jens
b7e833bbc4
git-svn-id: http://moon:8086/svn/software/trunk/projects/opencv@317 b431acfa-c32f-4a4a-93f1-934dc6c82436
278 lines
8.0 KiB
Python
Executable File
278 lines
8.0 KiB
Python
Executable File
# import the necessary packages
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from __future__ import print_function
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from imutils.video import FPS
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import numpy as np
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import argparse
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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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def colordistance(color1, color2):
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d0 = (color1[0]-color2[0])
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d1 = (color1[1]-color2[1])
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d2 = (color1[2]-color2[2])
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return np.math.sqrt(d0*d0 + d1*d1 + d2*d2)
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class FindObjects():
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def __init__(self):
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self.innerOuterRatio = 0.6 # const
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self.meanThickness = 0
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self.meanOuterDiameter = 0
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self.meanInnerDiameter = 0
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self.maxDiameter = -10000
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self.minDiameter = +10000
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self.pp = pprint.PrettyPrinter(indent=4)
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@staticmethod
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def getFamily(family, parentId, hierachy):
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family.append(parentId)
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if hierachy[0][parentId][2] != -1:
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return FindObjects.getFamily(family, hierachy[0][parentId][2], hierachy)
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else:
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return family
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def find(self, contours, hierachy):
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count = 0
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objects = []
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temp_objects = []
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objectsCandIds = []
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while count != -1:
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family = FindObjects.getFamily([], count, hierachy)
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dupDict = {}
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members = []
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for member in family:
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x,y,w,h = cv2.boundingRect(contours[member]);
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key = str([x,y,w,h]) + '.key'
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if not key in dupDict:
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dupDict[key] = member
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members.append({ 'id' : member, 'bbox' : [x,y,w,h]})
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objectsCandIds.append(members)
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count = hierachy[0][count][0]
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for family in objectsCandIds:
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obj = []
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for member in family:
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area = cv2.contourArea(contours[member['id']])
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perimeter = cv2.arcLength(contours[member['id']], True)
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pi = 3.14159265359
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Q = 0
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if (perimeter > 0):
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Q = 4*pi*area/(perimeter*perimeter)
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if Q > 0.7:
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diameter = max(member['bbox'][2], member['bbox'][3])
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self.maxDiameter = max(self.maxDiameter, diameter)
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self.minDiameter = min(self.minDiameter, diameter)
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member['diameter'] = diameter
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member['pos'] = (int(member['bbox'][0] + member['bbox'][2]/2), int(member['bbox'][1] + member['bbox'][3]/2))
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obj.append(member)
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if obj:
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temp_objects.append(obj)
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for obj in temp_objects:
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for member in obj:
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if member['diameter'] < self.innerOuterRatio*self.maxDiameter:
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member['isHole'] = True
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else:
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member['isHole'] = False
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if len(obj) == 2:
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self.meanThickness = int(abs(obj[0]['diameter'] - obj[1]['diameter'])/2)
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objects.append({'thickness' : self.meanThickness, 'members' : obj})
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return objects
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def printStats(self):
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print ("maxDiameter = " + str(self.maxDiameter) + " px")
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print ("minDiameter = " + str(self.minDiameter) + " px")
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print ("meanThickness = " + str(self.meanThickness) + " px")
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# construct the argument parse and parse the arguments
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ap = argparse.ArgumentParser()
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ap.add_argument("-n", "--num-frames", type=int, default=100,
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help="# of frames to loop over for FPS test")
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ap.add_argument("-r", "--framerate", type=int, default=30,
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help="# of frames to loop over for FPS test")
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ap.add_argument("-f", "--filename", type=str, default='piCamera',
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help="Whether or not frames should be displayed")
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args = vars(ap.parse_args())
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videoFile = args["filename"]
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framerate = args["framerate"]
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# created a *threaded *video stream, allow the camera sensor to warmup,
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# and start the FPS counter
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if videoFile == "piCamera":
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from PiVideoStream import PiVideoStream
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from picamera.array import PiRGBArray
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from picamera import PiCamera
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vs = PiVideoStream(resolution=(width,height), framerate=framerate).start()
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time.sleep(5.0)
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else:
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vs = cv2.VideoCapture(videoFile)
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# skip first 5 seconds
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for i in range(0, 5*framerate):
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ret, frame = vs.read()
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print("[INFO] sampling THREADED frames from `" + videoFile + "` at " + str(framerate) + " frame/s")
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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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if videoFile == "piCamera":
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frame = vs.read()
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else:
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ret, frame = vs.read()
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gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
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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_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(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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img1_contours = np.zeros((height,width,3), np.uint8)
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img1_contours = cv2.drawContours(img1_contours, contours, -1, (0,255,0), 1)
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objects = findObjects.find(contours, hierachy)
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# print (ids)
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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 = 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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roi = 0
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for member in obj['members']:
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# Draw bounding box
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x,y,w,h = [member['bbox'][0], member['bbox'][1], member['bbox'][2], member['bbox'][3]];
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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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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 bead in beads:
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beadColors.append(bead['color'])
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# cv2.imshow('Thresholded',img1_thr)
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cv2.imshow('Contours', img1_contours)
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cv2.imshow('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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if videoFile == "piCamera":
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cv2.waitKey(1)
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else:
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# cv2.waitKey(0)
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cv2.waitKey(int(1000.0/framerate))
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# update the FPS counter
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fps.update()
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# stop the timer and display FPS information
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fps.stop()
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if videoFile == "piCamera":
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vs.stop()
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print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
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print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
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if videoFile == "piCamera":
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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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# Analyze 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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# Kmeans create color classes
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Z = np.float32(plotColors)
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# Define criteria = ( type, max_iter, epsilon )
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criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.1)
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ret,label,center=cv2.kmeans(Z,10,None,criteria,10,cv2.KMEANS_PP_CENTERS)
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print (center)
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cnt1 = 0
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for c1 in center:
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cnt2 = 0
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for c2 in center:
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print ("Color distance["+ str(cnt1) + "," + str(cnt2) + "] = " + str(colordistance(c1, c2)))
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cnt2 += 1
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cnt1 += 1
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# Scatter plot of colors
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fig = plt.figure()
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ax = fig.add_subplot(111, projection='3d')
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ax.set_xlabel('blue')
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ax.set_ylabel('green')
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ax.set_zlabel('red')
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ax.scatter(blue, green, red, zdir='z', s=10, c=(0,0,0), lw = 0, depthshade=True)
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ax.scatter(center[:,2], center[:,1], center[:,0], zdir='z', s=500, facecolors=center, lw = 1, 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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