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- fixed runtime errors if no objects are present

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git-svn-id: http://moon:8086/svn/software/trunk/projects/opencv@321 b431acfa-c32f-4a4a-93f1-934dc6c82436
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jens
2016-09-20 17:43:47 +00:00
parent 48674e65f3
commit 90f525b909
1 changed files with 47 additions and 43 deletions
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beadDetect.py
+42 -38
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@@ -62,7 +62,7 @@ class FindObjects():
temp_objects = [] temp_objects = []
objectsCandIds = [] objectsCandIds = []
if hierachy.any(): if hierachy is not None:
while count != -1: while count != -1:
family = FindObjects.getFamily([], count, hierachy) family = FindObjects.getFamily([], count, hierachy)
@@ -294,51 +294,55 @@ if videoFile == "piCamera":
print("[INFO] Camera : elasped time: {:.2f}".format(vs.getfps().elapsed())) print("[INFO] Camera : elasped time: {:.2f}".format(vs.getfps().elapsed()))
print("[INFO] Camera: approx. FPS: {:.2f}".format(vs.getfps().fps())) print("[INFO] Camera: approx. FPS: {:.2f}".format(vs.getfps().fps()))
# Output stats if numColorClasses:
findObjects.printStats()
# Analyze colors # Output stats
numObjects = len(beadColors); findObjects.printStats()
blue = np.zeros(numObjects)
green = np.zeros(numObjects)
red = np.zeros(numObjects)
plotColors = np.zeros((numObjects,3))
i = 0
for color in beadColors:
blue[i] = color[0]
green[i] = color[1]
red[i] = color[2]
plotColors[i] = [red[i], green[i], blue[i]]
i += 1
# Kmeans create color classes # Analyze colors
Z = np.float32(plotColors) numObjects = len(beadColors);
blue = np.zeros(numObjects)
green = np.zeros(numObjects)
red = np.zeros(numObjects)
plotColors = np.zeros((numObjects,3))
i = 0
for color in beadColors:
blue[i] = color[0]
green[i] = color[1]
red[i] = color[2]
plotColors[i] = [red[i], green[i], blue[i]]
i += 1
# Define criteria = ( type, max_iter, epsilon ) # Kmeans create color classes
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.1) Z = np.float32(plotColors)
ret,label,center=cv2.kmeans(Z,numColorClasses,None,criteria,10,cv2.KMEANS_PP_CENTERS)
print (center) # Define criteria = ( type, max_iter, epsilon )
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)
cnt1 = 0 print (center)
for c1 in center:
cnt2 = 0
for c2 in center:
print ("Color distance["+ str(cnt1) + "," + str(cnt2) + "] = " + str(colordistance(c1, c2)))
cnt2 += 1
cnt1 += 1
# Scatter plot of colors cnt1 = 0
fig = plt.figure() for c1 in center:
ax = fig.add_subplot(111, projection='3d') cnt2 = 0
ax.set_xlabel('blue') for c2 in center:
ax.set_ylabel('green') print ("Color distance["+ str(cnt1) + "," + str(cnt2) + "] = " + str(colordistance(c1, c2)))
ax.set_zlabel('red') cnt2 += 1
ax.scatter(blue, green, red, zdir='z', s=10, c=(0,0,0), lw = 0, depthshade=True) cnt1 += 1
ax.scatter(center[:,2], center[:,1], center[:,0], zdir='z', s=500, facecolors=center, lw = 1, depthshade=True)
plt.show() # Scatter plot of colors
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.set_xlabel('blue')
ax.set_ylabel('green')
ax.set_zlabel('red')
ax.scatter(blue, green, red, zdir='z', s=10, c=(0,0,0), lw = 0, depthshade=True)
ax.scatter(center[:,2], center[:,1], center[:,0], zdir='z', s=500, facecolors=center, lw = 1, depthshade=True)
plt.show()
else:
print("Sorry, no colors have been detected")
# do a bit of cleanup # do a bit of cleanup
cv2.destroyAllWindows() cv2.destroyAllWindows()