Python Image.alpha_composite方法代碼示例

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import alpha_composite [as 別名]
def apply_colormap_on_image(org_im, activation, colormap_name):
    """
        Apply heatmap on image
    Args:
        org_img (PIL img): Original image
        activation_map (numpy arr): Activation map (grayscale) 0-255
        colormap_name (str): Name of the colormap
    """
    # Get colormap
    color_map = mpl_color_map.get_cmap(colormap_name)
    no_trans_heatmap = color_map(activation)

    # Change alpha channel in colormap to make sure original image is displayed
    heatmap = copy.copy(no_trans_heatmap)
    heatmap[:, :, 3] = 0.4
    heatmap = Image.fromarray((heatmap*255).astype(np.uint8))
    no_trans_heatmap = Image.fromarray((no_trans_heatmap*255).astype(np.uint8))

    # Apply heatmap on image
    heatmap_on_image = Image.new("RGBA", org_im.size)
    heatmap_on_image = Image.alpha_composite(heatmap_on_image, org_im.convert('RGBA'))
    heatmap_on_image = Image.alpha_composite(heatmap_on_image, heatmap)
    return no_trans_heatmap, heatmap_on_image 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import alpha_composite [as 別名]
def blend_at_intersection(self, images):
        pre_blend_images = []
        for idx, image in enumerate(images):
            x_start, y_start = self.get_start_indices(idx)
            image_data = np.asarray(image)
            overlap_data = self.overlap_map[y_start:y_start + self.image_size, x_start:x_start + self.image_size]
            alpha_image = image_data.copy()
            alpha_image[:, :, 3] = image_data[:, :, 3] / overlap_data
            pre_blend_image = np.zeros(self.dest_image_size + (4,), dtype=np.uint8)
            pre_blend_image[y_start:y_start + self.image_size, x_start:x_start + self.image_size] = alpha_image
            pre_blend_images.append(Image.fromarray(pre_blend_image, 'RGBA'))

        dest_image = pre_blend_images[0]
        for blend_image in pre_blend_images[1:]:
            dest_image = Image.alpha_composite(dest_image, blend_image)

        return dest_image 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import alpha_composite [as 別名]
def show_detections(detections):
    'Show image with drawn detections'

    for image, detections in detections.items():
        im = Image.open(image).convert('RGBA')
        overlay = Image.new('RGBA', im.size, (255, 255, 255, 0))
        draw = ImageDraw.Draw(overlay)
        detections.sort(key=lambda d: d['score'])
        for detection in detections:
            box = detection['bbox']
            alpha = int(detection['score'] * 255)
            draw.rectangle(box, outline=(255, 255, 255, alpha))
            draw.text((box[0] + 2, box[1]), '[{}]'.format(detection['class']),
                      fill=(255, 255, 255, alpha))
            draw.text((box[0] + 2, box[1] + 10), '{:.2}'.format(detection['score']),
                      fill=(255, 255, 255, alpha))
        im = Image.alpha_composite(im, overlay)
        im.show() 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import alpha_composite [as 別名]
def create_match_image(match):
	table_border = 10
	table_image = await draw_match_table(match)

	image = Image.new('RGBA', (table_image.size[0] + (table_border * 2), table_image.size[1] + table_border + 64))
	draw = ImageDraw.Draw(image)
	draw.rectangle([0, 0, image.size[0], image.size[1]], fill=discord_color2)
	draw.rectangle([0, 64, image.size[0], image.size[1]], fill=discord_color1)
	image.paste(table_image, (table_border, 64))

	title = TextCell(f"{'Radiant' if match['radiant_win'] else 'Dire'} Victory", font_size=48, color=("green" if match['radiant_win'] else "red"))
	title.render(draw, image, 64, 0, image.size[0] - 64, 64)

	team_icon = Image.open(radiant_icon if match['radiant_win'] else dire_icon).resize((64, 64))
	temp_image = Image.new("RGBA", image.size)
	temp_image.paste(team_icon, (0, 0))
	image = Image.alpha_composite(image, temp_image)

	fp = BytesIO()
	image.save(fp, format="PNG")
	fp.seek(0)

	return fp 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import alpha_composite [as 別名]
def heatmap(self, width, height, points, base_path=None, base_img=None):
        """
        :param points: sequence of tuples of (x, y), eg [(9, 20), (7, 3), (19, 12)]
        :return: If base_path of base_img provided, a heat map from the given points
                 is overlayed on the image. Otherwise, the heat map alone is returned
                 with a transparent background.
        """
        heatmap = self.grey_heatmapper.heatmap(width, height, points)
        heatmap = self._colourised(heatmap)
        heatmap = _img_to_opacity(heatmap, self.opacity)

        if base_path:
            background = Image.open(base_path)
            return Image.alpha_composite(background.convert('RGBA'), heatmap)
        elif base_img is not None:
            return Image.alpha_composite(base_img.convert('RGBA'), heatmap)
        else:
            return heatmap 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import alpha_composite [as 別名]
def remove_transparency(img: ImageType, bg_color=DEFAULT_BG_COLOR) -> ImageType:
    """Remove alpha transparency from PNG images

    Expects a PIL.Image object and returns an object of the same type with the
    changes applied.

    Special thanks to Yuji Tomita and Takahashi Shuuji
    (https://stackoverflow.com/a/33507138)
    """
    if img.mode in ('RGBA', 'LA') or (img.mode == 'P' and 'transparency' in img.info):
        orig_image = img.convert('RGBA')
        background = Image.new('RGBA', orig_image.size, bg_color)
        img = Image.alpha_composite(background, orig_image)
        return img.convert("RGB")
    else:
        return img 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import alpha_composite [as 別名]
def save_text_and_map_on_whitebg(self, map):
        # if no map or nothing changed
        if map is None or (map == self.previous_map_no_text and
                           self.previous_display_text == self.display_text):
            return
        self.map_no_text = map
        self.previous_map_no_text = self.map_no_text
        self.previous_display_text = self.display_text
        self.map_no_text.save(self.mapPath + '/' + self.roombaName +
                              'map_notext.png', "PNG")
        
        if( self.enableMapWithText ):
            final = Image.new('RGBA', self.base.size, (255,255,255,255))    # white
            # paste onto a white background, so it's easy to see
            final = Image.alpha_composite(final, map)
            final = final.rotate(self.angle, expand=True) #(NW 12/4/2018 fixed bug causing distorted maps when rotation is not 0 - moved rotate to here)
            # draw text
            self.draw_text(final, self.display_text, self.fnt)
            final.save(self.mapPath + '/'+self.roombaName + '_map.png', "PNG")
            # try to avoid other programs reading file while writing it,
            # rename should be atomic.
            os.rename(self.mapPath + '/' + self.roombaName + '_map.png',
                self.mapPath + '/' + self.roombaName + 'map.png') 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import alpha_composite [as 別名]
def save_text_and_map_on_whitebg(self, map):
        # if no map or nothing changed
        if map is None or (map == self.previous_map_no_text and
                           self.previous_display_text == self.display_text):
            return
        self.map_no_text = map
        self.previous_map_no_text = self.map_no_text
        self.previous_display_text = self.display_text
        self.map_no_text.save(self.mapPath + '/' + self.roombaName +
                              'map_notext.png', "PNG")
        final = Image.new('RGBA', self.base.size, (255,255,255,255))    # white
        # paste onto a white background, so it's easy to see
        final = Image.alpha_composite(final, map)
        # draw text
        self.draw_text(final, self.display_text, self.fnt)
        final.save(self.mapPath + '/'+self.roombaName + '_map.png', "PNG")
        # try to avoid other programs reading file while writing it,
        # rename should be atomic.
        os.rename(self.mapPath + '/' + self.roombaName + '_map.png',
            self.mapPath + '/' + self.roombaName + 'map.png') 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import alpha_composite [as 別名]
def calc_match_rate(self):
        if self.match_rate > 0:
            return self.match_rate
        self.match_rate = 0
        self.img = Image.new('RGBA', self.size)
        draw = ImageDraw.Draw(self.img)
        draw.polygon([(0, 0), (0, 255), (255, 255), (255, 0)], fill=(255, 255, 255, 255))
        for triangle in self.triangles:
            self.img = Image.alpha_composite(self.img, triangle.img_t or triangle.draw_it(self.size))
            # 與下方代碼功能相同，此版本便於理解但效率低
        # pixels = [self.img.getpixel((x, y)) for x in range(0, self.size[0], 2) for y in range(0, self.size[1], 2)]
        # for i in range(0, min(len(pixels), len(self.target_pixels))):
        #     delta_red   = pixels[i][0] - self.target_pixels[i][0]
        #     delta_green = pixels[i][1] - self.target_pixels[i][1]
        #     delta_blue  = pixels[i][2] - self.target_pixels[i][2]
        #     self.match_rate += delta_red   * delta_red   + \
        #                        delta_green * delta_green + \
        #                        delta_blue  * delta_blue
        arrs = [np.array(x) for x in list(self.img.split())]  # 分解為RGBA四通道
        for i in range(3):  # 對RGB通道三個矩陣分別與目標圖片相應通道作差取平方加和評估相似度
            self.match_rate += np.sum(np.square(arrs[i] - self.target_pixels[i]))[0] 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import alpha_composite [as 別名]
def overlay_text(img, position, text, font, align_right=False, rectangle=False):
    draw = ImageDraw.Draw(img)
    w, h = font.getsize(text)
    if align_right:
        x, y = position
        x -= w
        position = (x, y)
    if rectangle:
        x += 1
        y += 1
        position = (x, y)
        border = 1
        rect = (x - border, y, x + w, y + h + border)
        rect_img = Image.new('RGBA', (WIDTH, HEIGHT), color=(0, 0, 0, 0))
        rect_draw = ImageDraw.Draw(rect_img)
        rect_draw.rectangle(rect, (255, 255, 255))
        rect_draw.text(position, text, font=font, fill=(0, 0, 0, 0))
        img = Image.alpha_composite(img, rect_img)
    else:
        draw.text(position, text, font=font, fill=(255, 255, 255))
    return img 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import alpha_composite [as 別名]
def stitch_map(tiles, width, height, bbox, dpi):
    """
    Merge tiles together into one image.

    Args:
        tiles (list of dict of file): tiles for each layer
        width (float): page width in mm
        height (height): page height in mm
        dpi (dpi): resolution in dots per inch

    Returns:
        PIL.Image: merged map.

    """
    size = (int(width * dpi_to_dpmm(dpi)), int(height * dpi_to_dpmm(dpi)))
    background = Image.new('RGBA', size, (255, 255, 255))
    for layer in tiles:
        layer_img = Image.new("RGBA", size)
        for (x, y), tile_path in layer.items():
            tile = Image.open(tile_path)
            layer_img.paste(tile, ((x - bbox.min.x) * TILE_SIZE, (y - bbox.min.y) * TILE_SIZE))
        background = Image.alpha_composite(background, layer_img)
    add_scales_bar(background, bbox)
    return background.convert("RGB") 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import alpha_composite [as 別名]
def apply_colormap_on_image(org_im, activation, colormap_name):
    """
        Apply heatmap on image
    Args:
        org_img (PIL img): Original image
        activation_map (numpy arr): Activation map (grayscale) 0-255
        colormap_name (str): Name of the colormap
    """
    # Get colormap
    color_map = mpl_color_map.get_cmap(colormap_name)
    no_trans_heatmap = color_map(activation)
    # Change alpha channel in colormap to make sure original image is displayed
    heatmap = copy.copy(no_trans_heatmap)
    heatmap[:, :, 3] = 0.4
    heatmap = Image.fromarray((heatmap*255).astype(np.uint8))
    no_trans_heatmap = Image.fromarray((no_trans_heatmap*255).astype(np.uint8))

    # Apply heatmap on iamge
    heatmap_on_image = Image.new("RGBA", org_im.size)
    heatmap_on_image = Image.alpha_composite(heatmap_on_image, org_im.convert('RGBA'))
    heatmap_on_image = Image.alpha_composite(heatmap_on_image, heatmap)
    return no_trans_heatmap, heatmap_on_image 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import alpha_composite [as 別名]
def visualize_predictions(img, labels, font, unicode_map, fontsize=50):
    """
    This function takes in a filename of an image, and the labels in the string format given in a submission csv, and returns an image with the characters and predictions annotated.
    Copied from:
    https://www.kaggle.com/anokas/kuzushiji-visualisation
    """
    # Convert annotation string to array
    labels_split = labels.split(' ')
    if len(labels_split) < 3:
      return img
    labels = np.array(labels_split).reshape(-1, 3)

    # Read image
    imsource = Image.fromarray(img).convert('RGBA')
    bbox_canvas = Image.new('RGBA', imsource.size)
    char_canvas = Image.new('RGBA', imsource.size)
    bbox_draw = ImageDraw.Draw(bbox_canvas) # Separate canvases for boxes and chars so a box doesn't cut off a character
    char_draw = ImageDraw.Draw(char_canvas)

    for codepoint, x, y in labels:
        x, y = int(x), int(y)
        char = unicode_map[codepoint] # Convert codepoint to actual unicode character

        # Draw bounding box around character, and unicode character next to it
        bbox_draw.rectangle((x-10, y-10, x+10, y+10), fill=(255, 0, 0, 255))
        char_draw.text((x+25, y-fontsize*(3/4)), char, fill=(255, 0, 0, 255), font=font)

    imsource = Image.alpha_composite(Image.alpha_composite(imsource, bbox_canvas), char_canvas)
    imsource = imsource.convert("RGB") # Remove alpha for saving in jpg format.
    return np.asarray(imsource) 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import alpha_composite [as 別名]
def save_prediction_image(_, panoptic_pred, img_info, out_dir, colors, num_stuff):
    msk, cat, obj, iscrowd = panoptic_pred

    img = Image.open(img_info["abs_path"])

    # Prepare folders and paths
    folder, img_name = path.split(img_info["rel_path"])
    img_name, _ = path.splitext(img_name)
    out_dir = path.join(out_dir, folder)
    ensure_dir(out_dir)
    out_path = path.join(out_dir, img_name + ".jpg")

    # Render semantic
    sem = cat[msk].numpy()
    crowd = iscrowd[msk].numpy()
    sem[crowd == 1] = 255

    sem_img = Image.fromarray(colors[sem])
    sem_img = sem_img.resize(img_info["original_size"][::-1])

    # Render contours
    is_background = (sem < num_stuff) | (sem == 255)
    msk = msk.numpy()
    msk[is_background] = 0

    contours = find_boundaries(msk, mode="outer", background=0).astype(np.uint8) * 255
    contours = dilation(contours)

    contours = np.expand_dims(contours, -1).repeat(4, -1)
    contours_img = Image.fromarray(contours, mode="RGBA")
    contours_img = contours_img.resize(img_info["original_size"][::-1])

    # Compose final image and save
    out = Image.blend(img, sem_img, 0.5).convert(mode="RGBA")
    out = Image.alpha_composite(out, contours_img)
    out.convert(mode="RGB").save(out_path) 

# 需要導入模塊: from PIL import Image [as 別名]
# 或者: from PIL.Image import alpha_composite [as 別名]
def create_sample(self):

        found_bboxes = []
        images = []
        dominating_colour = None
        for _ in range(self.numbers_per_image):
            house_number_crop, bbox, median_colour = self.get_number_crop()
            if len(images) > 0:
                while True:
                    if is_close(median_colour, dominating_colour):
                        break
                    house_number_crop, bbox, median_colour = self.get_number_crop()
            else:
                dominating_colour = median_colour

            house_number_crop, bbox = self.adjust_house_number_crop(house_number_crop, bbox)
            paste_bbox = self.find_paste_location(bbox, found_bboxes)
            found_bboxes.append(paste_bbox)
            images.append(house_number_crop)

        base_image = self.create_base_image(tuple(dominating_colour))

        for image, bbox in zip(images, found_bboxes):
            base_image_data = np.asarray(base_image, dtype=np.uint8).copy()
            image_array = np.asarray(image, dtype=np.uint8)

            image_holder = np.zeros_like(base_image_data, dtype=np.uint8)
            image_holder[bbox.top:bbox.top + bbox.height, bbox.left:bbox.left + bbox.width, :] = image_array[:]
            image = Image.fromarray(image_holder, mode='RGBA')

            base_image_data[bbox.top:bbox.top + bbox.height, bbox.left:bbox.left + bbox.width, 3] = 255 - image_array[..., 3]
            base_image = Image.fromarray(base_image_data, mode='RGBA')
            base_image = Image.alpha_composite(base_image, image)

        return base_image, found_bboxes