Python cv2.COLOR_GRAY2BGR屬性代碼示例

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import COLOR_GRAY2BGR [as 別名]
def contour_filter(self, frame):
        _, contours, _ = cv2.findContours(frame,
            cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

        new_frame = np.zeros(frame.shape, np.uint8)
        for i, contour in enumerate(contours):
            c_area = cv2.contourArea(contour)
            if self.contour_min_area <= c_area <= self.contour_max_area:
                mask = np.zeros(frame.shape, np.uint8)
                cv2.drawContours(mask, contours, i, 255, cv2.FILLED)
                mask = cv2.bitwise_and(frame, mask)
                new_frame = cv2.bitwise_or(new_frame, mask)
        frame = new_frame

        if self.contour_disp_flag:
            frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR)
            cv2.drawContours(frame, contours, -1, (255, 0, 0), 1)

        return frame


    # A number of methods corresponding to the various trackbars available. 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import COLOR_GRAY2BGR [as 別名]
def draw_outputs(img, outputs, class_names=None):
    boxes, objectness, classes = outputs
    #boxes, objectness, classes = boxes[0], objectness[0], classes[0]
    wh = np.flip(img.shape[0:2])
    if img.ndim == 2 or img.shape[2] == 1:
        img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
    min_wh = np.amin(wh)
    if min_wh <= 100:
        font_size = 0.5
    else:
        font_size = 1
    for i in range(classes.shape[0]):
        x1y1 = tuple((np.array(boxes[i][0:2]) * wh).astype(np.int32))
        x2y2 = tuple((np.array(boxes[i][2:4]) * wh).astype(np.int32))
        img = cv2.rectangle(img, x1y1, x2y2, (255, 0, 0), 1)
        img = cv2.putText(img, '{}'.format(int(classes[i])), x1y1, cv2.FONT_HERSHEY_COMPLEX_SMALL, font_size,
                          (0, 0, 255), 1)
    return img 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import COLOR_GRAY2BGR [as 別名]
def draw_labels(x, y, class_names=None):
    img = x.numpy()
    if img.ndim == 2 or img.shape[2] == 1:
        img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
    boxes, classes = tf.split(y, (4, 1), axis=-1)
    classes = classes[..., 0]
    wh = np.flip(img.shape[0:2])
    min_wh = np.amin(wh)
    if min_wh <= 100:
        font_size = 0.5
    else:
        font_size = 1
    for i in range(len(boxes)):
        x1y1 = tuple((np.array(boxes[i][0:2]) * wh).astype(np.int32))
        x2y2 = tuple((np.array(boxes[i][2:4]) * wh).astype(np.int32))
        img = cv2.rectangle(img, x1y1, x2y2, (255, 0, 0), 1)
        if class_names:
            img = cv2.putText(img, class_names[classes[i]], x1y1, cv2.FONT_HERSHEY_COMPLEX_SMALL, font_size,
                              (0, 0, 255), 1)
        else:
            img = cv2.putText(img, str(classes[i]), x1y1, cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 1)
    return img 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import COLOR_GRAY2BGR [as 別名]
def blend_transparent(face_img, overlay_t_img):
    # Split out the transparency mask from the colour info
    overlay_img = overlay_t_img[:, :, :3]  # Grab the BRG planes
    overlay_mask = overlay_t_img[:, :, 3:]  # And the alpha plane

    # Again calculate the inverse mask
    background_mask = 255 - overlay_mask

    # Turn the masks into three channel, so we can use them as weights
    overlay_mask = cv2.cvtColor(overlay_mask, cv2.COLOR_GRAY2BGR)
    background_mask = cv2.cvtColor(background_mask, cv2.COLOR_GRAY2BGR)

    # Create a masked out face image, and masked out overlay
    # We convert the images to floating point in range 0.0 - 1.0
    face_part = (face_img * (1 / 255.0)) * (background_mask * (1 / 255.0))
    overlay_part = (overlay_img * (1 / 255.0)) * (overlay_mask * (1 / 255.0))

    # And finally just add them together, and rescale it back to an 8bit integer image
    return np.uint8(cv2.addWeighted(face_part, 255.0, overlay_part, 255.0, 0.0)) 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import COLOR_GRAY2BGR [as 別名]
def evaluate_model(model, digits, samples, labels):
    resp = model.predict(samples)
    err = (labels != resp).mean()
    print('error: %.2f %%' % (err*100))

    confusion = np.zeros((10, 10), np.int32)
    for i, j in zip(labels, resp):
        confusion[i, int(j)] += 1
    print('confusion matrix:')
    print(confusion)
    print()

    vis = []
    for img, flag in zip(digits, resp == labels):
        img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
        if not flag:
            img[...,:2] = 0
        vis.append(img)
    return mosaic(25, vis) 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import COLOR_GRAY2BGR [as 別名]
def blend_non_transparent(sprite, background_img):
    gray_overlay = cv2.cvtColor(background_img, cv2.COLOR_BGR2GRAY)
    overlay_mask = cv2.threshold(gray_overlay, 1, 255, cv2.THRESH_BINARY)[1]

    overlay_mask = cv2.erode(overlay_mask, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)))
    overlay_mask = cv2.blur(overlay_mask, (3, 3))

    background_mask = 255 - overlay_mask

    overlay_mask = cv2.cvtColor(overlay_mask, cv2.COLOR_GRAY2BGR)
    background_mask = cv2.cvtColor(background_mask, cv2.COLOR_GRAY2BGR)

    sprite_part = (sprite * (1 / 255.0)) * (background_mask * (1 / 255.0))
    overlay_part = (background_img * (1 / 255.0)) * (overlay_mask * (1 / 255.0))

    return np.uint8(cv2.addWeighted(sprite_part, 255.0, overlay_part, 255.0, 0.0)) 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import COLOR_GRAY2BGR [as 別名]
def get_image_and_mask(img_location, gray_flag):

    # Load image from file with alpha channel (UNCHANGED flag). If an alpha
    # channel does not exist, just return the base image.
    img = cv2.imread(img_location, cv2.IMREAD_UNCHANGED)
    if img.shape[2] <= 3:
        return img, None

    # Create an alpha channel matrix  with values between 0-255. Then
    # threshold the alpha channel to create a binary mask.
    channels = cv2.split(img)
    mask = np.array(channels[3])
    _, mask = cv2.threshold(mask, 250, 255, cv2.THRESH_BINARY)

    # Convert image and mask to grayscale or BGR based on input flag.
    if gray_flag:
        img = cv2.cvtColor(img, cv2.COLOR_BGRA2GRAY)
    else:
        img = cv2.cvtColor(img, cv2.COLOR_BGRA2BGR)
        mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)

    return img, mask


# Resize an image and mask based on an input scale ratio. 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import COLOR_GRAY2BGR [as 別名]
def evaluate_model(model, data, samples, labels):
    resp = model.predict(samples)
    print(resp)
    err = (labels != resp).mean()
    print('Accuracy: %.2f %%' % ((1 - err)*100))

    confusion = np.zeros((10, 10), np.int32)
    for i, j in zip(labels, resp):
        confusion[int(i), int(j)] += 1
    print('confusion matrix:')
    print(confusion)

    vis = []
    for img, flag in zip(data, resp == labels):
        img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
        if not flag:
            img[...,:2] = 0
        
        vis.append(img)
    return mosaic(16, vis) 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import COLOR_GRAY2BGR [as 別名]
def render(self, n_max=0, fallback_im=None):
        if self.image_scores is not None:
            im = cv2.applyColorMap((self.image_scores * 255).astype(np.uint8),
                                   cv2.COLORMAP_JET)
        else:
            assert fallback_im is not None
            im = cv2.cvtColor(fallback_im, cv2.COLOR_GRAY2BGR)

        if n_max == 0:
            n_max = self.ips_rc.shape[1]
        for i in range(n_max):
            thickness_relevant_score = \
                np.clip(self.ip_scores[i], 0.2, 0.6) - 0.2
            thickness = int(thickness_relevant_score * 20)
            if type(self.scales) == np.ndarray:
                radius = int(self.scales[i] * 10)
            else:
                radius = 10
            cv2.circle(im, tuple(self.ips_rc[[1, 0], i]),
                       radius, (0, 255, 0), thickness, cv2.LINE_AA)
        return im 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import COLOR_GRAY2BGR [as 別名]
def renderTrainSample(pair, fps, corr_rc, inl):
    ims = [cv2.cvtColor(i, cv2.COLOR_GRAY2BGR) for i in pair.im]

    rc = system.renderColors()
    for fp, corr, im in zip(fps, corr_rc, ims):
        for i in range(128):
            cv2.circle(im, tuple(fp.ips_rc[[1, 0], i]), 6, tuple(rc[i]), 1,
                       cv2.LINE_AA)
            if inl[i]:
                thck = -1
            else:
                thck = 1
            cv2.circle(im, tuple(corr[[1, 0], i]), 2, tuple(rc[i]), thck,
                       cv2.LINE_AA)

    renderings = [i.render(with_points=False) for i in fps]
    gray_ims = np.concatenate(ims, axis=0)
    rend = np.concatenate(renderings, axis=0)
    full_im = np.concatenate([gray_ims, rend], axis=1)
    outdir = os.path.join(
        'results', 'train_samples', hyperparams.methodEvalString())
    if not os.path.exists(outdir):
        os.makedirs(outdir)
    outfile = os.path.join(outdir, pair.name() + '.png')
    cv2.imwrite(outfile, full_im) 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import COLOR_GRAY2BGR [as 別名]
def concat_imgs(tuple_imgs, axis=1):
    """
    # Concat multiple images along 1 axis
    :param tuple_imgs: tuple of images
    :param axis: 0 means vertically and 1 means horizontally
    :return: Concat image
    """
    new_list_imgs = []
    for image in tuple_imgs:
        if len(image.shape) == 2:
            image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
        new_list_imgs.append(image)

    concat_im = np.concatenate(tuple(new_list_imgs), axis=axis)
    return concat_im


# ROS 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import COLOR_GRAY2BGR [as 別名]
def generate_colorbar(self, min_temp=None, max_temp=None, cmap=cv.COLORMAP_JET, height=None):
        if min_temp is None:
            min_temp = self.global_min_temp
        if max_temp is None:
            max_temp = self.global_max_temp
        cb_gray = np.arange(255,0,-1,dtype=np.uint8).reshape((255,1))
        if cmap is not None:
            cb_color = cv.applyColorMap(cb_gray, cmap)
        else:
            cb_color = cv.cvtColor(cb_gray, cv.COLOR_GRAY2BGR)
        for i in range(1,6):
            cb_color = np.concatenate( (cb_color, cb_color), axis=1 )
        
        if height is None:
            append_img = np.zeros( (self.thermal_image.shape[0], cb_color.shape[1]+30, 3), dtype=np.uint8 )
        else:
            append_img = np.zeros( (height, cb_color.shape[1]+30, 3), dtype=np.uint8 )

        append_img[append_img.shape[0]//2-cb_color.shape[0]//2  : append_img.shape[0]//2 - (cb_color.shape[0]//2) + cb_color.shape[0] , 10 : 10 + cb_color.shape[1] ] = cb_color
        cv.putText(append_img, str(min_temp), (5, append_img.shape[0]//2 - (cb_color.shape[0]//2) + cb_color.shape[0] + 30), cv.FONT_HERSHEY_PLAIN, 1, (255,0,0) , 1, 8)
        cv.putText(append_img, str(max_temp), (5, append_img.shape[0]//2-cb_color.shape[0]//2-20) , cv.FONT_HERSHEY_PLAIN, 1, (0,0,255) , 1, 8 )
        return append_img 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import COLOR_GRAY2BGR [as 別名]
def get_overlay(bg_image, fg_image, sizes=(40, 40)):
    fg_image = cv2.resize(fg_image, sizes)
    fg_mask = fg_image[:, :, 3:]
    fg_image = fg_image[:, :, :3]
    bg_mask = 255 - fg_mask
    bg_image = bg_image/255
    fg_image = fg_image/255
    fg_mask = cv2.cvtColor(fg_mask, cv2.COLOR_GRAY2BGR)/255
    bg_mask = cv2.cvtColor(bg_mask, cv2.COLOR_GRAY2BGR)/255
    image = cv2.addWeighted(bg_image*bg_mask, 255, fg_image*fg_mask, 255, 0.).astype(np.uint8)
    return image





# if __name__ == '__main__':
#     images = get_images("../images", ["apple", "star"])
#     print(images[0].shape)
#     print(np.max(images[0])) 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import COLOR_GRAY2BGR [as 別名]
def blend_non_transparent(face_img, overlay_img):
    # Let's find a mask covering all the non-black (foreground) pixels
    # NB: We need to do this on grayscale version of the image
    gray_overlay = cv2.cvtColor(overlay_img, cv2.COLOR_BGR2GRAY)
    overlay_mask = cv2.threshold(gray_overlay, 1, 255, cv2.THRESH_BINARY)[1]

    # Let's shrink and blur it a little to make the transitions smoother...
    overlay_mask = cv2.erode(overlay_mask, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)))
    overlay_mask = cv2.blur(overlay_mask, (3, 3))

    # And the inverse mask, that covers all the black (background) pixels
    background_mask = 255 - overlay_mask

    # Turn the masks into three channel, so we can use them as weights
    overlay_mask = cv2.cvtColor(overlay_mask, cv2.COLOR_GRAY2BGR)
    background_mask = cv2.cvtColor(background_mask, cv2.COLOR_GRAY2BGR)

    # Create a masked out face image, and masked out overlay
    # We convert the images to floating point in range 0.0 - 1.0
    face_part = (face_img * (1 / 255.0)) * (background_mask * (1 / 255.0))
    overlay_part = (overlay_img * (1 / 255.0)) * (overlay_mask * (1 / 255.0))

    # And finally just add them together, and rescale it back to an 8bit integer image
    return np.uint8(cv2.addWeighted(face_part, 255.0, overlay_part, 255.0, 0.0)) 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import COLOR_GRAY2BGR [as 別名]
def channel_convert(in_c, tar_type, img_list):
    # conversion among BGR, gray and y
    if in_c == 3 and tar_type == 'gray':  # BGR to gray
        gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list]
        return [np.expand_dims(img, axis=2) for img in gray_list]
    elif in_c == 3 and tar_type == 'y':  # BGR to y
        y_list = [bgr2ycbcr(img, only_y=True) for img in img_list]
        return [np.expand_dims(img, axis=2) for img in y_list]
    elif in_c == 1 and tar_type == 'RGB':  # gray/y to BGR
        return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list]
    else:
        return img_list