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git-svn-id: http://moon:8086/svn/software/trunk/projects/opencv@323 b431acfa-c32f-4a4a-93f1-934dc6c82436
This commit is contained in:
jens
2016-09-28 19:18:18 +00:00
parent 869ff43a45
commit 5434b0a16e
2 changed files with 177 additions and 167 deletions
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PiVideoStream.py
+21 -12
View File
@@ -14,8 +14,24 @@ class PiVideoStream:
self.camera.exposure_mode = 'auto' self.camera.exposure_mode = 'auto'
# self.camera.image_effect = 'colorbalance' # self.camera.image_effect = 'colorbalance'
# self.camera.image_effect_params = (0,1,1,1,0,0) # self.camera.image_effect_params = (0,1,1,1,0,0)
self.camera.exposure_mode = 'auto'
self.camera.awb_mode = 'off'
self.camera.awb_gains = (1.5, 1.5)
# self.camera.analog_gain = 1.75
# self.camera.digital_gain = 1.00
self.rawCapture = PiRGBArray(self.camera, size=resolution)
self.stream = self.camera.capture_continuous(self.rawCapture,
format="bgr", use_video_port=True)
# initialize the frame and the variable used to indicate
# if the thread should be stopped
self.frame = None
self.stopped = False
self.fps = None
self.thread = None
self.startup = True
def printSettings(self):
awb_gains = self.camera.awb_gains awb_gains = self.camera.awb_gains
print ("sensor_mode : " + str(self.camera.sensor_mode)) print ("sensor_mode : " + str(self.camera.sensor_mode))
print ("resolution : " + str(self.camera.resolution)) print ("resolution : " + str(self.camera.resolution))
@@ -38,18 +54,6 @@ class PiVideoStream:
print ("image_effect_params : " + str(self.camera.image_effect_params)) print ("image_effect_params : " + str(self.camera.image_effect_params))
print ("sharpness : " + str(self.camera.sharpness)) print ("sharpness : " + str(self.camera.sharpness))
self.rawCapture = PiRGBArray(self.camera, size=resolution)
self.stream = self.camera.capture_continuous(self.rawCapture,
format="bgr", use_video_port=True)
# initialize the frame and the variable used to indicate
# if the thread should be stopped
self.frame = None
self.stopped = False
self.fps = None
self.thread = None
self.startup = True
def start(self): def start(self):
# start the thread to read frames from the video stream # start the thread to read frames from the video stream
self.thread = Thread(target=self.update, args=()) self.thread = Thread(target=self.update, args=())
@@ -59,6 +63,11 @@ class PiVideoStream:
def update(self): def update(self):
# keep looping infinitely until the thread is stopped # keep looping infinitely until the thread is stopped
for f in self.stream: for f in self.stream:
if self.camera.exposure_mode == 'auto':
if self.camera.analog_gain > 1.75 and self.camera.digital_gain == 1.0:
self.camera.exposure_mode = 'off'
self.printSettings()
if self.startup: if self.startup:
self.fps = FPS().start() self.fps = FPS().start()
self.startup = False self.startup = False
beadDetect.py
+152 -151
View File
@@ -62,55 +62,58 @@ class FindObjects():
temp_objects = [] temp_objects = []
objectsCandIds = [] objectsCandIds = []
if hierachy is not None: try:
while count != -1: if hierachy.any():
family = FindObjects.getFamily([], count, hierachy) while count != -1:
family = FindObjects.getFamily([], count, hierachy)
dupDict = {} dupDict = {}
members = [] members = []
for member in family: for member in family:
x,y,w,h = cv2.boundingRect(contours[member]); x,y,w,h = cv2.boundingRect(contours[member]);
key = str([x,y,w,h]) + '.key' key = str([x,y,w,h]) + '.key'
if not key in dupDict: if not key in dupDict:
dupDict[key] = member dupDict[key] = member
members.append({ 'id' : member, 'bbox' : [x,y,w,h]}) members.append({ 'id' : member, 'bbox' : [x,y,w,h]})
objectsCandIds.append(members) objectsCandIds.append(members)
count = hierachy[0][count][0] count = hierachy[0][count][0]
for family in objectsCandIds: for family in objectsCandIds:
obj = [] obj = []
for member in family: for member in family:
area = cv2.contourArea(contours[member['id']]) area = cv2.contourArea(contours[member['id']])
perimeter = cv2.arcLength(contours[member['id']], True) perimeter = cv2.arcLength(contours[member['id']], True)
pi = 3.14159265359 pi = 3.14159265359
Q = 0 Q = 0
if (area > 10): if (area > 10):
Q = 4*pi*area/(perimeter*perimeter) Q = 4*pi*area/(perimeter*perimeter)
if Q > 0.7: if Q > 0.7:
diameter = max(member['bbox'][2], member['bbox'][3]) diameter = max(member['bbox'][2], member['bbox'][3])
self.maxDiameter = max(self.maxDiameter, diameter) self.maxDiameter = max(self.maxDiameter, diameter)
self.minDiameter = min(self.minDiameter, diameter) self.minDiameter = min(self.minDiameter, diameter)
member['diameter'] = diameter member['diameter'] = diameter
member['pos'] = (int(member['bbox'][0] + member['bbox'][2]/2), int(member['bbox'][1] + member['bbox'][3]/2)) member['pos'] = (int(member['bbox'][0] + member['bbox'][2]/2), int(member['bbox'][1] + member['bbox'][3]/2))
obj.append(member) obj.append(member)
if obj: if obj:
temp_objects.append(obj) temp_objects.append(obj)
for obj in temp_objects: for obj in temp_objects:
for member in obj: for member in obj:
if member['diameter'] < self.innerOuterRatio*self.maxDiameter: if member['diameter'] < self.innerOuterRatio*self.maxDiameter:
member['isHole'] = True member['isHole'] = True
else: else:
member['isHole'] = False member['isHole'] = False
if len(obj) == 2: if len(obj) == 2:
self.meanThickness = int(abs(obj[0]['diameter'] - obj[1]['diameter'])/2) self.meanThickness = int(abs(obj[0]['diameter'] - obj[1]['diameter'])/2)
objects.append({'thickness' : self.meanThickness, 'members' : obj}) objects.append({'thickness' : self.meanThickness, 'members' : obj})
except:
pass
return objects return objects
@@ -175,126 +178,127 @@ if args["calibrate"] == 'on':
params.append(0) params.append(0)
cv2.imwrite("lens_corr.png", np.uint8(lens_corr*127.0), params) cv2.imwrite("lens_corr.png", np.uint8(lens_corr*127.0), params)
cv2.imwrite("lens_shot.png", frame, params)
sys.exit() fps.stop()
frame = np.ones((height, width, 3), np.float32)
lens_corr = np.float32(cv2.imread("lens_corr.png"))/140.0
# loop over some frames...this time using the threaded stream
while fps._numFrames < args["num_frames"]:
# grab the frame from the threaded video stream
if videoFile == "piCamera": if videoFile == "piCamera":
frame = vs.read() vs.stop()
else:
ret, frame = vs.read()
if args["use_calibration"] == 'on': else:
frame = np.uint8(cv2.multiply(lens_corr, np.float32(frame))) frame = np.ones((height, width, 3), np.float32)
lens_corr = np.float32(cv2.imread("lens_corr.png"))/128.0
kernel = np.ones((3,3),np.uint8) # loop over some frames...this time using the threaded stream
frame_dilated = cv2.dilate(frame,kernel,iterations = 1) while fps._numFrames < args["num_frames"]:
gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY) # grab the frame from the threaded video stream
gray_blurred = cv2.medianBlur(gray,5)
# Canny edge detection if videoFile == "piCamera":
img1_canny = cv2.Canny(gray_blurred, 100, 50) frame = vs.read()
# Thresholding
# ret,img1_thr = cv2.threshold(gray_blurred,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
# img1_thr = cv2.adaptiveThreshold(gray_blurred,255,cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY,11,2)
# img1_thr = cv2.adaptiveThreshold(gray_blurred,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,11,2)
# Contours
(_, contours, hierachy) = cv2.findContours(img1_canny.copy(),cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
img1_contours = np.zeros((height,width,3), np.uint8)
img1_contours = cv2.drawContours(img1_contours, contours, -1, (0,255,0), 1)
objects = findObjects.find(contours, hierachy)
# print (ids)
img1_objects = frame.copy()
img1_colors = np.zeros((height,width,3), np.uint8)
maskCenter = 4
roides = []
beads = []
for obj in objects:
memberCount = 0
radius = 0
roi = 0
for member in obj['members']:
# Draw bounding box
x,y,w,h = [member['bbox'][0], member['bbox'][1], member['bbox'][2], member['bbox'][3]];
if member['isHole']:
img1_objects = cv2.rectangle(img1_objects,(x,y),(x+w,y+h),(255,0,0),2)
thickness = obj['thickness']-maskCenter
diameter = member['diameter']+thickness+maskCenter
roi = frame_dilated[y-thickness:y+diameter, x-thickness:x+diameter]
pos = member['pos']
if diameter > 0:
mask = np.zeros((diameter,diameter,1), np.uint8)
mask = cv2.circle(mask,(int(diameter/2), int(diameter/2)),radius,255,thickness)
mean_color = cv2.mean(roi)
img1_colors = cv2.circle(img1_colors,(int(pos[0]),int(pos[1])),int(diameter/2),mean_color,-1)
beads.append({'pos' : pos, 'diameter' : diameter, 'color' : [mean_color[0]/255, mean_color[1]/255, mean_color[2]/255]})
# img1_objects = cv2.circle(img1_objects,member['pos'],int(diameter/2),(255,255,255),thickness)
else:
img1_objects = cv2.rectangle(img1_objects,(x,y),(x+w,y+h),(0,0,255),2)
memberCount += 1
for bead in beads:
beadColors.append(bead['color'])
colorClasses = []
for bead in beads:
if not colorClasses:
colorClasses.append(bead['color'])
else: else:
d, i = mindistance(colorClasses, bead['color']) ret, frame = vs.read()
if d > 0.14:
if args["use_calibration"] == 'on':
frame = np.uint8(cv2.multiply(lens_corr, np.float32(frame)))
kernel = np.ones((3,3),np.uint8)
frame_dilated = cv2.dilate(frame,kernel,iterations = 1)
gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
gray_blurred = cv2.medianBlur(gray,5)
# Canny edge detection
img1_canny = cv2.Canny(gray_blurred, 100, 50)
# Thresholding
# ret,img1_thr = cv2.threshold(gray_blurred,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
# img1_thr = cv2.adaptiveThreshold(gray_blurred,255,cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY,11,2)
# img1_thr = cv2.adaptiveThreshold(gray_blurred,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,11,2)
# Contours
(_, contours, hierachy) = cv2.findContours(img1_canny.copy(),cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
img1_contours = np.zeros((height,width,3), np.uint8)
img1_contours = cv2.drawContours(img1_contours, contours, -1, (0,255,0), 1)
objects = findObjects.find(contours, hierachy)
# print (ids)
img1_objects = frame.copy()
img1_colors = np.zeros((height,width,3), np.uint8)
maskCenter = 4
roides = []
beads = []
for obj in objects:
memberCount = 0
radius = 0
roi = 0
for member in obj['members']:
# Draw bounding box
x,y,w,h = [member['bbox'][0], member['bbox'][1], member['bbox'][2], member['bbox'][3]];
if member['isHole']:
img1_objects = cv2.rectangle(img1_objects,(x,y),(x+w,y+h),(255,0,0),2)
thickness = obj['thickness']-maskCenter
diameter = member['diameter']+thickness+maskCenter
roi = frame_dilated[y-thickness:y+diameter, x-thickness:x+diameter]
pos = member['pos']
if diameter > 0:
mask = np.zeros((diameter,diameter,1), np.uint8)
mask = cv2.circle(mask,(int(diameter/2), int(diameter/2)),radius,255,thickness)
mean_color = cv2.mean(roi)
img1_colors = cv2.circle(img1_colors,(int(pos[0]),int(pos[1])),int(diameter/2),mean_color,-1)
beads.append({'pos' : pos, 'diameter' : diameter, 'color' : [mean_color[0]/255, mean_color[1]/255, mean_color[2]/255]})
# img1_objects = cv2.circle(img1_objects,member['pos'],int(diameter/2),(255,255,255),thickness)
else:
img1_objects = cv2.rectangle(img1_objects,(x,y),(x+w,y+h),(0,0,255),2)
memberCount += 1
for bead in beads:
beadColors.append(bead['color'])
colorClasses = []
for bead in beads:
if not colorClasses:
colorClasses.append(bead['color']) colorClasses.append(bead['color'])
else: else:
colorClasses[i][0] = 0.5*colorClasses[i][0] + 0.5*bead['color'][0] d, i = mindistance(colorClasses, bead['color'])
colorClasses[i][1] = 0.5*colorClasses[i][1] + 0.5*bead['color'][1] if d > 0.14:
colorClasses[i][2] = 0.5*colorClasses[i][2] + 0.5*bead['color'][2] colorClasses.append(bead['color'])
else:
colorClasses[i][0] = 0.5*colorClasses[i][0] + 0.5*bead['color'][0]
colorClasses[i][1] = 0.5*colorClasses[i][1] + 0.5*bead['color'][1]
colorClasses[i][2] = 0.5*colorClasses[i][2] + 0.5*bead['color'][2]
if numColorClasses != len(colorClasses): if numColorClasses != len(colorClasses):
numColorClasses = len(colorClasses) numColorClasses = len(colorClasses)
print("Found " + str(numColorClasses) + " color classes") print("Found " + str(numColorClasses) + " color classes")
cv2.imshow('Corrected', frame) cv2.imshow('Corrected', frame)
cv2.imshow('Contours', frame_dilated) cv2.imshow('Contours', frame_dilated)
cv2.imshow('Colors', img1_colors) cv2.imshow('Colors', img1_colors)
cv2.imshow('Canny',img1_canny) cv2.imshow('Canny',img1_canny)
cv2.imshow('Objects',img1_objects) cv2.imshow('Objects',img1_objects)
if videoFile == "piCamera":
cv2.waitKey(1)
else:
# cv2.waitKey(0)
cv2.waitKey(int(1000.0/framerate))
# update the FPS counter
fps.update()
# stop the timer and display FPS information
fps.stop()
if videoFile == "piCamera": if videoFile == "piCamera":
cv2.waitKey(1) vs.stop()
else:
# cv2.waitKey(0)
cv2.waitKey(int(1000.0/framerate))
print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
# update the FPS counter print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
fps.update() if videoFile == "piCamera":
print("[INFO] Camera : elasped time: {:.2f}".format(vs.getfps().elapsed()))
# stop the timer and display FPS information print("[INFO] Camera: approx. FPS: {:.2f}".format(vs.getfps().fps()))
fps.stop()
if videoFile == "piCamera":
vs.stop()
print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
if videoFile == "piCamera":
print("[INFO] Camera : elasped time: {:.2f}".format(vs.getfps().elapsed()))
print("[INFO] Camera: approx. FPS: {:.2f}".format(vs.getfps().fps()))
if numColorClasses:
# Output stats # Output stats
findObjects.printStats() findObjects.printStats()
@@ -318,7 +322,7 @@ if numColorClasses:
# Define criteria = ( type, max_iter, epsilon ) # Define criteria = ( type, max_iter, epsilon )
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.1) criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.1)
ret,label,center=cv2.kmeans(np.float32(plotColors), numColorClasses, None, criteria, 10, cv2.KMEANS_PP_CENTERS) ret,label,center=cv2.kmeans(Z,numColorClasses,None,criteria,10,cv2.KMEANS_PP_CENTERS)
print (center) print (center)
@@ -341,9 +345,6 @@ if numColorClasses:
plt.show() plt.show()
else:
print("Sorry, no colors have been detected")
# do a bit of cleanup # do a bit of cleanup
cv2.destroyAllWindows() cv2.destroyAllWindows()