python类contourArea()的实例源码

def get_largest(im, n):
    # Find contours of the shape
    major = cv2.__version__.split('.')[0]
    if major == '3':
        _, contours, _ = cv2.findContours(im.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    else:
        contours, _ = cv2.findContours(im.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    # Cycle through contours and add area to array
    areas = []
    for c in contours:
        areas.append(cv2.contourArea(c))

    # Sort array of areas by size
    sorted_areas = sorted(zip(areas, contours), key=lambda x: x[0], reverse=True)

    if sorted_areas and len(sorted_areas) >= n:
        # Find nth largest using data[n-1][1]
        return sorted_areas[n - 1][1]
    else:
        return None

def classify(img):
    cimg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    img2 = cv2.medianBlur(cimg, 13)

    ret, thresh1 = cv2.threshold(cimg, 100, 120, cv2.THRESH_BINARY)
    t2 = copy.copy(thresh1)

    x, y = thresh1.shape
    arr = np.zeros((x, y, 3), np.uint8)
    final_contours = []
    image, contours, hierarchy = cv2.findContours(t2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    #cv2.imshow('image', image)
    #k = cv2.waitKey(0)
    for i in range(len(contours)):
        cnt = contours[i]
        if cv2.contourArea(cnt) > 35000 and cv2.contourArea(cnt) < 15000:
            cv2.drawContours(img, [cnt], -1, [0, 255, 255])
            cv2.fillConvexPoly(arr, cnt, [255, 255, 255])
            final_contours.append(cnt)
    cv2.imshow('arr', arr)
    k = cv2.waitKey(0)
    return arr

def hand_contour_find(contours):
    max_area=0
    largest_contour=-1
    for i in range(len(contours)):
        cont=contours[i]
        area=cv2.contourArea(cont)
        if(area>max_area):
            max_area=area
            largest_contour=i
    if(largest_contour==-1):
        return False,0
    else:
        h_contour=contours[largest_contour]
        return True,h_contour

# 4. Detect & mark fingers

def remove_blobs(image, min_area=0, max_area=sys.maxsize, threshold=128,
                 method='8-connected', return_mask=False):
    """Binarize image using threshold, and remove (turn into black)
    blobs of connected pixels of white of size bigger or equal than
    min_area but smaller or equal than max_area from the original image,
    returning it afterward."""
    method = method.lower()
    if method == '4-connected':
        method = cv2.LINE_4
    elif method in ('16-connected', 'antialiased'):
        method = cv2.LINE_AA
    else:  # 8-connected
        method = cv2.LINE_8
    mono_image = binarize_image(image, method='boolean', threshold=threshold)
    _, all_contours, _ = cv2.findContours(mono_image, cv2.RETR_LIST,
                                          cv2.CHAIN_APPROX_SIMPLE)
    contours = np.array([contour for contour in all_contours
                         if min_area <= cv2.contourArea(contour) <= max_area])
    mask = np.ones(mono_image.shape, np.uint8)
    cv2.drawContours(mask, contours, -1, 0, -1, lineType=method)
    return image, 255 * mask

def contourImg(image):
    #Find contours in the image the first and last returns dont matter so the _ is just a placeholder to ignore them
    _, contours, _ = cv2.findContours(image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    #The contouring operation does some weird stuff to the image so this line just fills the whole thing with black
    image.fill(0)
    boundingRect = []
    #Loops through all contours bigger than minArea pixels. That number is tweakable and determined by testing
    for j in [i for i in contours if cv2.contourArea(i) > minArea]:
        #br is a (list/tuple)? of the form x, y, width, height where (x,y) is the (top/bottom)? (left/right)? corner
        br = cv2.boundingRect(j)
        if(abs(br[2]/br[3] - INDASPECT) < indAspectTol and cv2.contourArea(j)/(br[2]*br[3]) > covTol):
            boundingRect.append(br)
    for x in range(0, len(boundingRect)):
        for y in range(x+1, len(boundingRect)):
            i = boundingRect[x]
            j = boundingRect[y]
            if(abs(i[1]-j[1]) < i[3]/2) and abs(abs(i[0]-j[0])/i[1] - GRPASPECT) < grpAspectTol:
                return [createRectCnt(i), createRectCnt(j)]
    return None

def diagContour(image):
     #Find contours in the image the first and last returns dont matter so the _ is just a placeholder to ignore them
    _, contours, _ = cv2.findContours(image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    #The contouring operation does some weird stuff to the image so this line just fills the whole thing with black
    image.fill(0)
    boundingRect = []
    firstFail = []
    #Loops through all contours bigger than minArea pixels. That number is tweakable and determined by testing
    for j in [i for i in contours if cv2.contourArea(i) > minArea]:
        #br is a (list/tuple)? of the form x, y, width, height where (x,y) is the (top/bottom)? (left/right)? corner
        br = cv2.boundingRect(j)
        if(abs(br[2]/br[3] - INDASPECT) < indAspectTol and cv2.contourArea(j)/(br[2]*br[3]) > covTol):
            boundingRect.append(br)
        else:
            firstFail.append([br, br[2]/br[3], cv2.contourArea(j)/(br[2]*br[3])])
    secondRound = []
    for x in range(0, len(boundingRect)):
        for y in range(x+1, len(boundingRect)):
            i = boundingRect[x]
            j = boundingRect[y]
            secondRound.append([(x,y,i,j), (abs(i[1]-j[1]), i[3]/2), abs(i[0]-j[0])/i[1]])
    for x in secondRound:
        if(x[1][0] < x[1][1] and x[2] - GRPASPECT < grpAspectTol):
            return firstFail, secondRound, [createRectCnt(x[0][2]), createRectCnt(x[0][3])]
    return firstFail, secondRound, None

def findContours(arg_img,arg_canvas, arg_MinMaxArea=False, arg_debug= False):
    image= arg_img.copy()
    #print image
    canvas= arg_canvas.copy()
    if len(image)==3:
        image = cv2.cvtColor(self.image, cv2.COLOR_GRAY2BGR)
    if sys.version_info.major == 2: 
        ctrs, hier = cv2.findContours(image.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    else:
        _, ctrs, hier = cv2.findContours(image.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

    if arg_MinMaxArea is not False:
        ctrs = filter(lambda x : arg_MinMaxArea[1]> cv2.contourArea(x) > arg_MinMaxArea[0] , ctrs)

    print '>>> ', len(ctrs)
    for ctr in ctrs:
        print 'Area: ', cv2.contourArea(ctr)
        cv2.drawContours(canvas, [ctr], 0, (0, 128, 255), 3)
    if arg_debug:
        cv2.imwrite('Debug/debug_findContours.jpg',canvas)
    return canvas

def get_contour(self, arg_frame, arg_export_index, arg_export_path, arg_export_filename, arg_binaryMethod):
        # Otsu's thresholding after Gaussian filtering
        tmp = cv2.cvtColor(arg_frame, cv2.COLOR_RGB2GRAY)
        blur = cv2.GaussianBlur(tmp,(5,5),0)
        if arg_binaryMethod== 0:
            ret, thresholdedImg= cv2.threshold(blur.copy() , self.threshold_graylevel, 255 , 0)
        elif arg_binaryMethod == 1:
            ret,thresholdedImg = cv2.threshold(blur.copy(),0 ,255 ,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
        elif arg_binaryMethod== 2:
            thresholdedImg = cv2.adaptiveThreshold(blur.copy(),255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,5,0)

        result = cv2.cvtColor(thresholdedImg, cv2.COLOR_GRAY2RGB)
        ctrs, hier = cv2.findContours(thresholdedImg, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

        ctrs = filter(lambda x : cv2.contourArea(x) > self.threshold_size , ctrs)

        rects = [[cv2.boundingRect(ctr) , ctr] for ctr in ctrs]

        for rect , cntr in rects:
            cv2.drawContours(result, [cntr], 0, (0, 128, 255), 3)
        if arg_export_index:
            cv2.imwrite(arg_export_path+ arg_export_filename+'.jpg', result)
        print "Get Contour success"
        return result

def get_bounding_rect( cap, win_cap, win, upper, lower):
    msk = cv2.dilate(cv2.erode( cv2.inRange( cv2.blur( cv2.cvtColor( cap, cv2.COLOR_BGR2HSV ), (5,5) ), np.array(lower), np.array(upper) ), None, iterations=3), None, iterations=3)
    im2, contours, hierarchy = cv2.findContours( msk, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE )
    if len(contours) > 0:
        areas = [cv2.contourArea(c) for c in contours] # get the area of each contour
        max_index = np.argmax(areas) # get the index of the largest contour by area
        cnts = contours[max_index] # get the largest contout by area
        cv2.drawContours(msk, [cnts], 0, (0,255,0), 3) # Draw the contours to the mask image
        x,y,w,h = cv2.boundingRect(cnts) #  get the bouding box information about the contour
        cv2.rectangle(win_cap,(x,y),(x+w,y+h),(255,255,255),2) # Draw rectangle on the image to represent the bounding box
        cv2.imshow( "debug.", win_cap )
        try:
            self.smt_dash.putNumber('vis_x', x)
            self.smt_dash.putNumber('vis_y', y)
            self.smt_dash.putNumber('vis_w', w)
            self.smt_dash.putNumber('vis_h', h)
        except Exception:
            pass

def find_largest_contour_index(contours):
    """
    Finds and returns the index of the largest contour from a list of contours.
    Returs `None` if the contour list is empty.
    """
    if len(contours) <= 0:
        log_message = "The length of contour lists is non-positive!"
        raise Exception(log_message)

    largest_contour_index = 0

    contour_iterator = 1
    while contour_iterator < len(contours):
        if cv2.contourArea(contours[contour_iterator]) > cv2.contourArea(contours[largest_contour_index]):
            largest_contour_index = contour_iterator
        contour_iterator += 1

    return largest_contour_index

def get_contours(image, polydb=0.03, contour_range=5, show=False):
    # find the contours in the edged image, keeping only the largest ones, and initialize the screen contour
    # if cv2version == 3: im2, contours, hierarchy = cv2.findContours(image.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
    contours = _findContours(image, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
    cnts = sorted(contours, key = cv2.contourArea, reverse = True)[:contour_range]

    # loop over the contours
    screenCnt = None
    for c in cnts:
        # approximate the contour
        peri = cv2.arcLength(c, True) #finds the Contour Perimeter
        approx = cv2.approxPolyDP(c, polydb * peri, True)

        # if our approximated contour has four points, then we can assume that we have found our screen
        if len(approx) == 4:
            screenCnt = approx
            break

    if screenCnt is None: raise EdgeNotFound()

    # sometimes the algorythm finds a strange non-convex shape. The shape conforms to the card but its not complete, so then just complete the shape into a convex form
    if not cv2.isContourConvex(screenCnt):
        screenCnt = cv2.convexHull(screenCnt)
        x,y,w,h = cv2.boundingRect(screenCnt)
        screenCnt = np.array([[[x, y]], [[x+w, y]], [[x+w, y+h]], [[x, y+h]]])

    if show: #this is for debugging puposes
        new_image = image.copy()
        cv2.drawContours(new_image, [screenCnt], -1, (255, 255, 0), 2)
        cv2.imshow("Contour1 image", new_image)
        cv2.waitKey(0)
        cv2.destroyAllWindows()

    return screenCnt

def get_contours(image, polydb=0.03, contour_range=5, show=False):
    # find the contours in the edged image, keeping only the largest ones, and initialize the screen contour
    if cv2version == 3: im2, contours, hierarchy = cv2.findContours(image.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
    cnts = sorted(contours, key = cv2.contourArea, reverse = True)[:contour_range]

    # loop over the contours
    screenCnt = None
    for c in cnts:
        # approximate the contour
        peri = cv2.arcLength(c, True) #finds the Contour Perimeter
        approx = cv2.approxPolyDP(c, polydb * peri, True)

        # if our approximated contour has four points, then we can assume that we have found our screen
        if len(approx) == 4:
            screenCnt = approx
            break

    if screenCnt is None: raise EdgeNotFound()

    # sometimes the algorythm finds a strange non-convex shape. The shape conforms to the card but its not complete, so then just complete the shape into a convex form
    if not cv2.isContourConvex(screenCnt):
        screenCnt = cv2.convexHull(screenCnt)
        x,y,w,h = cv2.boundingRect(screenCnt)
        screenCnt = np.array([[[x, y]], [[x+w, y]], [[x+w, y+h]], [[x, y+h]]])

    if show: #this is for debugging puposes
        new_image = image.copy()
        cv2.drawContours(new_image, [screenCnt], -1, (255, 255, 0), 2)
        cv2.imshow("Contour1 image", new_image)
        cv2.waitKey(0)
        cv2.destroyAllWindows()

    return screenCnt

def get_contours(image, polydb=0.1, contour_range=7, show=False):
    # find the contours in the edged image, keeping only the largest ones, and initialize the screen contour
    contours = _findContours(image, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
    cnts = sorted(contours, key = cv2.contourArea, reverse = True)[:contour_range]

    # loop over the contours
    screenCnt = None
    for c in cnts:
        # approximate the contour
        peri = cv2.arcLength(c, True) #finds the Contour Perimeter
        approx = cv2.approxPolyDP(c, polydb * peri, True)

        # if our approximated contour has four points, then we can assume that we have found our screen
        if len(approx) == 4:
            screenCnt = approx
            break

    if screenCnt is None:
        raise EdgeNotFound()

    # sometimes the algorythm finds a strange non-convex shape. The shape conforms to the card but its not complete, so then just complete the shape into a convex form
    if not cv2.isContourConvex(screenCnt):
        screenCnt = cv2.convexHull(screenCnt)
        x,y,w,h = cv2.boundingRect(screenCnt)
        screenCnt = numpy.array([[[x, y]], [[x+w, y]], [[x+w, y+h]], [[x, y+h]]])

    if show: #this is for debugging puposes
        new_image = image.copy()
        cv2.drawContours(new_image, [screenCnt], -1, (255, 255, 0), 2)
        cv2.imshow("Contour1 image", new_image)
        cv2.waitKey(0)
        cv2.destroyAllWindows()

    return screenCnt

def get_contours(image, polydb=0.03, contour_range=7, show=False):
    # find the contours in the edged image, keeping only the largest ones, and initialize the screen contour
    # if cv2version == 3: im2, contours, hierarchy = cv2.findContours(image.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
    contours = _findContours(image, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
    cnts = sorted(contours, key = cv2.contourArea, reverse = True)[:contour_range]

    # loop over the contours
    screenCnt = None
    for c in cnts:
        # approximate the contour
        peri = cv2.arcLength(c, True) #finds the Contour Perimeter
        approx = cv2.approxPolyDP(c, polydb * peri, True)

        # if our approximated contour has four points, then we can assume that we have found our screen
        if len(approx) == 4:
            screenCnt = approx
            break

    if screenCnt is None: raise EdgeNotFound()

    # sometimes the algorythm finds a strange non-convex shape. The shape conforms to the card but its not complete, so then just complete the shape into a convex form
    if not cv2.isContourConvex(screenCnt):
        screenCnt = cv2.convexHull(screenCnt)
        x,y,w,h = cv2.boundingRect(screenCnt)
        screenCnt = numpy.array([[[x, y]], [[x+w, y]], [[x+w, y+h]], [[x, y+h]]])

    if show: #this is for debugging puposes
        new_image = image.copy()
        cv2.drawContours(new_image, [screenCnt], -1, (255, 255, 0), 2)
        cv2.imshow("Contour1 image", new_image)
        cv2.waitKey(0)
        cv2.destroyAllWindows()

    return screenCnt

def find_self():
    low_white = np.array([0,0,255])
    upper_white = np.array([1,255,255])

    mask,mmx, mmy = get_mini_map_mask(low_white,upper_white)

    _, contours, _ = cv2.findContours(mask.copy(), 1,2)

    for cnt in contours:
        M = cv2.moments(cnt)
        print(cv2.contourArea(cnt))
        if cv2.contourArea(cnt) == 4:
            #centroid from img moments
            cx = int(M['m10']/M['m00'])
            cy = int(M['m01']/M['m00'])
            print(cx,cy)

    cv2.imshow('img', mask)
    cv2.waitKey(0)

def find_yellow_birds():
    ps, psx, psy = RS.getPlayingScreen()

    lower_pink = np.array([28,197,168])
    upper_pink = np.array([29,234,239])

    mask = cv2.inRange(ps, lower_pink, upper_pink)

    #cv2.imshow('img', mask)
    #cv2.waitKey(0)

    _, contours, _ = cv2.findContours(mask, 1,2)

    # returns true if birds found 
    for cnt in contours:
        if cv2.contourArea(cnt) > 0:
            return 1

def cropCircle(img):
    '''
    there many imaged taken thresholded, which means many images is
    present as a circle with black surrounded. This function is to
    find the largest inscribed rectangle to the thresholed image and
    then crop the image to the rectangle.

    input: img - the cv2 module

    return: img_crop, rectangle, tile_size
    '''
    if(img.shape[0] > img.shape[1]):
        tile_size = (int(img.shape[1]*256/img.shape[0]),256)
    else:
        tile_size = (256, int(img.shape[0]*256/img.shape[1]))

    img = cv2.resize(img, dsize=tile_size)

    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY);
    _, thresh = cv2.threshold(gray, 10, 255, cv2.THRESH_BINARY)

    _, contours, _ = cv2.findContours(thresh.copy(),cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)

    main_contour = sorted(contours, key = cv2.contourArea, reverse = True)[0]

    ff = np.zeros((gray.shape[0],gray.shape[1]), 'uint8')
    cv2.drawContours(ff, main_contour, -1, 1, 15)
    ff_mask = np.zeros((gray.shape[0]+2,gray.shape[1]+2), 'uint8')
    cv2.floodFill(ff, ff_mask, (int(gray.shape[1]/2), int(gray.shape[0]/2)), 1)

    rect = maxRect(ff)
    rectangle = [min(rect[0],rect[2]), max(rect[0],rect[2]), min(rect[1],rect[3]), max(rect[1],rect[3])]
    img_crop = img[rectangle[0]:rectangle[1], rectangle[2]:rectangle[3]]
    cv2.rectangle(ff,(min(rect[1],rect[3]),min(rect[0],rect[2])),(max(rect[1],rect[3]),max(rect[0],rect[2])),3,2)

    return [img_crop, rectangle, tile_size]

def cropCircle(img):
    '''
    there many imaged taken thresholded, which means many images is
    present as a circle with black surrounded. This function is to
    find the largest inscribed rectangle to the thresholed image and
    then crop the image to the rectangle.

    input: img - the cv2 module

    return: img_crop, rectangle, tile_size
    '''
    if(img.shape[0] > img.shape[1]):
        tile_size = (int(img.shape[1]*256/img.shape[0]),256)
    else:
        tile_size = (256, int(img.shape[0]*256/img.shape[1]))

    img = cv2.resize(img, dsize=tile_size)

    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY);
    _, thresh = cv2.threshold(gray, 10, 255, cv2.THRESH_BINARY)

    _, contours, _ = cv2.findContours(thresh.copy(),cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)

    main_contour = sorted(contours, key = cv2.contourArea, reverse = True)[0]

    ff = np.zeros((gray.shape[0],gray.shape[1]), 'uint8')
    cv2.drawContours(ff, main_contour, -1, 1, 15)
    ff_mask = np.zeros((gray.shape[0]+2,gray.shape[1]+2), 'uint8')
    cv2.floodFill(ff, ff_mask, (int(gray.shape[1]/2), int(gray.shape[0]/2)), 1)

    rect = maxRect(ff)
    rectangle = [min(rect[0],rect[2]), max(rect[0],rect[2]), min(rect[1],rect[3]), max(rect[1],rect[3])]
    img_crop = img[rectangle[0]:rectangle[1], rectangle[2]:rectangle[3]]
    cv2.rectangle(ff,(min(rect[1],rect[3]),min(rect[0],rect[2])),(max(rect[1],rect[3]),max(rect[0],rect[2])),3,2)

    return [img_crop, rectangle, tile_size]

def find_display_contour(edge_img_arr):
  display_contour = None
  edge_copy = edge_img_arr.copy()
  contours,hierarchy = cv2.findContours(edge_copy, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
  top_cntrs = sorted(contours, key = cv2.contourArea, reverse = True)[:10]

  for cntr in top_cntrs:
    peri = cv2.arcLength(cntr,True)
    approx = cv2.approxPolyDP(cntr, 0.02 * peri, True)

    if len(approx) == 4:
      display_contour = approx
      break

  return display_contour