jsenella / PFE-PilotageDrone

Commit 81de032f26d477a407d8e26fb99da1b0da0688d4

Authored by Justine
1 parent 78fbd752

Ajout de l'application

Showing 43 changed files with 753 additions and 0 deletions   Show diff stats
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Python/Application/ColorObject.py 0 → 100644
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  1 +import cv2
  2 +import numpy as np
  3 +from Parameters import *
  4 +
  5 +def IsolateObject(frame ,Lower, Upper):
  6 + hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
  7 + mask = cv2.inRange(hsv, Lower, Upper)
  8 + kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5))
  9 + mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
  10 + res = cv2.bitwise_and(frame,frame, mask= mask)
  11 + return mask, res
  12 +
  13 +def ColorDisplayResults(liste, nb):
  14 + for i in range (0, len(liste)):
  15 + if i == 0:
  16 + titre = 'mask'+str(nb)
  17 + else:
  18 + titre = 'res'+str(nb)
  19 + cv2.imshow(titre, liste[i])
  20 + return
  21 +
  22 +def CombineColors(color1, color2):
  23 + mask = cv2.bitwise_or(color1[0], color2[0])
  24 + res = cv2.bitwise_or(color1[1], color2[1])
  25 + return mask, res
  26 +
  27 +def ColorChoiceProcessing(image, choix):
  28 + if choix == '1':
  29 + blueMask, blueRes = IsolateObject(image, LowerBlue, UpperBlue)
  30 + return [blueMask, blueRes]
  31 + elif choix == '2':
  32 + greenMask, greenRes = IsolateObject(image, LowerGreen, UpperGreen)
  33 + return [greenMask, greenRes]
  34 + elif choix == '3':
  35 + redMask, redRes = IsolateObject(image, LowerRed, UpperRed)
  36 + return[redMask, redRes]
  37 + elif choix == '4':
  38 + blue = ColorChoiceProcessing(image, '1')
  39 + green = ColorChoiceProcessing(image, '2')
  40 + bgMask, bgRes = CombineColors(blue, green)
  41 + return [bgMask, bgRes]
  42 + elif choix == '5':
  43 + blue = ColorChoiceProcessing(image, '1')
  44 + red = ColorChoiceProcessing(image, '3')
  45 + brMask, brRes = CombineColors(blue, red)
  46 + return [brMask, brRes]
  47 + elif choix == '6':
  48 + red = ColorChoiceProcessing(image, '3')
  49 + green = ColorChoiceProcessing(image, '2')
  50 + rgMask, rgRes = CombineColors(red, green)
  51 + return [rgMask, rgRes]
  52 + elif choix == '7':
  53 + bg = ColorChoiceProcessing(image, '4')
  54 + red = ColorChoiceProcessing(image, '3')
  55 + bgrMask, bgrRes = CombineColors(bg, red)
  56 + return [bgrMask, bgrRes]
  57 + else:
  58 + return
  59 +
  60 +def ColorChoice() :
  61 + image = None
  62 + print('\t\tFind Color Object Menu\n')
  63 + while (image is None):
  64 + image = str(raw_input('\tImage to use ? By default couleur.png \n'))
  65 + if not image:
  66 + image = 'couleur.png'
  67 + image = cv2.imread(str(image))
  68 + print ('\t1. Blue\n\t2. Green\n\t3. Red\n\t4. Blue Green\n\t5. Blue Red\n\t6. Red Green\n\t7. Blue Green Red\n')
  69 + choix = raw_input('\n\tMultiple choices possible\n')
  70 + for i in range (0, len(choix)):
  71 + liste = ColorChoiceProcessing(image, choix[i])
  72 + if liste :
  73 + ColorDisplayResults(liste,choix[i])
  74 + cv2.waitKey(0)
  75 + cv2.destroyAllWindows()
  76 + return
... ...
Python/Application/Contour.py 0 → 100644
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  1 +import cv2
  2 +import numpy as np
  3 +import numpy as np
  4 +import cv2
  5 +
  6 +def FindContours(img):
  7 + imgray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
  8 + #cv2.imshow("Gray", imgray)
  9 + ret,thresh = cv2.threshold(imgray,127,255,0)
  10 + #cv2.imshow("threshold", thresh)
  11 + _,contours,hierarchy = cv2.findContours(thresh, 1, 2)
  12 + return contours
  13 +
  14 +def FindCenterofMass(contours):
  15 + liste = []
  16 + area = []
  17 + centerMass = []
  18 + for i in range (0,len(contours)):
  19 + cnt = contours[i]
  20 + #print cnt
  21 + M = cv2.moments(cnt)
  22 + area.append(cv2.contourArea(cnt))
  23 + tmp = cv2.contourArea(cnt)
  24 + if (M['m00'] != 0) and (tmp > 1000):
  25 + cx = int(M['m10']/M['m00'])
  26 + cy = int(M['m01']/M['m00'])
  27 + centerMass.append((cx,cy))
  28 + else:
  29 + liste.append(i)
  30 + return liste, area, centerMass
  31 +
  32 +
  33 +
  34 +def CleanContours(img):
  35 + contours = FindContours(img)
  36 + liste, _, _ = FindCenterofMass(contours)
  37 + for j in range (0,len(liste)):
  38 + a = liste[j]
  39 + if (a < len(contours)):
  40 + contours.pop(a)
  41 + return contours
  42 +
  43 +def DrawBoundingBox(img):
  44 + contours = FindContours(img)
  45 + for cnt in contours:
  46 + x,y,w,h = cv2.boundingRect(cnt)
  47 + img = cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
  48 + cv2.imshow('Bounding Box', img)
  49 + return img
  50 +
  51 +def DrawEnclosingCircle(img):
  52 + contours = FindContours(img)
  53 + for cnt in contours:
  54 + (x,y),radius = cv2.minEnclosingCircle(cnt)
  55 + center = (int(x),int(y))
  56 + radius = int(radius)
  57 + img = cv2.circle(img,center,radius,(0,255,0),2)
  58 + cv2.imshow('Enclosing Circle', img)
  59 + return img
  60 +
  61 +def DrawContours(img, contours, titre):
  62 + img = cv2.drawContours(img, contours, -1, (0,255,0), 3)
  63 + cv2.imshow(titre, img)
  64 + return img
  65 +
  66 +def DrawCenterofMass(img):
  67 + contours = FindContours(img)
  68 + _, _, centerMass = FindCenterofMass(contours)
  69 + for x, y in centerMass:
  70 + img=cv2.circle(img, (x,y), 10, (124,255,255), 1)
  71 + cv2.imshow('Center of Mass', img)
  72 + return img
  73 +
  74 +def DrawArea(img):
  75 + contours = CleanContours(img)
  76 + img = cv2.drawContours(img, contours, -1, (255, 0, 0), cv2.FILLED)
  77 + cv2.imshow('Area', img)
  78 + return img
  79 +
  80 +def ContourChoiceProcessing(img, choice):
  81 + if choice == '1':
  82 + contours = FindContours(img)
  83 + img = DrawContours(img, contours, 'Contours')
  84 + return img
  85 + elif choice == '2':
  86 + contours = CleanContours(img)
  87 + img = DrawContours(img, contours, 'Clean Contours')
  88 + return img
  89 + elif choice == '3':
  90 + img = DrawCenterofMass(img)
  91 + return img
  92 + elif choice == '4':
  93 + img = DrawArea(img)
  94 + return img
  95 + elif choice == '5':
  96 + img = ContourChoiceProcessing(img, '2')
  97 + img = DrawCenterofMass(img)
  98 + return img
  99 + elif choice == '6':
  100 + img = DrawArea(img)
  101 + img = DrawCenterofMass(img)
  102 + return img
  103 + elif choice == '7':
  104 + img = DrawBoundingBox(img)
  105 + return img
  106 + elif choice == '8':
  107 + img = DrawEnclosingCircle(img)
  108 + return img
  109 + else:
  110 + return
  111 +
  112 +def ContourChoice():
  113 + image = None
  114 + img = None
  115 + print('\t\tContours Features Menu\n')
  116 + while (image is None):
  117 + image = str(raw_input('\tImage to use? By default couleur.png \n'))
  118 + if not image:
  119 + image = 'couleur.png'
  120 + image = cv2.imread(str(image))
  121 + print ('\t1. Contours\n\t2. Clean Contours\n\t3. Center of Mass\n\t4. Area\n\t5. Contours + Center of Mass\n\t6. Area + Center of Mass\n\t7. Bounding Box\n\t8. Enclosing Circle\n')
  122 + choix = raw_input('\n\tMultiple choices possible\n')
  123 + for i in range (0, len(choix)):
  124 + img = image.copy()
  125 + img = ContourChoiceProcessing(img, choix[i])
  126 + if img is not None:
  127 + cv2.waitKey(0)
  128 + cv2.destroyAllWindows()
  129 + return
  130 +
... ...
Python/Application/FondNoir.png 0 → 100644
View file @81de032

28.4 KB
Python/Application/Gradient.py 0 → 100644
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  1 +import cv2
  2 +import numpy as np
  3 +from matplotlib import pyplot as plt
  4 +img = cv2.imread('couleur.png',0)
  5 +
  6 +def Everything (img):
  7 + laplacian = cv2.Laplacian(img,cv2.CV_64F)
  8 + sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=5)
  9 + sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=5)
  10 + plt.subplot(2,2,1),plt.imshow(img,cmap = 'gray')
  11 + plt.title('Original'), plt.xticks([]), plt.yticks([])
  12 + plt.subplot(2,2,2),plt.imshow(laplacian,cmap = 'gray')
  13 + plt.title('Laplacian'), plt.xticks([]), plt.yticks([])
  14 + plt.subplot(2,2,3),plt.imshow(sobelx,cmap = 'gray')
  15 + plt.title('Sobel X'), plt.xticks([]), plt.yticks([])
  16 + plt.subplot(2,2,4),plt.imshow(sobely,cmap = 'gray')
  17 + plt.title('Sobel Y'), plt.xticks([]), plt.yticks([])
  18 + plt.show()
  19 + return laplacian
  20 +
  21 +def Laplacian(img):
  22 + laplacian = cv2.Laplacian(img,cv2.CV_64F)
  23 + plt.plot,plt.imshow(laplacian,cmap = 'gray')
  24 + plt.title('Laplacian'), plt.xticks([]), plt.yticks([])
  25 + plt.show()
  26 + return laplacian
  27 +
  28 +def SobelX(img):
  29 + sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=5)
  30 + plt.plot,plt.imshow(sobelx,cmap = 'gray')
  31 + plt.title('Sobel X'), plt.xticks([]), plt.yticks([])
  32 + plt.show()
  33 + return sobelx
  34 +
  35 +def SobelY(img):
  36 + sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=5)
  37 + plt.plot,plt.imshow(sobely,cmap = 'gray')
  38 + plt.title('Sobel Y'), plt.xticks([]), plt.yticks([])
  39 + plt.show()
  40 + return sobely
  41 +
  42 +def GradientChoiceProcessing(img, choice):
  43 + if choice == '1':
  44 + img = Laplacian(img)
  45 + return img
  46 + elif choice == '2':
  47 + img = SobelX(img)
  48 + return img
  49 + elif choice == '3':
  50 + img = SobelY(img)
  51 + return img
  52 + elif choice == '4':
  53 + img = Everything(img)
  54 + return img
  55 + else:
  56 + return
  57 +
  58 +
  59 +def GradientChoice() :
  60 + image = None
  61 + print('\t\tGradient Menu\n')
  62 + while (image is None):
  63 + image = str(raw_input('\tImage to use? By default couleur.png \n'))
  64 + if not image:
  65 + image = 'couleur.png'
  66 + image = cv2.imread(str(image), 0)
  67 + print ('\t1. Laplacian\n\t2. Sobel X\n\t3. Sobel Y\n\t4. Everything\n')
  68 + choice = raw_input('\n\tMultiple choices possible\n')
  69 + for i in range (0, len(choice)):
  70 + img = image.copy()
  71 + img = GradientChoiceProcessing(img, choice[i])
  72 + return
  73 +
... ...
Python/Application/ImageComponents.py 0 → 100644
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  1 +import cv2
  2 +import numpy as np
  3 +from matplotlib import pyplot as plt
  4 +
  5 +def Corners():
  6 + image = None
  7 + while (image is None):
  8 + image = str(raw_input('\tImage to use ? By default couleur.png \n'))
  9 + if not image:
  10 + image = 'couleur.png'
  11 + image = cv2.imread(str(image))
  12 + img = image.copy()
  13 + gray= cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
  14 + gray = np.float32(gray)
  15 + dst = cv2.cornerHarris(gray, 5, 5, 0.15)
  16 + dst = cv2.dilate(dst, None)
  17 + img[dst>0.01*dst.max()] = [0,0,255]
  18 + cv2.imshow('dst', img)
  19 + cv2.waitKey(0)
  20 + cv2.destroyAllWindows()
  21 + return
  22 +
  23 +def Edges():
  24 + image = None
  25 + while (image is None):
  26 + image = str(raw_input('\tImage to use ? By default couleur.png \n'))
  27 + if not image:
  28 + image = 'couleur.png'
  29 + image = cv2.imread(str(image))
  30 + img = image.copy()
  31 + edges = cv2.Canny(img, 100, 200)
  32 + plt.subplot(121),plt.imshow(img,cmap='gray')
  33 + plt.title('Original Image'), plt.xticks([]), plt.yticks([])
  34 + plt.subplot(122),plt.imshow(edges,cmap = 'gray')
  35 + plt.title('Edge Image'), plt.xticks([]), plt.yticks([])
  36 + plt.show()
  37 + return
... ...
Python/Application/Parameters.py 0 → 100644
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  1 +import numpy as np
  2 +
  3 +LowerBlue = np.array([102,50,50])
  4 +UpperBlue = np.array([107,255,255])
  5 +LowerRed = np.array([0,50,50])
  6 +UpperRed = np.array([11,255,255])
  7 +LowerGreen = np.array([60,50,50])
  8 +UpperGreen = np.array([80, 255, 255])
... ...
Python/Application/ShapeDetection.py 0 → 100644
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  1 +import cv2
  2 +import numpy as np
  3 +import Contour
  4 +
  5 +##!!!!!Faire le pretraitement
  6 +
  7 +#Test : Luminosite, orientation dans l'espace, distance
  8 +#Etudier les limites
  9 +
  10 +def FindCircles():
  11 + image = None
  12 + while (image is None):
  13 + image = str(raw_input('\tImage to use ? By default couleur.png \n'))
  14 + if not image:
  15 + image = 'couleur.png'
  16 + image = cv2.imread(str(image), 0)
  17 + img = cv2.medianBlur(image, 5)
  18 + cimg = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
  19 + circles = cv2.HoughCircles(img, cv2.HOUGH_GRADIENT, 1, 20, param1 = 50, param2 = 30, minRadius = 0, maxRadius = 0)
  20 + circles = np.uint16(np.around(circles))
  21 + for i in circles[0,:]:
  22 + cv2.circle(cimg,(i[0],i[1]),i[2],(0,255,0),2)
  23 + cv2.circle(cimg,(i[0],i[1]),2,(0,0,255),3)
  24 +
  25 + cv2.imshow('detected circles',cimg)
  26 + cv2.waitKey(0)
  27 + cv2.destroyAllWindows()
  28 + return
  29 +
  30 +def FindShapes():
  31 + image = None
  32 + while (image is None):
  33 + image = str(raw_input('\tImage to use ? By default couleur.png \n'))
  34 + if not image:
  35 + image = 'couleur.png'
  36 + image = cv2.imread(str(image))
  37 + height, width, channels = image.shape
  38 + mask = np.zeros((height, width, 3), dtype = "uint8")
  39 + contours = Contour.FindContours(image)
  40 + img = image.copy()
  41 + #Contour.DrawContours(img, contours, "Contours Shape Detection")
  42 + for cnt in contours:
  43 + approx = cv2.approxPolyDP(cnt, 0.01*cv2.arcLength(cnt, True), True)
  44 + if len(approx) == 3 :
  45 + print "Triangle"
  46 + cv2.drawContours(image, [cnt], 0, (255, 255, 0), -1)
  47 + elif len(approx) == 4:
  48 + print "Square"
  49 + cv2.drawContours(image, [cnt], 0, (255, 255, 255), -1)
  50 + elif len(approx) == 5:
  51 + print "Pentagon"
  52 + cv2.drawContours(image, [cnt], 0, (0, 255, 0), -1)
  53 + #elif len(approx) == 8:
  54 + # print "Huit"
  55 + # cv2.drawContours(image, [cnt], 0, (0, 0, 0), -1)
  56 + elif len(approx) > 6 and len(approx) < 9:
  57 + print "Arrow"
  58 + cv2.drawContours(image, [cnt], 0, (255, 0, 255), -1)
  59 + cv2.drawContours(mask, [cnt], 0, (255,255,255), -1)
  60 + elif len(approx) > 19:
  61 + print "Circle"
  62 + cv2.drawContours(image, [cnt], 0, (255, 0, 0), -1)
  63 + cv2.imshow('Mask', mask)
  64 + cv2.imshow('detected shapes', image)
  65 + cv2.waitKey(0)
  66 + cv2.destroyAllWindows()
  67 + return
... ...
Python/Application/Skeleton2.py 0 → 100644
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  1 +import cv2
  2 +import numpy as np
  3 +
  4 +def Skeletization(img, shape, size):
  5 + if shape == '1':
  6 + shape = cv2.MORPH_RECT
  7 + elif shape == '2':
  8 + shape = cv2.MORPH_ELLIPSE
  9 + elif shape == '3':
  10 + shape = cv2.MORPH_CROSS
  11 + else:
  12 + return
  13 +
  14 + cv2.normalize(img, img, 0, 255, cv2.NORM_MINMAX)
  15 + skeleton = np.zeros(img.shape, np.uint8)
  16 + eroded = np.zeros(img.shape, np.uint8)
  17 + temp = np.zeros(img.shape, np.uint8)
  18 +
  19 + _,thresh = cv2.threshold(img, 127, 255, 0)
  20 +
  21 + kernel = cv2.getStructuringElement(shape,(int(size), int(size)))
  22 +
  23 + while(True):
  24 + cv2.erode(thresh, kernel, eroded)
  25 + cv2.dilate(eroded, kernel, temp)
  26 + cv2.subtract(thresh, temp, temp)
  27 + cv2.bitwise_or(skeleton, temp, skeleton)
  28 + thresh, eroded = eroded, thresh
  29 +
  30 + if cv2.countNonZero(thresh) == 0:
  31 + break
  32 +
  33 + kernel = np.ones((20,20), np.uint8)
  34 + skeleton = cv2.morphologyEx(skeleton, cv2.MORPH_CLOSE, kernel)
  35 + cv2.imwrite('DefaultSkeleton.png', skeleton)
  36 + cv2.imshow('skeleton', skeleton)
  37 + return skeleton
  38 +
  39 +def KernelChoice():
  40 + image = None
  41 + size = '0'
  42 + choice = '0'
  43 + print('\t\tSkeleton Menu\n')
  44 + while (image is None):
  45 + image = str(raw_input('\tImage to use? By default FondNoir.png \n'))
  46 + if not image:
  47 + image = 'FondNoir.png'
  48 + image = cv2.imread(str(image),0)
  49 + print('\t1. Rectangle\n\t2. Ellipse\n\t3. Cross\n')
  50 + while choice < '1' or choice > '3':
  51 + choice = raw_input('\n\tKernel Choice. Rectangle by Default\n')
  52 + if not choice:
  53 + choice = '1'
  54 + while int(size) % 2 == 0:
  55 + size = raw_input('\n\tPlease specify the kernel size (Odd number). By default it\'s 3\n')
  56 + if not size:
  57 + size = '3'
  58 + Skeletization(image, choice, size)
  59 +
  60 + cv2.waitKey(0)
  61 + cv2.destroyAllWindows()
  62 +
  63 + return
... ...
Python/Application/Test.py 0 → 100644
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... ... @@ -0,0 +1,45 @@
  1 +import cv2
  2 +import numpy as np
  3 +import Contour
  4 +import ColorObject
  5 +from Parameters import *
  6 +
  7 +def ColoredArrow():
  8 + image = cv2.imread('couleur.png')
  9 + height, width, channels = image.shape
  10 + mask = np.zeros((height, width, 3), dtype = "uint8")
  11 + contours = Contour.FindContours(image)
  12 + for cnt in contours:
  13 + tmp = cv2.contourArea(cnt)
  14 + approx = cv2.approxPolyDP(cnt, 0.01*cv2.arcLength(cnt, True), True)
  15 + if len(approx) > 6 and len(approx) < 9 and tmp > 1000:
  16 + print "Arrow"
  17 + cv2.drawContours(mask, [cnt], 0, (255,255,255), -1)
  18 + cv2.imshow('Mask', mask)
  19 + arrows = cv2.bitwise_and(image, mask)
  20 + cv2.imshow('Arrows', arrows)
  21 + cv2.imwrite('Arrows.png', arrows)
  22 + cv2.waitKey(0)
  23 + cv2.destroyAllWindows()
  24 + return arrows
  25 +
  26 +def FindColor(img):
  27 + height, width, channels = img.shape
  28 + blueMask, blueRes = ColorObject.IsolateObject(img, LowerBlue, UpperBlue)
  29 + redMask, redRes = ColorObject.IsolateObject(img, LowerRed, UpperRed)
  30 + greenMask, greenRes = ColorObject.IsolateObject(img, LowerGreen, UpperGreen)
  31 + greyRes = cv2.bitwise_xor(img, greenRes)
  32 + greyRes = cv2.bitwise_xor(greyRes, redRes)
  33 + greyRes = cv2.bitwise_xor(greyRes, blueRes)
  34 + kernel = np.ones((5,5),np.uint8)
  35 + greyRes = cv2.erode(greyRes,kernel,iterations = 1)
  36 + cv2.imshow('Grey Arrow', greyRes)
  37 + cv2.imshow('Red Arrow', redRes)
  38 + cv2.imshow('Blue Arrow', blueRes)
  39 + cv2.imshow('Green Arrow', greenRes)
  40 + cv2.waitKey(0)
  41 + cv2.destroyAllWindows()
  42 + return
  43 +
  44 +image = ColoredArrow()
  45 +FindColor(image)
... ...
Python/Application/Threshold.py 0 → 100644
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  1 +import cv2
  2 +import numpy as np
  3 +from matplotlib import pyplot as plt
  4 +
  5 +def ThresholdEverything(img):
  6 + ret,th1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY)
  7 + th2 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,11,2)
  8 + th3 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)
  9 + titles = ['Original Image', 'Global Thresholding (v = 127)',
  10 + 'Adaptive Mean Thresholding', 'Adaptive Gaussian Thresholding']
  11 + images = [img, th1, th2, th3]
  12 + for i in xrange(4):
  13 + plt.subplot(2,2,i+1),plt.imshow(images[i],'gray')
  14 + plt.title(titles[i])
  15 + plt.xticks([]),plt.yticks([])
  16 + plt.show()
  17 + return images
  18 +
  19 +def GlobalThresholding(img):
  20 + ret,th1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY)
  21 + plt.plot(), plt.imshow(th1, 'gray')
  22 + plt.title('Global Thresholding (v = 127)')
  23 + plt.show()
  24 + return th1
  25 +
  26 +def AdaptiveMeanThresholding(img):
  27 + th2 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,11,2)
  28 + plt.plot(), plt.imshow(th2, 'gray')
  29 + plt.title('Adaptive Mean Thresholding')
  30 + plt.show()
  31 + return th2
  32 +
  33 +def AdaptiveGaussianThresholding(img):
  34 + th3 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)
  35 + plt.plot(), plt.imshow(th3, 'gray')
  36 + plt.title('Adaptive Gaussian Thresholding')
  37 + plt.show()
  38 + return th3
  39 +
  40 +def ThresholdChoiceProcessing(img, choice):
  41 + if choice == '1':
  42 + img = GlobalThresholding(img)
  43 + return img
  44 + elif choice == '2':
  45 + img = AdaptiveMeanThresholding(img)
  46 + return img
  47 + elif choice == '3':
  48 + img = AdaptiveGaussianThresholding(img)
  49 + return img
  50 + elif choice == '4':
  51 + img = ThresholdEverything(img)
  52 + return img
  53 + else:
  54 + return
  55 +
  56 +
  57 +def ThresholdChoice() :
  58 + image = None
  59 + print('\t\tThreshold Menu\n')
  60 + while (image is None):
  61 + image = str(raw_input('\tImage to use? By default couleur.png \n'))
  62 + if not image:
  63 + image = 'couleur.png'
  64 + image = cv2.imread(str(image), 0)
  65 + img = cv2.medianBlur(image,5)
  66 + print ('\t1. Global Thresholding\n\t2. Adaptive Mean Thresholding\n\t3. Adaptive Gaussian Thresholding\n\t4. Everything\n')
  67 + choice = raw_input('\n\tMultiple choices possible\n')
  68 + for i in range (0, len(choice)):
  69 + img = image.copy()
  70 + img = ThresholdChoiceProcessing(img, choice[i])
  71 + return
  72 +
... ...
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  1 +import ColorObject
  2 +import Contour
  3 +import Gradient
  4 +import Skeleton2
  5 +import Threshold
  6 +import ImageComponents
  7 +import ShapeDetection
  8 +
  9 +def ProcessChoiceMenu(choice):
  10 + if choice == '1':
  11 + ColorObject.ColorChoice()
  12 + elif choice == '2' :
  13 + Skeleton2.KernelChoice()
  14 + elif choice == '3' :
  15 + Contour.ContourChoice()
  16 + elif choice == '4' :
  17 + ImageComponents.Edges()
  18 + elif choice == '5' :
  19 + ImageComponents.Corners()
  20 + elif choice == '6' :
  21 + ShapeDetection.FindCircles()
  22 + elif choice == '7' :
  23 + ShapeDetection.FindShapes()
  24 + elif choice == '8' :
  25 + Gradient.GradientChoice()
  26 + elif choice == '9':
  27 + Threshold.ThresholdChoice()
  28 + else:
  29 + exit()
  30 + return
  31 +
  32 +def MenuApplication():
  33 + print ('\t\t\tMenu Traitement d\'images \n\n')
  34 + print ('\t1. Find a colored object')
  35 + print ('\t2. Skeletisation')
  36 + print ('\t3. Contours')
  37 + print ('\t4. Canny edge detection')
  38 + print ('\t5. Corner detection')
  39 + print ('\t6. Find a circle')
  40 + print ('\t7. Find Basic shapes')
  41 + print ('\t8. Gradient')
  42 + print ('\t9. Threshold')
  43 + choice = raw_input('\n\tChoix multiple possible\n')
  44 + for i in range (0, len(choice)):
  45 + ProcessChoiceMenu(choice[i])
  46 + return
  47 +
  48 +MenuApplication()
  49 +
... ...
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  1 +<div style="-webkit-column-count: 2; -moz-column-count: 2; column-count: 2; -webkit-column-rule: 1px dotted #e0e0e0; -moz-column-rule: 1px dotted #e0e0e0; column-rule: 1px dotted #e0e0e0;">
  2 + <div style="display: inline-block;">
  3 +
  4 +<h1><span style="color:red">OpenCV</span></h1>
  5 +
  6 +
  7 +
  8 +<h4><span style="color:green">Filtre</span></h4>
  9 +<pre>
  10 +filter2D() Filtre linéaire non séparable
  11 +sepFilter2D() Filtre linéaire séparable
  12 +boxFilter(), Lisse une image avec un filtre
  13 +GaussianBlur(), linéaire ou non linéaire
  14 +medianBlur(),
  15 +bilateralFilter().
  16 +Sobel(),Scharr() Calcule les dérivées de l'image spatiale
  17 +Laplatian() Calcule le Laplacien
  18 +exode(),dilate() Opérations morphologiques
  19 +</pre>
  20 +<h4><span style="color:green">Histogrammes</span></h4>
  21 +<pre>
  22 +calcHist() Calcule l'histogramme d'une image
  23 +calcBackProject() Projette l'histogrmme
  24 +equalizeHist() Normalise le contraste et la luminosité
  25 + d'une image
  26 +compareHist() Compare deux histogrammes</pre>
  27 +<h4><span style="color:green">Détection d'objets</span></h4>
  28 +<pre>
  29 +matchTemplate Calcule la carte de distance pour un modèle
  30 + donné
  31 +CascadeClassifier
  32 +HOGDescriptor Détecteur d'objets de N. Dalal utilisant
  33 + l'histogramme des gradients orientés (HOG).
  34 +</pre>
  35 +<h4><span style="color:green">Manipulation de matrices</span></h4>
  36 +<pre>
  37 +src.copyTo(dst) Copie une matrice vers une autre
  38 +m.clone() Fait une copie d'une matrice
  39 +m.reshape(ch,rows) Change les dimensions d'une matrice ou son
  40 + nombre de canaux sans copier les données
  41 +m.row(i),m.col(i) Prend la ligne/colonne d'une matrice
  42 +m.rowRange(Range()) Prend un intervalle de lignes/colonnes
  43 +m.colRange(Range())
  44 +m.diag(i) Prend la diagonale d'une matrice
  45 +m(Range(),Range()) Prend une sous-matrice
  46 +split() Sépare les canaux d'une matrice
  47 +merge() Fusionne plusieurs canaux dans une matrice</pre>
  48 +</div>
  49 +
  50 + <h4> <span style="color:green">Transformations géométriques</span></h4>
  51 + <pre>
  52 + resize() Redimensionne l'image
  53 + getRectSubPix() Extrait un patch
  54 + warpAffine() Déforme une image affinement
  55 + warpPerspective() Déforme une image perspectivement
  56 + remap() Déformation d'image générique
  57 + convertMaps() Optimise les chemins pour une exécution
  58 + plus rapide de remap()</pre>
  59 + <h4> <span style="color:green">Diverses transformations d'image</span></h4>
  60 + <pre>
  61 + cvtColor() Convertit une image d'un espace
  62 + de couleur à l'autre
  63 + threshold() Convertit une image niveau de gris en
  64 + image binaire
  65 + adaptivethreshold() Utilise un seuil fixe ou variable
  66 + floodFill() Trouve un composant connecté en utilisant
  67 + un algorithme de croissance de région
  68 + integral() Calcule l'intégral d'une image
  69 + distanceTransform() Construit une carte de distance ou un
  70 + diagramme discret de Voronir pour une
  71 + image binaire
  72 + watershed() Algorithmes degmentation d'image à base
  73 + grabCut() de marqueurs</pre>
  74 +
  75 + <h4> <span style="color:green">Opérations arithmétiques</span></h4>
  76 + <pre>
  77 + bitwise_and() Opérations booléennes sur deux images
  78 + bitwise_or()
  79 + bitwise_xor()
  80 + add() Additions de deux images
  81 + addWeighted() Mélange de deux images</pre>
  82 + <div style="display: inline-block;">
  83 + <h4> <span style="color:green">Interface Graphique Simple</span></h4>
  84 + <pre>
  85 + destroyWindow(name) Détruit la fenêtre spécifiée
  86 + imshow(name, img) Affiche une image dans une fenêtre
  87 + waitKey(delay) Attend un appui de touche pendant le temps spécifié </pre>
  88 +
  89 + </div>
0 90 \ No newline at end of file
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  1 +import cv2
  2 +import numpy as np
  3 +from matplotlib import pyplot as plt
  4 +
  5 +image = cv2.imread('couleur.png')
  6 +bilateral = cv2.bilateralFilter(image, 5, 75, 75)
  7 +blur = cv2.blur(image, (5, 5))
  8 +box = cv2.boxFilter(image, -1, (5, 5))
  9 +DD = cv2.filter2D(image, -1, (5, 5))
  10 +gaussian = cv2.GaussianBlur(image, (5, 5), 50)
  11 +median = cv2.medianBlur(image, 5)
  12 +sep = cv2.sepFilter2D(image, -1, 75, 75)
  13 +
  14 +kernel = np.array([[-1,-1,-1], [-1,9,-1], [-1,-1,-1]])
  15 +im = cv2.filter2D(image, -1, kernel)
  16 +bilateral2 = cv2.bilateralFilter(im, 5, 150, 150)
  17 +
  18 +plt.subplot(2,3,1),plt.imshow(image)
  19 +plt.title('Original'), plt.xticks([]), plt.yticks([])
  20 +plt.subplot(2,3,2),plt.imshow(bilateral)
  21 +plt.title('Bilateral Filter'), plt.xticks([]), plt.yticks([])
  22 +plt.subplot(2,3,3),plt.imshow(blur)
  23 +plt.title('Blur'), plt.xticks([]), plt.yticks([])
  24 +plt.subplot(2,3,4),plt.imshow(box)
  25 +plt.title('Box Filter'), plt.xticks([]), plt.yticks([])
  26 +plt.subplot(2,3,5),plt.imshow(gaussian)
  27 +plt.title('Gaussian Filter'), plt.xticks([]), plt.yticks([])
  28 +plt.subplot(2,3,6),plt.imshow(median)
  29 +plt.title('Median Blur'), plt.xticks([]), plt.yticks([])
  30 +plt.show()
  31 +
  32 +cv2.imshow('Sharp', im)
  33 +cv2.imshow('Bilateral', bilateral2)
  34 +#cv2.imshow('original', image)
  35 +#cv2.imshow('bilateral filter', bilateral)
  36 +#cv2.imshow('blur', blur)
  37 +#cv2.imshow('box', box)
  38 +#cv2.imshow('filter 2D', DD)
  39 +#cv2.imshow('gaussian filter', gaussian)
  40 +#cv2.imshow('median blur', median)
  41 +#cv2.imshow('sep Filter 2D', sep)
  42 +
  43 +cv2.waitKey(0)
  44 +cv2.destroyAllWindows()
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