# Copyright 2020 Google Research. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """A set of functions that are used for visualization. These functions often receive an image, perform some visualization on the image. The functions do not return a value, instead they modify the image itself. """ import abc import collections import matplotlib import matplotlib.pyplot as plt # pylint: disable=g-import-not-at-top import numpy as np import PIL.Image as Image import PIL.ImageColor as ImageColor import PIL.ImageDraw as ImageDraw import PIL.ImageFont as ImageFont import six from six.moves import range from six.moves import zip import tensorflow.compat.v1 as tf from visualize import shape_utils from visualize import standard_fields as fields _TITLE_LEFT_MARGIN = 10 _TITLE_TOP_MARGIN = 10 STANDARD_COLORS = [ "AliceBlue", "Chartreuse", "Aqua", "Aquamarine", "Azure", "Beige", "Bisque", "BlanchedAlmond", "BlueViolet", "BurlyWood", "CadetBlue", "AntiqueWhite", "Chocolate", "Coral", "CornflowerBlue", "Cornsilk", "Crimson", "Cyan", "DarkCyan", "DarkGoldenRod", "DarkGrey", "DarkKhaki", "DarkOrange", "DarkOrchid", "DarkSalmon", "DarkSeaGreen", "DarkTurquoise", "DarkViolet", "DeepPink", "DeepSkyBlue", "DodgerBlue", "FireBrick", "FloralWhite", "ForestGreen", "Fuchsia", "Gainsboro", "GhostWhite", "Gold", "GoldenRod", "Salmon", "Tan", "HoneyDew", "HotPink", "IndianRed", "Ivory", "Khaki", "Lavender", "LavenderBlush", "LawnGreen", "LemonChiffon", "LightBlue", "LightCoral", "LightCyan", "LightGoldenRodYellow", "LightGray", "LightGrey", "LightGreen", "LightPink", "LightSalmon", "LightSeaGreen", "LightSkyBlue", "LightSlateGray", "LightSlateGrey", "LightSteelBlue", "LightYellow", "Lime", "LimeGreen", "Linen", "Magenta", "MediumAquaMarine", "MediumOrchid", "MediumPurple", "MediumSeaGreen", "MediumSlateBlue", "MediumSpringGreen", "MediumTurquoise", "MediumVioletRed", "MintCream", "MistyRose", "Moccasin", "NavajoWhite", "OldLace", "Olive", "OliveDrab", "Orange", "OrangeRed", "Orchid", "PaleGoldenRod", "PaleGreen", "PaleTurquoise", "PaleVioletRed", "PapayaWhip", "PeachPuff", "Peru", "Pink", "Plum", "PowderBlue", "Purple", "Red", "RosyBrown", "RoyalBlue", "SaddleBrown", "Green", "SandyBrown", "SeaGreen", "SeaShell", "Sienna", "Silver", "SkyBlue", "SlateBlue", "SlateGray", "SlateGrey", "Snow", "SpringGreen", "SteelBlue", "GreenYellow", "Teal", "Thistle", "Tomato", "Turquoise", "Violet", "Wheat", "White", "WhiteSmoke", "Yellow", "YellowGreen", ] def _force_matplotlib_backend(): """force the backend of matplotlib to Agg.""" if matplotlib.get_backend().lower() != "agg": # case-insensitive matplotlib.use("Agg") # Set headless-friendly backend. def _get_multiplier_for_color_randomness(): """Returns a multiplier to get semi-random colors from successive indices. This function computes a prime number, p, in the range [2, 17] that: - is closest to len(STANDARD_COLORS) / 10 - does not divide len(STANDARD_COLORS) If no prime numbers in that range satisfy the constraints, p is returned as 1. Once p is established, it can be used as a multiplier to select non-consecutive colors from STANDARD_COLORS: colors = [(p * i) % len(STANDARD_COLORS) for i in range(20)] """ num_colors = len(STANDARD_COLORS) prime_candidates = [5, 7, 11, 13, 17] # Remove all prime candidates that divide the number of colors. prime_candidates = [p for p in prime_candidates if num_colors % p] if not prime_candidates: return 1 # Return the closest prime number to num_colors / 10. abs_distance = [np.abs(num_colors / 10.0 - p) for p in prime_candidates] num_candidates = len(abs_distance) inds = [i for _, i in sorted(zip(abs_distance, range(num_candidates)))] return prime_candidates[inds[0]] def save_image_array_as_png(image, output_path): """Saves an image (represented as a numpy array) to PNG. Args: image: a numpy array with shape [height, width, 3]. output_path: path to which image should be written. """ image_pil = Image.fromarray(np.uint8(image)).convert("RGB") with tf.gfile.Open(output_path, "w") as fid: image_pil.save(fid, "PNG") def encode_image_array_as_png_str(image): """Encodes a numpy array into a PNG string. Args: image: a numpy array with shape [height, width, 3]. Returns: PNG encoded image string. """ image_pil = Image.fromarray(np.uint8(image)) output = six.BytesIO() image_pil.save(output, format="PNG") png_string = output.getvalue() output.close() return png_string def draw_bounding_box_on_image_array( image, ymin, xmin, ymax, xmax, color="red", thickness=4, display_str_list=(), use_normalized_coordinates=True ): """Adds a bounding box to an image (numpy array). Bounding box coordinates can be specified in either absolute (pixel) or normalized coordinates by setting the use_normalized_coordinates argument. Args: image: a numpy array with shape [height, width, 3]. ymin: ymin of bounding box. xmin: xmin of bounding box. ymax: ymax of bounding box. xmax: xmax of bounding box. color: color to draw bounding box. Default is red. thickness: line thickness. Default value is 4. display_str_list: list of strings to display in box (each to be shown on its own line). use_normalized_coordinates: If True (default), treat coordinates ymin, xmin, ymax, xmax as relative to the image. Otherwise treat coordinates as absolute. """ image_pil = Image.fromarray(np.uint8(image)).convert("RGB") draw_bounding_box_on_image( image_pil, ymin, xmin, ymax, xmax, color, thickness, display_str_list, use_normalized_coordinates ) np.copyto(image, np.array(image_pil)) def draw_bounding_box_on_image( image, ymin, xmin, ymax, xmax, color="red", thickness=4, display_str_list=(), use_normalized_coordinates=True ): """Adds a bounding box to an image. Bounding box coordinates can be specified in either absolute (pixel) or normalized coordinates by setting the use_normalized_coordinates argument. Each string in display_str_list is displayed on a separate line above the bounding box in black text on a rectangle filled with the input 'color'. If the top of the bounding box extends to the edge of the image, the strings are displayed below the bounding box. Args: image: a PIL.Image object. ymin: ymin of bounding box. xmin: xmin of bounding box. ymax: ymax of bounding box. xmax: xmax of bounding box. color: color to draw bounding box. Default is red. thickness: line thickness. Default value is 4. display_str_list: list of strings to display in box (each to be shown on its own line). use_normalized_coordinates: If True (default), treat coordinates ymin, xmin, ymax, xmax as relative to the image. Otherwise treat coordinates as absolute. """ draw = ImageDraw.Draw(image) im_width, im_height = image.size if use_normalized_coordinates: (left, right, top, bottom) = (xmin * im_width, xmax * im_width, ymin * im_height, ymax * im_height) else: (left, right, top, bottom) = (xmin, xmax, ymin, ymax) if thickness > 0: draw.line( [(left, top), (left, bottom), (right, bottom), (right, top), (left, top)], width=thickness, fill=color ) try: font = ImageFont.truetype("arial.ttf", 24) except IOError: font = ImageFont.load_default() # If the total height of the display strings added to the top of the bounding # box exceeds the top of the image, stack the strings below the bounding box # instead of above. display_str_heights = [font.getsize(ds)[1] for ds in display_str_list] # Each display_str has a top and bottom margin of 0.05x. total_display_str_height = (1 + 2 * 0.05) * sum(display_str_heights) if top > total_display_str_height: text_bottom = top else: text_bottom = bottom + total_display_str_height # Reverse list and print from bottom to top. for display_str in display_str_list[::-1]: text_width, text_height = font.getsize(display_str) margin = np.ceil(0.05 * text_height) draw.rectangle([(left, text_bottom - text_height - 2 * margin), (left + text_width, text_bottom)], fill=color) draw.text((left + margin, text_bottom - text_height - margin), display_str, fill="black", font=font) text_bottom -= text_height - 2 * margin def draw_bounding_boxes_on_image_array(image, boxes, color="red", thickness=4, display_str_list_list=()): """Draws bounding boxes on image (numpy array). Args: image: a numpy array object. boxes: a 2 dimensional numpy array of [N, 4]: (ymin, xmin, ymax, xmax). The coordinates are in normalized format between [0, 1]. color: color to draw bounding box. Default is red. thickness: line thickness. Default value is 4. display_str_list_list: list of list of strings. a list of strings for each bounding box. The reason to pass a list of strings for a bounding box is that it might contain multiple labels. Raises: ValueError: if boxes is not a [N, 4] array """ image_pil = Image.fromarray(image) draw_bounding_boxes_on_image(image_pil, boxes, color, thickness, display_str_list_list) np.copyto(image, np.array(image_pil)) def draw_bounding_boxes_on_image(image, boxes, color="red", thickness=4, display_str_list_list=()): """Draws bounding boxes on image. Args: image: a PIL.Image object. boxes: a 2 dimensional numpy array of [N, 4]: (ymin, xmin, ymax, xmax). The coordinates are in normalized format between [0, 1]. color: color to draw bounding box. Default is red. thickness: line thickness. Default value is 4. display_str_list_list: list of list of strings. a list of strings for each bounding box. The reason to pass a list of strings for a bounding box is that it might contain multiple labels. Raises: ValueError: if boxes is not a [N, 4] array """ boxes_shape = boxes.shape if not boxes_shape: return if len(boxes_shape) != 2 or boxes_shape[1] != 4: raise ValueError("Input must be of size [N, 4]") for i in range(boxes_shape[0]): display_str_list = () if display_str_list_list: display_str_list = display_str_list_list[i] draw_bounding_box_on_image( image, boxes[i, 0], boxes[i, 1], boxes[i, 2], boxes[i, 3], color, thickness, display_str_list ) def create_visualization_fn( category_index, include_masks=False, include_keypoints=False, include_track_ids=False, **kwargs ): """Constructs a visualization function that can be wrapped in a py_func. py_funcs only accept positional arguments. This function returns a suitable function with the correct positional argument mapping. The positional arguments in order are: 0: image 1: boxes 2: classes 3: scores [4-6]: masks (optional) [4-6]: keypoints (optional) [4-6]: track_ids (optional) -- Example 1 -- vis_only_masks_fn = create_visualization_fn(category_index, include_masks=True, include_keypoints=False, include_track_ids=False, **kwargs) image = tf.py_func(vis_only_masks_fn, inp=[image, boxes, classes, scores, masks], Tout=tf.uint8) -- Example 2 -- vis_masks_and_track_ids_fn = create_visualization_fn(category_index, include_masks=True, include_keypoints=False, include_track_ids=True, **kwargs) image = tf.py_func(vis_masks_and_track_ids_fn, inp=[image, boxes, classes, scores, masks, track_ids], Tout=tf.uint8) Args: category_index: a dict that maps integer ids to category dicts. e.g. {1: {1: 'dog'}, 2: {2: 'cat'}, ...} include_masks: Whether masks should be expected as a positional argument in the returned function. include_keypoints: Whether keypoints should be expected as a positional argument in the returned function. include_track_ids: Whether track ids should be expected as a positional argument in the returned function. **kwargs: Additional kwargs that will be passed to visualize_boxes_and_labels_on_image_array. Returns: Returns a function that only takes tensors as positional arguments. """ def visualization_py_func_fn(*args): """Visualization function that can be wrapped in a tf.py_func. Args: *args: First 4 positional arguments must be: image - uint8 numpy array with shape (img_height, img_width, 3). boxes - a numpy array of shape [N, 4]. classes - a numpy array of shape [N]. scores - a numpy array of shape [N] or None. -- Optional positional arguments -- instance_masks - a numpy array of shape [N, image_height, image_width]. keypoints - a numpy array of shape [N, num_keypoints, 2]. track_ids - a numpy array of shape [N] with unique track ids. Returns: uint8 numpy array with shape (img_height, img_width, 3) with overlaid boxes. """ image = args[0] boxes = args[1] classes = args[2] scores = args[3] masks = keypoints = track_ids = None pos_arg_ptr = 4 # Positional argument for first optional tensor (masks). if include_masks: masks = args[pos_arg_ptr] pos_arg_ptr += 1 if include_keypoints: keypoints = args[pos_arg_ptr] pos_arg_ptr += 1 if include_track_ids: track_ids = args[pos_arg_ptr] return visualize_boxes_and_labels_on_image_array( image, boxes, classes, scores, category_index=category_index, instance_masks=masks, keypoints=keypoints, track_ids=track_ids, **kwargs ) return visualization_py_func_fn def _resize_original_image(image, image_shape): image = tf.expand_dims(image, 0) image = tf.image.resize_images( image, image_shape, method=tf.image.ResizeMethod.NEAREST_NEIGHBOR, align_corners=True ) return tf.cast(tf.squeeze(image, 0), tf.uint8) def draw_bounding_boxes_on_image_tensors( images, boxes, classes, scores, category_index, original_image_spatial_shape=None, true_image_shape=None, instance_masks=None, keypoints=None, keypoint_edges=None, track_ids=None, max_boxes_to_draw=20, min_score_thresh=0.2, use_normalized_coordinates=True, ): """Draws bounding boxes, masks, and keypoints on batch of image tensors. Args: images: A 4D uint8 image tensor of shape [N, H, W, C]. If C > 3, additional channels will be ignored. If C = 1, then we convert the images to RGB images. boxes: [N, max_detections, 4] float32 tensor of detection boxes. classes: [N, max_detections] int tensor of detection classes. Note that classes are 1-indexed. scores: [N, max_detections] float32 tensor of detection scores. category_index: a dict that maps integer ids to category dicts. e.g. {1: {1: 'dog'}, 2: {2: 'cat'}, ...} original_image_spatial_shape: [N, 2] tensor containing the spatial size of the original image. true_image_shape: [N, 3] tensor containing the spatial size of unpadded original_image. instance_masks: A 4D uint8 tensor of shape [N, max_detection, H, W] with instance masks. keypoints: A 4D float32 tensor of shape [N, max_detection, num_keypoints, 2] with keypoints. keypoint_edges: A list of tuples with keypoint indices that specify which keypoints should be connected by an edge, e.g. [(0, 1), (2, 4)] draws edges from keypoint 0 to 1 and from keypoint 2 to 4. track_ids: [N, max_detections] int32 tensor of unique tracks ids (i.e. instance ids for each object). If provided, the color-coding of boxes is dictated by these ids, and not classes. max_boxes_to_draw: Maximum number of boxes to draw on an image. Default 20. min_score_thresh: Minimum score threshold for visualization. Default 0.2. use_normalized_coordinates: Whether to assume boxes and kepoints are in normalized coordinates (as opposed to absolute coordinates). Default is True. Returns: 4D image tensor of type uint8, with boxes drawn on top. """ # Additional channels are being ignored. if images.shape[3] > 3: images = images[:, :, :, 0:3] elif images.shape[3] == 1: images = tf.image.grayscale_to_rgb(images) visualization_keyword_args = { "use_normalized_coordinates": use_normalized_coordinates, "max_boxes_to_draw": max_boxes_to_draw, "min_score_thresh": min_score_thresh, "agnostic_mode": False, "line_thickness": 4, "keypoint_edges": keypoint_edges, } if true_image_shape is None: true_shapes = tf.constant(-1, shape=[images.shape.as_list()[0], 3]) else: true_shapes = true_image_shape if original_image_spatial_shape is None: original_shapes = tf.constant(-1, shape=[images.shape.as_list()[0], 2]) else: original_shapes = original_image_spatial_shape visualize_boxes_fn = create_visualization_fn( category_index, include_masks=instance_masks is not None, include_keypoints=keypoints is not None, include_track_ids=track_ids is not None, **visualization_keyword_args ) elems = [true_shapes, original_shapes, images, boxes, classes, scores] if instance_masks is not None: elems.append(instance_masks) if keypoints is not None: elems.append(keypoints) if track_ids is not None: elems.append(track_ids) def draw_boxes(image_and_detections): """Draws boxes on image.""" true_shape = image_and_detections[0] original_shape = image_and_detections[1] if true_image_shape is not None: image = shape_utils.pad_or_clip_nd(image_and_detections[2], [true_shape[0], true_shape[1], 3]) if original_image_spatial_shape is not None: image_and_detections[2] = _resize_original_image(image, original_shape) image_with_boxes = tf.py_func(visualize_boxes_fn, image_and_detections[2:], tf.uint8) return image_with_boxes images = tf.map_fn(draw_boxes, elems, dtype=tf.uint8, back_prop=False) return images def draw_side_by_side_evaluation_image( eval_dict, category_index, max_boxes_to_draw=20, min_score_thresh=0.2, use_normalized_coordinates=True, keypoint_edges=None, ): """Creates a side-by-side image with detections and groundtruth. Bounding boxes (and instance masks, if available) are visualized on both subimages. Args: eval_dict: The evaluation dictionary returned by eval_util.result_dict_for_batched_example() or eval_util.result_dict_for_single_example(). category_index: A category index (dictionary) produced from a labelmap. max_boxes_to_draw: The maximum number of boxes to draw for detections. min_score_thresh: The minimum score threshold for showing detections. use_normalized_coordinates: Whether to assume boxes and keypoints are in normalized coordinates (as opposed to absolute coordinates). Default is True. keypoint_edges: A list of tuples with keypoint indices that specify which keypoints should be connected by an edge, e.g. [(0, 1), (2, 4)] draws edges from keypoint 0 to 1 and from keypoint 2 to 4. Returns: A list of [1, H, 2 * W, C] uint8 tensor. The subimage on the left corresponds to detections, while the subimage on the right corresponds to groundtruth. """ detection_fields = fields.DetectionResultFields() input_data_fields = fields.InputDataFields() images_with_detections_list = [] # Add the batch dimension if the eval_dict is for single example. if len(eval_dict[detection_fields.detection_classes].shape) == 1: for key in eval_dict: if key != input_data_fields.original_image and key != input_data_fields.image_additional_channels: eval_dict[key] = tf.expand_dims(eval_dict[key], 0) for indx in range(eval_dict[input_data_fields.original_image].shape[0]): instance_masks = None if detection_fields.detection_masks in eval_dict: instance_masks = tf.cast( tf.expand_dims(eval_dict[detection_fields.detection_masks][indx], axis=0), tf.uint8 ) keypoints = None if detection_fields.detection_keypoints in eval_dict: keypoints = tf.expand_dims(eval_dict[detection_fields.detection_keypoints][indx], axis=0) groundtruth_instance_masks = None if input_data_fields.groundtruth_instance_masks in eval_dict: groundtruth_instance_masks = tf.cast( tf.expand_dims(eval_dict[input_data_fields.groundtruth_instance_masks][indx], axis=0), tf.uint8 ) images_with_detections = draw_bounding_boxes_on_image_tensors( tf.expand_dims(eval_dict[input_data_fields.original_image][indx], axis=0), tf.expand_dims(eval_dict[detection_fields.detection_boxes][indx], axis=0), tf.expand_dims(eval_dict[detection_fields.detection_classes][indx], axis=0), tf.expand_dims(eval_dict[detection_fields.detection_scores][indx], axis=0), category_index, original_image_spatial_shape=tf.expand_dims( eval_dict[input_data_fields.original_image_spatial_shape][indx], axis=0 ), true_image_shape=tf.expand_dims(eval_dict[input_data_fields.true_image_shape][indx], axis=0), instance_masks=instance_masks, keypoints=keypoints, keypoint_edges=keypoint_edges, max_boxes_to_draw=max_boxes_to_draw, min_score_thresh=min_score_thresh, use_normalized_coordinates=use_normalized_coordinates, ) images_with_groundtruth = draw_bounding_boxes_on_image_tensors( tf.expand_dims(eval_dict[input_data_fields.original_image][indx], axis=0), tf.expand_dims(eval_dict[input_data_fields.groundtruth_boxes][indx], axis=0), tf.expand_dims(eval_dict[input_data_fields.groundtruth_classes][indx], axis=0), tf.expand_dims( tf.ones_like(eval_dict[input_data_fields.groundtruth_classes][indx], dtype=tf.float32), axis=0 ), category_index, original_image_spatial_shape=tf.expand_dims( eval_dict[input_data_fields.original_image_spatial_shape][indx], axis=0 ), true_image_shape=tf.expand_dims(eval_dict[input_data_fields.true_image_shape][indx], axis=0), instance_masks=groundtruth_instance_masks, keypoints=None, keypoint_edges=None, max_boxes_to_draw=None, min_score_thresh=0.0, use_normalized_coordinates=use_normalized_coordinates, ) images_to_visualize = tf.concat([images_with_detections, images_with_groundtruth], axis=2) if input_data_fields.image_additional_channels in eval_dict: images_with_additional_channels_groundtruth = draw_bounding_boxes_on_image_tensors( tf.expand_dims(eval_dict[input_data_fields.image_additional_channels][indx], axis=0), tf.expand_dims(eval_dict[input_data_fields.groundtruth_boxes][indx], axis=0), tf.expand_dims(eval_dict[input_data_fields.groundtruth_classes][indx], axis=0), tf.expand_dims( tf.ones_like(eval_dict[input_data_fields.groundtruth_classes][indx], dtype=tf.float32), axis=0 ), category_index, original_image_spatial_shape=tf.expand_dims( eval_dict[input_data_fields.original_image_spatial_shape][indx], axis=0 ), true_image_shape=tf.expand_dims(eval_dict[input_data_fields.true_image_shape][indx], axis=0), instance_masks=groundtruth_instance_masks, keypoints=None, keypoint_edges=None, max_boxes_to_draw=None, min_score_thresh=0.0, use_normalized_coordinates=use_normalized_coordinates, ) images_to_visualize = tf.concat([images_to_visualize, images_with_additional_channels_groundtruth], axis=2) images_with_detections_list.append(images_to_visualize) return images_with_detections_list def draw_keypoints_on_image_array( image, keypoints, color="red", radius=2, use_normalized_coordinates=True, keypoint_edges=None, keypoint_edge_color="green", keypoint_edge_width=2, ): """Draws keypoints on an image (numpy array). Args: image: a numpy array with shape [height, width, 3]. keypoints: a numpy array with shape [num_keypoints, 2]. color: color to draw the keypoints with. Default is red. radius: keypoint radius. Default value is 2. use_normalized_coordinates: if True (default), treat keypoint values as relative to the image. Otherwise treat them as absolute. keypoint_edges: A list of tuples with keypoint indices that specify which keypoints should be connected by an edge, e.g. [(0, 1), (2, 4)] draws edges from keypoint 0 to 1 and from keypoint 2 to 4. keypoint_edge_color: color to draw the keypoint edges with. Default is red. keypoint_edge_width: width of the edges drawn between keypoints. Default value is 2. """ image_pil = Image.fromarray(np.uint8(image)).convert("RGB") draw_keypoints_on_image( image_pil, keypoints, color, radius, use_normalized_coordinates, keypoint_edges, keypoint_edge_color, keypoint_edge_width, ) np.copyto(image, np.array(image_pil)) def draw_keypoints_on_image( image, keypoints, color="red", radius=2, use_normalized_coordinates=True, keypoint_edges=None, keypoint_edge_color="green", keypoint_edge_width=2, ): """Draws keypoints on an image. Args: image: a PIL.Image object. keypoints: a numpy array with shape [num_keypoints, 2]. color: color to draw the keypoints with. Default is red. radius: keypoint radius. Default value is 2. use_normalized_coordinates: if True (default), treat keypoint values as relative to the image. Otherwise treat them as absolute. keypoint_edges: A list of tuples with keypoint indices that specify which keypoints should be connected by an edge, e.g. [(0, 1), (2, 4)] draws edges from keypoint 0 to 1 and from keypoint 2 to 4. keypoint_edge_color: color to draw the keypoint edges with. Default is red. keypoint_edge_width: width of the edges drawn between keypoints. Default value is 2. """ draw = ImageDraw.Draw(image) im_width, im_height = image.size keypoints_x = [k[1] for k in keypoints] keypoints_y = [k[0] for k in keypoints] if use_normalized_coordinates: keypoints_x = tuple([im_width * x for x in keypoints_x]) keypoints_y = tuple([im_height * y for y in keypoints_y]) for keypoint_x, keypoint_y in zip(keypoints_x, keypoints_y): draw.ellipse( [(keypoint_x - radius, keypoint_y - radius), (keypoint_x + radius, keypoint_y + radius)], outline=color, fill=color, ) if keypoint_edges is not None: for keypoint_start, keypoint_end in keypoint_edges: if ( keypoint_start < 0 or keypoint_start >= len(keypoints) or keypoint_end < 0 or keypoint_end >= len(keypoints) ): continue edge_coordinates = [ keypoints_x[keypoint_start], keypoints_y[keypoint_start], keypoints_x[keypoint_end], keypoints_y[keypoint_end], ] draw.line(edge_coordinates, fill=keypoint_edge_color, width=keypoint_edge_width) def draw_mask_on_image_array(image, mask, color="red", alpha=0.4): """Draws mask on an image. Args: image: uint8 numpy array with shape (img_height, img_height, 3) mask: a uint8 numpy array of shape (img_height, img_height) with values between either 0 or 1. color: color to draw the keypoints with. Default is red. alpha: transparency value between 0 and 1. (default: 0.4) Raises: ValueError: On incorrect data type for image or masks. """ if image.dtype != np.uint8: raise ValueError("`image` not of type np.uint8") if mask.dtype != np.uint8: raise ValueError("`mask` not of type np.uint8") if np.any(np.logical_and(mask != 1, mask != 0)): raise ValueError("`mask` elements should be in [0, 1]") if image.shape[:2] != mask.shape: raise ValueError( "The image has spatial dimensions %s but the mask has " "dimensions %s" % (image.shape[:2], mask.shape) ) rgb = ImageColor.getrgb(color) pil_image = Image.fromarray(image) solid_color = np.expand_dims(np.ones_like(mask), axis=2) * np.reshape(list(rgb), [1, 1, 3]) pil_solid_color = Image.fromarray(np.uint8(solid_color)).convert("RGBA") pil_mask = Image.fromarray(np.uint8(255.0 * alpha * mask)).convert("L") pil_image = Image.composite(pil_solid_color, pil_image, pil_mask) np.copyto(image, np.array(pil_image.convert("RGB"))) def visualize_boxes_and_labels_on_image_array( image, boxes, classes, scores, category_index, instance_masks=None, instance_boundaries=None, keypoints=None, keypoint_edges=None, track_ids=None, use_normalized_coordinates=False, max_boxes_to_draw=20, min_score_thresh=0.5, agnostic_mode=False, line_thickness=4, groundtruth_box_visualization_color="black", skip_boxes=False, skip_scores=False, skip_labels=False, skip_track_ids=False, ): """Overlay labeled boxes on an image with formatted scores and label names. This function groups boxes that correspond to the same location and creates a display string for each detection and overlays these on the image. Note that this function modifies the image in place, and returns that same image. Args: image: uint8 numpy array with shape (img_height, img_width, 3) boxes: a numpy array of shape [N, 4] classes: a numpy array of shape [N]. Note that class indices are 1-based, and match the keys in the label map. scores: a numpy array of shape [N] or None. If scores=None, then this function assumes that the boxes to be plotted are groundtruth boxes and plot all boxes as black with no classes or scores. category_index: a dict containing category dictionaries (each holding category index `id` and category name `name`) keyed by category indices. instance_masks: a numpy array of shape [N, image_height, image_width] with values ranging between 0 and 1, can be None. instance_boundaries: a numpy array of shape [N, image_height, image_width] with values ranging between 0 and 1, can be None. keypoints: a numpy array of shape [N, num_keypoints, 2], can be None keypoint_edges: A list of tuples with keypoint indices that specify which keypoints should be connected by an edge, e.g. [(0, 1), (2, 4)] draws edges from keypoint 0 to 1 and from keypoint 2 to 4. track_ids: a numpy array of shape [N] with unique track ids. If provided, color-coding of boxes will be determined by these ids, and not the class indices. use_normalized_coordinates: whether boxes is to be interpreted as normalized coordinates or not. max_boxes_to_draw: maximum number of boxes to visualize. If None, draw all boxes. min_score_thresh: minimum score threshold for a box to be visualized agnostic_mode: boolean (default: False) controlling whether to evaluate in class-agnostic mode or not. This mode will display scores but ignore classes. line_thickness: integer (default: 4) controlling line width of the boxes. groundtruth_box_visualization_color: box color for visualizing groundtruth boxes skip_boxes: whether to skip the drawing of bounding boxes. skip_scores: whether to skip score when drawing a single detection skip_labels: whether to skip label when drawing a single detection skip_track_ids: whether to skip track id when drawing a single detection Returns: uint8 numpy array with shape (img_height, img_width, 3) with overlaid boxes. """ # Create a display string (and color) for every box location, group any boxes # that correspond to the same location. box_to_display_str_map = collections.defaultdict(list) box_to_color_map = collections.defaultdict(str) box_to_instance_masks_map = {} box_to_instance_boundaries_map = {} box_to_keypoints_map = collections.defaultdict(list) box_to_track_ids_map = {} if not max_boxes_to_draw: max_boxes_to_draw = boxes.shape[0] for i in range(boxes.shape[0]): if max_boxes_to_draw == len(box_to_color_map): break if scores is None or scores[i] > min_score_thresh: box = tuple(boxes[i].tolist()) if instance_masks is not None: box_to_instance_masks_map[box] = instance_masks[i] if instance_boundaries is not None: box_to_instance_boundaries_map[box] = instance_boundaries[i] if keypoints is not None: box_to_keypoints_map[box].extend(keypoints[i]) if track_ids is not None: box_to_track_ids_map[box] = track_ids[i] if scores is None: box_to_color_map[box] = groundtruth_box_visualization_color else: display_str = "" if not skip_labels: if not agnostic_mode: if classes[i] in six.viewkeys(category_index): class_name = category_index[classes[i]]["name"] else: class_name = "N/A" display_str = str(class_name) if not skip_scores: if not display_str: display_str = "{}%".format(int(100 * scores[i])) else: display_str = "{}: {}%".format(display_str, int(100 * scores[i])) if not skip_track_ids and track_ids is not None: if not display_str: display_str = "ID {}".format(track_ids[i]) else: display_str = "{}: ID {}".format(display_str, track_ids[i]) box_to_display_str_map[box].append(display_str) if agnostic_mode: box_to_color_map[box] = "DarkOrange" elif track_ids is not None: prime_multipler = _get_multiplier_for_color_randomness() box_to_color_map[box] = STANDARD_COLORS[(prime_multipler * track_ids[i]) % len(STANDARD_COLORS)] else: box_to_color_map[box] = STANDARD_COLORS[classes[i] % len(STANDARD_COLORS)] # Draw all boxes onto image. for box, color in box_to_color_map.items(): ymin, xmin, ymax, xmax = box if instance_masks is not None: draw_mask_on_image_array(image, box_to_instance_masks_map[box], color=color) if instance_boundaries is not None: draw_mask_on_image_array(image, box_to_instance_boundaries_map[box], color="red", alpha=1.0) draw_bounding_box_on_image_array( image, ymin, xmin, ymax, xmax, color=color, thickness=0 if skip_boxes else line_thickness, display_str_list=box_to_display_str_map[box], use_normalized_coordinates=use_normalized_coordinates, ) if keypoints is not None: draw_keypoints_on_image_array( image, box_to_keypoints_map[box], color=color, radius=line_thickness / 2, use_normalized_coordinates=use_normalized_coordinates, keypoint_edges=keypoint_edges, keypoint_edge_color=color, keypoint_edge_width=line_thickness // 2, ) return image def add_cdf_image_summary(values, name): """Adds a tf.summary.image for a CDF plot of the values. Normalizes `values` such that they sum to 1, plots the cumulative distribution function and creates a tf image summary. Args: values: a 1-D float32 tensor containing the values. name: name for the image summary. """ _force_matplotlib_backend() def cdf_plot(values): """Numpy function to plot CDF.""" normalized_values = values / np.sum(values) sorted_values = np.sort(normalized_values) cumulative_values = np.cumsum(sorted_values) fraction_of_examples = np.arange(cumulative_values.size, dtype=np.float32) / cumulative_values.size fig = plt.figure(frameon=False) ax = fig.add_subplot("111") ax.plot(fraction_of_examples, cumulative_values) ax.set_ylabel("cumulative normalized values") ax.set_xlabel("fraction of examples") fig.canvas.draw() width, height = fig.get_size_inches() * fig.get_dpi() image = np.frombuffer(fig.canvas.tostring_rgb(), dtype="uint8").reshape(1, int(height), int(width), 3) return image cdf_plot = tf.py_func(cdf_plot, [values], tf.uint8) tf.summary.image(name, cdf_plot) def add_hist_image_summary(values, bins, name): """Adds a tf.summary.image for a histogram plot of the values. Plots the histogram of values and creates a tf image summary. Args: values: a 1-D float32 tensor containing the values. bins: bin edges which will be directly passed to np.histogram. name: name for the image summary. """ _force_matplotlib_backend() def hist_plot(values, bins): """Numpy function to plot hist.""" fig = plt.figure(frameon=False) ax = fig.add_subplot("111") y, x = np.histogram(values, bins=bins) ax.plot(x[:-1], y) ax.set_ylabel("count") ax.set_xlabel("value") fig.canvas.draw() width, height = fig.get_size_inches() * fig.get_dpi() image = np.frombuffer(fig.canvas.tostring_rgb(), dtype="uint8").reshape(1, int(height), int(width), 3) return image hist_plot = tf.py_func(hist_plot, [values, bins], tf.uint8) tf.summary.image(name, hist_plot) class EvalMetricOpsVisualization(six.with_metaclass(abc.ABCMeta, object)): """Abstract base class responsible for visualizations during evaluation. Currently, summary images are not run during evaluation. One way to produce evaluation images in Tensorboard is to provide tf.summary.image strings as `value_ops` in tf.estimator.EstimatorSpec's `eval_metric_ops`. This class is responsible for accruing images (with overlaid detections and groundtruth) and returning a dictionary that can be passed to `eval_metric_ops`. """ def __init__( self, category_index, max_examples_to_draw=5, max_boxes_to_draw=20, min_score_thresh=0.2, use_normalized_coordinates=True, summary_name_prefix="evaluation_image", keypoint_edges=None, ): """Creates an EvalMetricOpsVisualization. Args: category_index: A category index (dictionary) produced from a labelmap. max_examples_to_draw: The maximum number of example summaries to produce. max_boxes_to_draw: The maximum number of boxes to draw for detections. min_score_thresh: The minimum score threshold for showing detections. use_normalized_coordinates: Whether to assume boxes and keypoints are in normalized coordinates (as opposed to absolute coordinates). Default is True. summary_name_prefix: A string prefix for each image summary. keypoint_edges: A list of tuples with keypoint indices that specify which keypoints should be connected by an edge, e.g. [(0, 1), (2, 4)] draws edges from keypoint 0 to 1 and from keypoint 2 to 4. """ self._category_index = category_index self._max_examples_to_draw = max_examples_to_draw self._max_boxes_to_draw = max_boxes_to_draw self._min_score_thresh = min_score_thresh self._use_normalized_coordinates = use_normalized_coordinates self._summary_name_prefix = summary_name_prefix self._keypoint_edges = keypoint_edges self._images = [] def clear(self): self._images = [] def add_images(self, images): """Store a list of images, each with shape [1, H, W, C].""" if len(self._images) >= self._max_examples_to_draw: return # Store images and clip list if necessary. self._images.extend(images) if len(self._images) > self._max_examples_to_draw: self._images[self._max_examples_to_draw :] = [] def get_estimator_eval_metric_ops(self, eval_dict): # pyformat: disable """Returns metric ops for use in tf.estimator.EstimatorSpec. Args: eval_dict: A dictionary that holds an image, groundtruth, and detections for a batched example. Note that, we use only the first example for visualization. See eval_util.result_dict_for_batched_example() for a convenient method for constructing such a dictionary. The dictionary contains fields.InputDataFields.original_image: [batch_size, H, W, 3] image. fields.InputDataFields.original_image_spatial_shape: [batch_size, 2] tensor containing the size of the original image. fields.InputDataFields.true_image_shape: [batch_size, 3] tensor containing the spatial size of the upadded original image. fields.InputDataFields.groundtruth_boxes - [batch_size, num_boxes, 4] float32 tensor with groundtruth boxes in range [0.0, 1.0]. fields.InputDataFields.groundtruth_classes - [batch_size, num_boxes] int64 tensor with 1-indexed groundtruth classes. fields.InputDataFields.groundtruth_instance_masks - (optional) [batch_size, num_boxes, H, W] int64 tensor with instance masks. fields.DetectionResultFields.detection_boxes - [batch_size, max_num_boxes, 4] float32 tensor with detection boxes in range [0.0, 1.0]. fields.DetectionResultFields.detection_classes - [batch_size, max_num_boxes] int64 tensor with 1-indexed detection classes. fields.DetectionResultFields.detection_scores - [batch_size, max_num_boxes] float32 tensor with detection scores. fields.DetectionResultFields.detection_masks - (optional) [batch_size, max_num_boxes, H, W] float32 tensor of binarized masks. fields.DetectionResultFields.detection_keypoints - (optional) [batch_size, max_num_boxes, num_keypoints, 2] float32 tensor with keypoints. Returns: A dictionary of image summary names to tuple of (value_op, update_op). The `update_op` is the same for all items in the dictionary, and is responsible for saving a single side-by-side image with detections and groundtruth. Each `value_op` holds the tf.summary.image string for a given image. """ # pyformat: enable if self._max_examples_to_draw == 0: return {} images = self.images_from_evaluation_dict(eval_dict) def get_images(): """Returns a list of images, padded to self._max_images_to_draw.""" images = self._images while len(images) < self._max_examples_to_draw: images.append(np.array(0, dtype=np.uint8)) self.clear() return images def image_summary_or_default_string(summary_name, image): """Returns image summaries for non-padded elements.""" return tf.cond( tf.equal(tf.size(tf.shape(image)), 4), # pyformat: disable lambda: tf.summary.image(summary_name, image), lambda: tf.constant(""), ) if tf.executing_eagerly(): update_op = self.add_images([[images[0]]]) # pylint: disable=assignment-from-none image_tensors = get_images() else: update_op = tf.py_func(self.add_images, [[images[0]]], []) image_tensors = tf.py_func(get_images, [], [tf.uint8] * self._max_examples_to_draw) eval_metric_ops = {} for i, image in enumerate(image_tensors): summary_name = self._summary_name_prefix + "/" + str(i) value_op = image_summary_or_default_string(summary_name, image) eval_metric_ops[summary_name] = (value_op, update_op) return eval_metric_ops @abc.abstractmethod def images_from_evaluation_dict(self, eval_dict): """Converts evaluation dictionary into a list of image tensors. To be overridden by implementations. Args: eval_dict: A dictionary with all the necessary information for producing visualizations. Returns: A list of [1, H, W, C] uint8 tensors. """ raise NotImplementedError class VisualizeSingleFrameDetections(EvalMetricOpsVisualization): """Class responsible for single-frame object detection visualizations.""" def __init__( self, category_index, max_examples_to_draw=5, max_boxes_to_draw=20, min_score_thresh=0.2, use_normalized_coordinates=True, summary_name_prefix="Detections_Left_Groundtruth_Right", keypoint_edges=None, ): super(VisualizeSingleFrameDetections, self).__init__( category_index=category_index, max_examples_to_draw=max_examples_to_draw, max_boxes_to_draw=max_boxes_to_draw, min_score_thresh=min_score_thresh, use_normalized_coordinates=use_normalized_coordinates, summary_name_prefix=summary_name_prefix, keypoint_edges=keypoint_edges, ) def images_from_evaluation_dict(self, eval_dict): return draw_side_by_side_evaluation_image( eval_dict, self._category_index, self._max_boxes_to_draw, self._min_score_thresh, self._use_normalized_coordinates, self._keypoint_edges, )