Python numpy.int0方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int0 [as 別名]
def getEllipseRotation(image, cnt):
    try:
        # Gets rotated bounding ellipse of contour
        ellipse = cv2.fitEllipse(cnt)
        centerE = ellipse[0]
        # Gets rotation of ellipse; same as rotation of contour
        rotation = ellipse[2]
        # Gets width and height of rotated ellipse
        widthE = ellipse[1][0]
        heightE = ellipse[1][1]
        # Maps rotation to (-90 to 90). Makes it easier to tell direction of slant
        rotation = translateRotation(rotation, widthE, heightE)

        cv2.ellipse(image, ellipse, (23, 184, 80), 3)
        return rotation
    except:
        # Gets rotated bounding rectangle of contour
        rect = cv2.minAreaRect(cnt)
        # Creates box around that rectangle
        box = cv2.boxPoints(rect)
        # Not exactly sure
        box = np.int0(box)
        # Gets center of rotated rectangle
        center = rect[0]
        # Gets rotation of rectangle; same as rotation of contour
        rotation = rect[2]
        # Gets width and height of rotated rectangle
        width = rect[1][0]
        height = rect[1][1]
        # Maps rotation to (-90 to 90). Makes it easier to tell direction of slant
        rotation = translateRotation(rotation, width, height)
        return rotation

#################### FRC VISION PI Image Specific ############# 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int0 [as 別名]
def density_slice(rast, rel=np.less_equal, threshold=1000, nodata=-9999):
    '''
    Returns a density slice from a given raster. Arguments:
        rast        A gdal.Dataset or a NumPy array
        rel         A NumPy logic function; defaults to np.less_equal
        threshold   An integer number
    '''
    # Can accept either a gdal.Dataset or numpy.array instance
    if not isinstance(rast, np.ndarray):
        rastr = rast.ReadAsArray()

    else:
        rastr = rast.copy()

    if (len(rastr.shape) > 2 and min(rastr.shape) > 1):
        raise ValueError('Expected a single-band raster array')

    return np.logical_and(
        rel(rastr, np.ones(rast.shape) * threshold),
        np.not_equal(rastr, np.ones(rast.shape) * nodata)).astype(np.int0) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int0 [as 別名]
def remove_border(contour, ary):
    """Remove everything outside a border contour."""
    # Use a rotated rectangle (should be a good approximation of a border).
    # If it's far from a right angle, it's probably two sides of a border and
    # we should use the bounding box instead.
    c_im = np.zeros(ary.shape)
    r = cv2.minAreaRect(contour)
    degs = r[2]
    if angle_from_right(degs) <= 10.0:
        box = cv2.cv.BoxPoints(r)
        box = np.int0(box)
        cv2.drawContours(c_im, [box], 0, 255, -1)
        cv2.drawContours(c_im, [box], 0, 0, 4)
    else:
        x1, y1, x2, y2 = cv2.boundingRect(contour)
        cv2.rectangle(c_im, (x1, y1), (x2, y2), 255, -1)
        cv2.rectangle(c_im, (x1, y1), (x2, y2), 0, 4)

    return np.minimum(c_im, ary) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int0 [as 別名]
def masks_to_rects(masks):
        rects = []
        for mask in masks:
            decoded_mask = mask
            contours = cv2.findContours(decoded_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[-2]

            areas = []
            boxes = []
            for contour in contours:
                area = cv2.contourArea(contour)
                areas.append(area)

                rect = cv2.minAreaRect(contour)
                box = cv2.boxPoints(rect)
                box = np.int0(box)
                boxes.append(box)

            if areas:
                i = np.argmax(areas)
                rects.append(boxes[i])

        return rects 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int0 [as 別名]
def remove_border(contour, ary):
    """Remove everything outside a border contour."""
    # Use a rotated rectangle (should be a good approximation of a border).
    # If it's far from a right angle, it's probably two sides of a border and
    # we should use the bounding box instead.
    c_im = np.zeros(ary.shape)
    r = cv2.minAreaRect(contour)
    degs = r[2]
    if angle_from_right(degs) <= 10.0:
        box = cv2.boxPoints(r)
        box = np.int0(box)
        cv2.drawContours(c_im, [box], 0, 255, -1)
        cv2.drawContours(c_im, [box], 0, 0, 4)
    else:
        x1, y1, x2, y2 = cv2.boundingRect(contour)
        cv2.rectangle(c_im, (x1, y1), (x2, y2), 255, -1)
        cv2.rectangle(c_im, (x1, y1), (x2, y2), 0, 4)

    return np.minimum(c_im, ary) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int0 [as 別名]
def back_forward_convert(coordinate, with_label=True):
    """
    :param coordinate: format [x1, y1, x2, y2, x3, y3, x4, y4, (label)] 
    :param with_label: default True
    :return: format [x_c, y_c, w, h, theta, (label)]
    """

    boxes = []
    if with_label:
        for rect in coordinate:
            box = np.int0(rect[:-1])
            box = box.reshape([4, 2])
            rect1 = cv2.minAreaRect(box)

            x, y, w, h, theta = rect1[0][0], rect1[0][1], rect1[1][0], rect1[1][1], rect1[2]
            boxes.append([x, y, w, h, theta, rect[-1]])

    else:
        for rect in coordinate:
            box = np.int0(rect)
            box = box.reshape([4, 2])
            rect1 = cv2.minAreaRect(box)

            x, y, w, h, theta = rect1[0][0], rect1[0][1], rect1[1][0], rect1[1][1], rect1[2]
            boxes.append([x, y, w, h, theta])

    return np.array(boxes, dtype=np.float32) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int0 [as 別名]
def draw_box_with_color_rotate(img_batch, boxes, text):

    def draw_box_cv(img, boxes, text):
        img = img + np.array(cfgs.PIXEL_MEAN)
        boxes = boxes.astype(np.int64)
        img = np.array(img * 255 / np.max(img), np.uint8)
        for box in boxes:
            x_c, y_c, w, h, theta = box[0], box[1], box[2], box[3], box[4]
            rect = ((x_c, y_c), (w, h), theta)
            rect = cv2.boxPoints(rect)
            rect = np.int0(rect)
            color = (np.random.randint(255), np.random.randint(255), np.random.randint(255))
            cv2.drawContours(img, [rect], -1, color, 3)

        text = str(text)
        cv2.putText(img,
                    text=text,
                    org=((img.shape[1]) // 2, (img.shape[0]) // 2),
                    fontFace=3,
                    fontScale=1,
                    color=(255, 0, 0))

        img = img[:, :, ::-1]
        return img

    img_tensor = tf.squeeze(img_batch, 0)
    img_tensor_with_boxes = tf.py_func(draw_box_cv,
                                       inp=[img_tensor, boxes, text],
                                       Tout=[tf.uint8])

    img_tensor_with_boxes = tf.reshape(img_tensor_with_boxes, tf.shape(img_batch))

    return img_tensor_with_boxes 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int0 [as 別名]
def bboxes_to_xys(bboxes, image_shape):
    """Convert Seglink bboxes to xys, i.e., eight points
    The `image_shape` is used to to make sure all points return are valid, i.e., within image area
    """
    if len(bboxes) == 0:
        return []
    
    assert np.ndim(bboxes) == 2 and np.shape(bboxes)[-1] == 5, 'invalid `bboxes` param with shape =  ' + str(np.shape(bboxes))
    
    h, w = image_shape[0:2]
    def get_valid_x(x):
        if x < 0:
            return 0
        if x >= w:
            return w - 1
        return x
    
    def get_valid_y(y):
        if y < 0:
            return 0
        if y >= h:
            return h - 1
        return y
    
    xys = np.zeros((len(bboxes), 8))
    for bbox_idx, bbox in enumerate(bboxes):
        bbox = ((bbox[0], bbox[1]), (bbox[2], bbox[3]), bbox[4])
        points = cv2.cv.BoxPoints(bbox)
        points = np.int0(points)
        for i_xy, (x, y) in enumerate(points):
            x = get_valid_x(x)
            y = get_valid_y(y)
            points[i_xy, :] = [x, y]
        points = np.reshape(points, -1)
        xys[bbox_idx, :] = points
    return xys 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int0 [as 別名]
def cfmask(mask, mask_values=(1,2,3,4,255), nodata=-9999):
    '''
    Returns a binary mask according to the CFMask algorithm results for the
    image; mask has True for water, cloud, shadow, and snow (if any) and False
    everywhere else. More information can be found:
        https://landsat.usgs.gov/landsat-surface-reflectance-quality-assessment

    Landsat 4-7 Pre-Collection pixel_qa values to be masked:
        mask_values = (1, 2, 3, 4)

    Landsat 4-7 Collection 1 pixel_qa values to be masked (for "Medium" confidence):
        mask_values = (1, 68, 72, 80, 112, 132, 136, 144, 160, 176, 224)

    Landsat 8 Collection 1 pixel_qa values to be masked (for "Medium" confidence):
        mask_values = (1, 324, 328, 386, 388, 392, 400, 416, 432, 480, 832, 836, 840, 848, 864, 880, 900, 904, 912, 928, 944, 992, 1024)

    Arguments:
        mask        A gdal.Dataset or a NumPy array
        mask_path   The path to an EOS HDF4 CFMask raster
        mask_values The values in the mask that correspond to NoData pixels
        nodata      The NoData value; defaults to -9999.
    '''
    if not isinstance(mask, np.ndarray):
        maskr = mask.ReadAsArray()

    else:
        maskr = mask.copy()

    # Mask according to bit-packing described here:
    # https://landsat.usgs.gov/landsat-surface-reflectance-quality-assessment
    maskr = np.in1d(maskr.reshape((maskr.shape[0] * maskr.shape[1])), mask_values)\
        .reshape((1, maskr.shape[0], maskr.shape[1])).astype(np.int0)

    return maskr 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int0 [as 別名]
def crop_from_points(img, corners, make_square=False):

    cnt = np.array([corners[0], corners[1], corners[2], corners[3]])

    rect = cv2.minAreaRect(cnt)
    center, size, theta = rect

    # Angle correction
    if theta < -45:
        theta += 90
        cnt = np.array([corners[1], corners[3], corners[0], corners[2]])

        rect = (center, size, theta)
        box = cv2.boxPoints(rect)
        box = np.int0(box)

        height = int(rect[1][0])
        width = int(rect[1][1])
    else:
        rect = (center, size, theta)
        box = cv2.boxPoints(rect)
        box = np.int0(box)

        height = int(rect[1][1])
        width = int(rect[1][0])

    src_pts = np.float32([corners[0],corners[1],corners[2],corners[3]])
    dst_pts = np.float32([[0,0],[width,0],[0,height],[width,height]])

    M = cv2.getPerspectiveTransform(src_pts, dst_pts)
    warped = cv2.warpPerspective(img, M, (width, height))

    transformation_data = {
        'matrix': M,
        'original_shape': (height, width)
    }

    return warped, transformation_data 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int0 [as 別名]
def make_r_gt_mask(fet_h, fet_w, img_h, img_w, gtboxes):
    gtboxes = np.reshape(gtboxes, [-1, 6])  # [x, y, w, h, theta, label]

    areas = gtboxes[:, 2] * gtboxes[:, 3]
    arg_areas = np.argsort(-1 * areas)  # sort from large to small
    gtboxes = gtboxes[arg_areas]

    fet_h, fet_w = int(fet_h), int(fet_w)
    mask = np.zeros(shape=[fet_h, fet_w], dtype=np.int32)
    for a_box in gtboxes:
        # print(a_box)
        box = cv2.boxPoints(((a_box[0], a_box[1]), (a_box[2], a_box[3]), a_box[4]))
        box = np.reshape(box, [-1, ])
        label = a_box[-1]
        new_box = []
        for i in range(8):
            if i % 2 == 0:
                x = box[i]
                new_x = int(x * fet_w / float(img_w))
                new_box.append(new_x)
            else:
                y = box[i]
                new_y = int(y*fet_h/float(img_h))
                new_box.append(new_y)

        new_box = np.int0(new_box).reshape([4, 2])
        color = int(label)
        # print(type(color), color)
        cv2.fillConvexPoly(mask, new_box, color=color)
    # print (mask.dtype)
    return mask 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int0 [as 別名]
def backward_convert(coordinate, with_label=True):
    """
    :param coordinate: format [x1, y1, x2, y2, x3, y3, x4, y4, (label)]
    :param with_label: default True
    :return: format [x_c, y_c, w, h, theta, (label)]
    """

    boxes = []
    if with_label:
        for rect in coordinate:
            box = np.int0(rect[:-1])
            box = box.reshape([4, 2])
            rect1 = cv2.minAreaRect(box)

            x, y, w, h, theta = rect1[0][0], rect1[0][1], rect1[1][0], rect1[1][1], rect1[2]
            boxes.append([x, y, w, h, theta, rect[-1]])

    else:
        for rect in coordinate:
            box = np.int0(rect)
            box = box.reshape([4, 2])
            rect1 = cv2.minAreaRect(box)

            x, y, w, h, theta = rect1[0][0], rect1[0][1], rect1[1][0], rect1[1][1], rect1[2]
            boxes.append([x, y, w, h, theta])

    return np.array(boxes, dtype=np.float32) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int0 [as 別名]
def draw_a_rectangel_in_img(draw_obj, box, color, width, method):
    '''
    use draw lines to draw rectangle. since the draw_rectangle func can not modify the width of rectangle
    :param draw_obj:
    :param box: [x1, y1, x2, y2]
    :return:
    '''
    if method == 0:
        x1, y1, x2, y2 = box[0], box[1], box[2], box[3]
        top_left, top_right = (x1, y1), (x2, y1)
        bottom_left, bottom_right = (x1, y2), (x2, y2)

        draw_obj.line(xy=[top_left, top_right],
                      fill=color,
                      width=width)
        draw_obj.line(xy=[top_left, bottom_left],
                      fill=color,
                      width=width)
        draw_obj.line(xy=[bottom_left, bottom_right],
                      fill=color,
                      width=width)
        draw_obj.line(xy=[top_right, bottom_right],
                      fill=color,
                      width=width)
    else:
        x_c, y_c, w, h, theta = box[0], box[1], box[2], box[3], box[4]
        rect = ((x_c, y_c), (w, h), theta)
        rect = cv2.boxPoints(rect)
        rect = np.int0(rect)
        draw_obj.line(xy=[(rect[0][0], rect[0][1]), (rect[1][0], rect[1][1])],
                      fill=color,
                      width=width)
        draw_obj.line(xy=[(rect[1][0], rect[1][1]), (rect[2][0], rect[2][1])],
                      fill=color,
                      width=width)
        draw_obj.line(xy=[(rect[2][0], rect[2][1]), (rect[3][0], rect[3][1])],
                      fill=color,
                      width=width)
        draw_obj.line(xy=[(rect[3][0], rect[3][1]), (rect[0][0], rect[0][1])],
                      fill=color,
                      width=width) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int0 [as 別名]
def mask_to_bbox(mask):
    img_box = np.zeros_like(mask)
    _, cnt, _ = cv2.findContours(mask, 1, 2)
    rect = cv2.minAreaRect(cnt[0])
    box = cv2.boxPoints(rect)
    box = np.int0(box)
    cv2.drawContours(img_box, [box], 0, 1, -1)
    return img_box 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import int0 [as 別名]
def detectRoundel( frame, debug=False ):
    global g_mser
    global THRESHOLD_FRACTION
    if g_mser == None:
        g_mser = cv2.MSER( _delta = 10, _min_area=100, _max_area=300*50*2 )
    gray = cv2.cvtColor( frame, cv2.COLOR_BGR2GRAY )
    contours = g_mser.detect(gray, None)
    rectangles = []
    circles = []
    for cnt in contours:
        rect = cv2.minAreaRect(cnt)
        area = len(cnt) # MSER returns all points within area, not boundary points
        rectangleArea = float(rect[1][0]*rect[1][1])
        rectangleAspect = max(rect[1][0], rect[1][1]) / float(min(rect[1][0], rect[1][1]))
        if area/rectangleArea > 0.70 and rectangleAspect > 3.0:
            (x,y),(w,h),angle = rect
            rectangles.append( ((int(x+0.5),int(y+0.5)), (int(w+0.5),int(h+0.5)), int(angle)) )
        cir = cv2.minEnclosingCircle(cnt)
        (x,y),radius = cir
        circleArea = math.pi*radius*radius
        if area/circleArea > 0.64:
            circles.append( ((int(x+0.5),int(y+0.5)),int(radius+0.5)) )
    rectangles = removeDuplicities( rectangles )
    result = matchCircRect( circles=circles, rectangles=rectangles )
    if debug:
        for rect in rectangles:
            box = cv2.cv.BoxPoints(rect)
            box = np.int0(box)
            cv2.drawContours( frame,[box],0,(255,0,0),2)
        for cir in circles:
            (x,y),radius = cir
            center = (int(x),int(y))
            radius = int(radius)
            cv2.circle(frame, center, radius, (0,255,0), 2)
        if result:
            (x1,y1),(x2,y2) = result
            cv2.line(frame, (int(x1),int(y1)), (int(x2),int(y2)), (0,0,255), 3)
    return result