#10000
#500
params.minArea = area / 6596
params.maxArea = area / 711
# Filter by Circularity
params.filterByCircularity = True
params.minCircularity = 0
params.maxCircularity = 255
# Filter by Convexity
params.filterByConvexity = False
params.minConvexity = 0
params.maxConvexity = 255
# Filter by Inertia
params.filterByInertia = True
params.minInertiaRatio = 0
ver = (cv2.__version__).split('.')
if int(ver[0]) < 3:
detector = cv2.SimpleBlobDetector(params)
else:
detector = cv2.SimpleBlobDetector_create(params)
keypoints = detector.detect(im)
return keypoints #,im
def detect_blobs(self, image):
# Setup SimpleBlobDetector parameters.
params = cv2.SimpleBlobDetector_Params()
params.filterByArea = True
params.minArea = 200
params.filterByColor = True
params.blobColor = 255
params.filterByCircularity = False
params.filterByConvexity = False
params.filterByInertia = False
# Create a detector with the parameters
ver = cv2.__version__.split('.')
if int(ver[0]) < 3:
detector = cv2.SimpleBlobDetector(params)
else:
detector = cv2.SimpleBlobDetector_create(params)
blobs = detector.detect(image)
return blobs
def blob():
# Read image
im = cv2.imread("raw_footage\\1.jpeg", cv2.IMREAD_GRAYSCALE)
cv2.imshow("Keypoints", im)
cv2.waitKey(0)
# Set up the detector with default parameters.
detector = cv2.SimpleBlobDetector()
# Detect blobs.
keypoints = detector.detect(im)
# Draw detected blobs as red circles.
# cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures the size of the circle corresponds to the size of blob
im_with_keypoints = cv2.drawKeypoints(
im, keypoints, np.array([]), (0, 0, 255),
cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
# Show keypoints
cv2.imshow("Keypoints", im_with_keypoints)
cv2.waitKey(0)
def blob_detect(image):
global size_point
global space_point
blob = (size_point, space_point)
detector = cv2.SimpleBlobDetector()
# Detect blobs
keypoints = detector.detect(image)
new_keypoints = []
# filter blob on size
for points in keypoints:
if points.size >= blob[0]:
new_keypoints = new_keypoints + [points]
keypoints = color.remove_double(new_keypoints, blob[1])
# Draw detected blobs as red circles
# cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures the size of the circle corresponds to the size of blob
return cv2.drawKeypoints(image, keypoints, np.array([]), (255, 0, 0),cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS),keypoints
def create_blob_detector(self):
params = cv2.SimpleBlobDetector_Params(
) # Create blob detector with parameters.
params.filterByArea = True # Filter blobs by total area.
params.minArea = 700 # Minimum area of a blob to be detected.
params.maxArea = 15000 # Maximum area of a blob to be detected.
params.filterByColor = True # Filter blobs by colour.
params.blobColor = 255 # Only detect white spots as blobs.
params.filterByCircularity = False # Do not filter by circularity of blobs.
params.filterByConvexity = False # Do not filter by convexity of blobs.
params.filterByInertia = False # Do not filter by inertia of blobs.
ver = cv2.__version__.split(
'.') # Set version of blob detector created.
if int(ver[0]) < 3:
self.detector = cv2.SimpleBlobDetector(
params) # Create blob detector.
else:
self.detector = cv2.SimpleBlobDetector_create(
params) # Create blob detector.
def cell_detect_via_blob(frame):
resize_ratio, orignal = filters.applyPrimaryFilteringOnImage(
frame, last_thresh_min=40)
# Setup SimpleBlobDetector parameters.
params = cv2.SimpleBlobDetector_Params()
# Filter by Circularity
params.filterByCircularity = True
params.minCircularity = 0.1
# Create a detector with the parameters
ver = (cv2.__version__).split('.')
if int(ver[0]) < 3:
detector = cv2.SimpleBlobDetector()
else:
detector = cv2.SimpleBlobDetector_create()
keypoints = detector.detect(orignal)
print keypoints
processedframe = cv2.drawKeypoints(
orignal, keypoints, np.array([]), (0, 0, 255),
cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
return processedframe
def create_blob_detector(blob_min_area=12,
blob_min_int=0.0,
blob_max_int=1.0,
blob_th_step=5):
params = cv2.SimpleBlobDetector_Params()
params.filterByArea = True
params.minArea = blob_min_area
params.maxArea = 30
params.filterByCircularity = False
params.filterByColor = False
params.filterByConvexity = False
params.filterByInertia = False
# blob detection only works with "uint8" images.
params.minThreshold = int(blob_min_int * 255)
params.maxThreshold = int(blob_max_int * 255)
params.thresholdStep = blob_th_step
ver = (cv2.__version__).split('.')
if int(ver[0]) < 3:
return cv2.SimpleBlobDetector(params)
else:
return cv2.SimpleBlobDetector_create(params)
def getBlobDetector(exParams):
key = 'detector_params'
params = cv2.SimpleBlobDetector_Params()
params.minThreshold = 0
params.maxThreshold = 256
params.filterByArea = True
params.minArea = 300
params.maxArea = 20000
params.filterByCircularity = False
params.minCircularity = 0.1
params.filterByConvexity = False
params.minConvexity = 0.5
ver = cv2.__version__.split('.')
if int(ver[0]) < 3:
return cv2.SimpleBlobDetector(params)
else:
return cv2.SimpleBlobDetector_create(params)
def create_detector(self):
"""
@brief Setup SimpleBlobDetector parameters.
@param None
@return detector object
"""
# Change thresholds
self.params.minThreshold = params["blob_detector"]["minThreshold"]
self.params.maxThreshold = params["blob_detector"]["maxThreshold"]
# Filter by Area.
self.params.filterByArea = params["blob_detector"]["filterByArea"]
self.params.minArea = params["blob_detector"]["minArea"]
self.params.maxArea = params["blob_detector"]["maxArea"]
# Filter by Circularity
self.params.filterByCircularity = params["blob_detector"][
"filterByCircularity"]
self.params.minCircularity = params["blob_detector"]["minCircularity"]
# Filter by Convexity
self.params.filterByConvexity = params["blob_detector"][
"filterByConvexity"]
self.params.minConvexity = params["blob_detector"]["minConvexity"]
# Filter by Inertia
self.params.filterByInertia = params["blob_detector"][
"filterByInertia"]
self.params.minInertiaRatio = params["blob_detector"][
"minInertiaRatio"]
# Create a detector with the parameters
ver = (cv2.__version__).split('.')
if int(ver[0]) < 3:
return cv2.SimpleBlobDetector(self.params)
else:
return cv2.SimpleBlobDetector_create(self.params)
def detect_markers():
width = 1000
height = 1200
# Read image
im = cv2.imread("./markers.png", cv2.IMREAD_GRAYSCALE)
im = cv2.resize(im, (width, height))
# Setup SimpleBlobDetector parameters.
params = cv2.SimpleBlobDetector_Params()
params.filterByCircularity = True
params.maxCircularity = .85
params.minCircularity = .785
MAX_WIDTH = 215.9
MAX_HEIGHT = 279.4
# Create a detector with the parameters
ver = (cv2.__version__).split('.')
if int(ver[0]) < 3:
detector = cv2.SimpleBlobDetector(params)
else:
detector = cv2.SimpleBlobDetector_create(params)
# Detect blobs.
keypoints = detector.detect(im)
# Draw detected blobs as red circles.
# cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures
# the size of the circle corresponds to the size of blob
points = filterKeypoint(keypoints, width / 215.9, height / 279.4)
# for keypoint in keypoints:
# print(keypoint.pt[0], keypoint.pt[1])
for p in points:
print(p)
im_with_keypoints = cv2.drawKeypoints(
im, keypoints, np.array([]), (0, 0, 255),
cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
# Show blobs
cv2.imshow("Keypoints", im_with_keypoints)
cv2.waitKey(0)
def blob_function(img, is_utility):
params = cv2.SimpleBlobDetector_Params()
params.minThreshold = 10
params.maxThreshold = 256
# Filter by Circularity
params.filterByCircularity = True
params.minCircularity = 0.8
# Filter by Area.
params.filterByArea = True
if (is_utility):
params.minArea = 14
else:
params.filterByArea = True
params.minArea = 30
# Filter by Convexity
params.filterByConvexity = True
params.minConvexity = 0.87
# Filter by Inertia
params.filterByInertia = True
params.minInertiaRatio = 0.1
params.filterByColor = True
params.blobColor = 255
# Create a detector with the parameters
ver = (cv2.__version__).split('.')
if int(ver[0]) < 3:
detector = cv2.SimpleBlobDetector(params)
else:
detector = cv2.SimpleBlobDetector_create(params)
# Detect blobs.
keypoints = detector.detect(img)
return keypoints