- improved color detector
git-svn-id: http://moon:8086/svn/software/trunk/projects/opencv@311 b431acfa-c32f-4a4a-93f1-934dc6c82436
This commit is contained in:
+49
-24
@@ -10,6 +10,8 @@ import imutils
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import time
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import cv2
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import pprint
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from matplotlib import pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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width = 320
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height = 240
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@@ -90,7 +92,6 @@ class FindObjects():
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objects.append({'thickness' : self.meanThickness, 'members' : obj})
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# self.pp.pprint(objects)
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return objects
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def printStats(self):
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@@ -114,20 +115,25 @@ time.sleep(2.0)
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fps = FPS().start()
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findObjects = FindObjects()
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beadColors = []
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# loop over some frames...this time using the threaded stream
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while fps._numFrames < args["num_frames"]:
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# grab the frame from the threaded video stream
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frame = vs.read()
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gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
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gray_bluured = cv2.medianBlur(gray,5)
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gray_blurred = cv2.medianBlur(gray,5)
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# Canny edge detection
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img1_canny = cv2.Canny(gray_bluured, 100, 50)
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img1_canny = cv2.Canny(gray_blurred, 100, 50)
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kernel = np.ones((1,1),np.uint8)
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# img1_canny = cv2.blur(img1_canny,(3,3))
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# img1_canny = cv2.dilate(img1_canny,kernel,iterations = 1)
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# Thresholding
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# ret,img1_thr = cv2.threshold(gray1,120,255,cv2.THRESH_BINARY)
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# img1_thr = cv2.adaptiveThreshold(gray1,255,cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY,11,2)
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# img1_thr = cv2.adaptiveThreshold(gray1,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,11,2)
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# ret,img1_thr = cv2.threshold(gray_blurred,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
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# img1_thr = cv2.adaptiveThreshold(gray_blurred,255,cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY,11,2)
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# img1_thr = cv2.adaptiveThreshold(gray_blurred,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,11,2)
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# Contours
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(_, contours, hierachy) = cv2.findContours(img1_canny.copy(),cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
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@@ -138,8 +144,9 @@ while fps._numFrames < args["num_frames"]:
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img1_objects = frame.copy()
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img1_colors = np.zeros((height,width,3), np.uint8)
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maskCenter = 2
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maskCenter = 4
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roides = []
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beads = []
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for obj in objects:
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memberCount = 0
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radius = 0
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@@ -150,28 +157,26 @@ while fps._numFrames < args["num_frames"]:
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if member['isHole']:
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img1_objects = cv2.rectangle(img1_objects,(x,y),(x+w,y+h),(255,0,0),2)
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thickness = obj['thickness']-maskCenter
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radius = int((member['diameter']+thickness+maskCenter)/2)
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roi = frame[y-thickness:y+2*radius, x-thickness:x+2*radius]
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roides.append({'roi' : roi, 'pos' : member['pos'], 'radius' : radius, 'thickness' : thickness})
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diameter = member['diameter']+thickness+maskCenter
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roi = frame[y-thickness:y+diameter, x-thickness:x+diameter]
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pos = member['pos']
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if diameter > 0:
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mask = np.zeros((diameter,diameter,1), np.uint8)
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mask = cv2.circle(mask,(int(diameter/2), int(diameter/2)),radius,255,thickness)
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mean_color = cv2.mean(roi)
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img1_colors = cv2.circle(img1_colors,(int(pos[0]),int(pos[1])),int(diameter/2),mean_color,-1)
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beads.append({'pos' : pos, 'diameter' : diameter, 'color' : mean_color[0:3]})
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img1_objects = cv2.circle(img1_objects,member['pos'],int(diameter/2),(255,255,255),thickness)
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else:
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img1_objects = cv2.rectangle(img1_objects,(x,y),(x+w,y+h),(0,0,255),2)
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memberCount += 1
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for r in roides:
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if radius > 0:
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radius = r['radius']
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thickness = r['thickness']
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roi = r['roi']
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pos = r['pos']
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mask = np.zeros((2*radius,2*radius,1), np.uint8)
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mask = cv2.circle(mask,(radius, radius),radius,255,thickness)
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mean_color = cv2.mean(roi)
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center = (int(pos[0]),int(pos[1]))
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img1_colors = cv2.circle(img1_colors,center,radius,mean_color,thickness)
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for bead in beads:
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beadColors.append(bead['color'])
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cv2.imshow('detected colors', img1_colors)
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# cv2.imshow('Thresholded',img1_thr)
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cv2.imshow('detected colors', img1_colors)
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cv2.imshow('Canny',img1_canny)
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cv2.imshow('Objects',img1_objects)
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cv2.waitKey(1)
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@@ -179,8 +184,6 @@ while fps._numFrames < args["num_frames"]:
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# update the FPS counter
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fps.update()
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findObjects.printStats()
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# stop the timer and display FPS information
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fps.stop()
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vs.stop()
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@@ -189,6 +192,28 @@ print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
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print("[INFO] Camera : elasped time: {:.2f}".format(vs.getfps().elapsed()))
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print("[INFO] Camera: approx. FPS: {:.2f}".format(vs.getfps().fps()))
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# Output stats
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findObjects.printStats()
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# Scatter plot of colors
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numObjects = len(beadColors);
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blue = np.zeros(numObjects)
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green = np.zeros(numObjects)
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red = np.zeros(numObjects)
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plotColors = np.zeros((numObjects,3))
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i = 0
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for color in beadColors:
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blue[i] = color[0]/255
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green[i] = color[1]/255
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red[i] = color[2]/255
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plotColors[i] = [red[i], green[i], blue[i]]
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i += 1
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fig = plt.figure()
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ax = fig.add_subplot(111, projection='3d')
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ax.scatter(blue, green, red, zdir='z', s=50, facecolors=plotColors, lw = 0, depthshade=True)
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plt.show()
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# do a bit of cleanup
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cv2.destroyAllWindows()
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