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""" |
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Mask R-CNN |
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Configurations and data loading code for the synthetic Shapes dataset. |
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This is a duplicate of the code in the noteobook train_shapes.ipynb for easy |
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import into other notebooks, such as inspect_model.ipynb. |
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Copyright (c) 2017 Matterport, Inc. |
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Licensed under the MIT License (see LICENSE for details) |
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Written by Waleed Abdulla |
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""" |
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import math |
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import random |
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import numpy as np |
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import cv2 |
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from config import Config |
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import utils |
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class ShapesConfig(Config): |
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"""Configuration for training on the toy shapes dataset. |
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Derives from the base Config class and overrides values specific |
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to the toy shapes dataset. |
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""" |
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NAME = "shapes" |
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GPU_COUNT = 1 |
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IMAGES_PER_GPU = 8 |
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NUM_CLASSES = 1 + 3 |
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IMAGE_MIN_DIM = 128 |
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IMAGE_MAX_DIM = 128 |
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RPN_ANCHOR_SCALES = (8, 16, 32, 64, 128) |
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TRAIN_ROIS_PER_IMAGE = 32 |
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STEPS_PER_EPOCH = 100 |
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VALIDATION_STEPS = 5 |
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class ShapesDataset(utils.Dataset): |
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"""Generates the shapes synthetic dataset. The dataset consists of simple |
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shapes (triangles, squares, circles) placed randomly on a blank surface. |
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The images are generated on the fly. No file access required. |
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""" |
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def load_shapes(self, count, height, width): |
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"""Generate the requested number of synthetic images. |
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count: number of images to generate. |
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height, width: the size of the generated images. |
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""" |
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self.add_class("shapes", 1, "square") |
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self.add_class("shapes", 2, "circle") |
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self.add_class("shapes", 3, "triangle") |
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for i in range(count): |
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bg_color, shapes = self.random_image(height, width) |
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self.add_image("shapes", image_id=i, path=None, |
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width=width, height=height, |
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bg_color=bg_color, shapes=shapes) |
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def load_image(self, image_id): |
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"""Generate an image from the specs of the given image ID. |
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Typically this function loads the image from a file, but |
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in this case it generates the image on the fly from the |
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specs in image_info. |
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""" |
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info = self.image_info[image_id] |
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bg_color = np.array(info['bg_color']).reshape([1, 1, 3]) |
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image = np.ones([info['height'], info['width'], 3], dtype=np.uint8) |
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image = image * bg_color.astype(np.uint8) |
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for shape, color, dims in info['shapes']: |
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image = self.draw_shape(image, shape, dims, color) |
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return image |
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def image_reference(self, image_id): |
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"""Return the shapes data of the image.""" |
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info = self.image_info[image_id] |
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if info["source"] == "shapes": |
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return info["shapes"] |
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else: |
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super(self.__class__).image_reference(self, image_id) |
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def load_mask(self, image_id): |
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"""Generate instance masks for shapes of the given image ID. |
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""" |
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info = self.image_info[image_id] |
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shapes = info['shapes'] |
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count = len(shapes) |
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mask = np.zeros([info['height'], info['width'], count], dtype=np.uint8) |
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for i, (shape, _, dims) in enumerate(info['shapes']): |
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mask[:, :, i:i + 1] = self.draw_shape(mask[:, :, i:i + 1].copy(), |
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shape, dims, 1) |
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occlusion = np.logical_not(mask[:, :, -1]).astype(np.uint8) |
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for i in range(count - 2, -1, -1): |
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mask[:, :, i] = mask[:, :, i] * occlusion |
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occlusion = np.logical_and( |
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occlusion, np.logical_not(mask[:, :, i])) |
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class_ids = np.array([self.class_names.index(s[0]) for s in shapes]) |
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return mask, class_ids.astype(np.int32) |
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def draw_shape(self, image, shape, dims, color): |
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"""Draws a shape from the given specs.""" |
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x, y, s = dims |
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if shape == 'square': |
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image = cv2.rectangle(image, (x - s, y - s), |
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(x + s, y + s), color, -1) |
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elif shape == "circle": |
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image = cv2.circle(image, (x, y), s, color, -1) |
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elif shape == "triangle": |
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points = np.array([[(x, y - s), |
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(x - s / math.sin(math.radians(60)), y + s), |
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(x + s / math.sin(math.radians(60)), y + s), |
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]], dtype=np.int32) |
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image = cv2.fillPoly(image, points, color) |
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return image |
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def random_shape(self, height, width): |
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"""Generates specifications of a random shape that lies within |
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the given height and width boundaries. |
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Returns a tuple of three valus: |
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* The shape name (square, circle, ...) |
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* Shape color: a tuple of 3 values, RGB. |
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* Shape dimensions: A tuple of values that define the shape size |
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and location. Differs per shape type. |
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""" |
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shape = random.choice(["square", "circle", "triangle"]) |
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color = tuple([random.randint(0, 255) for _ in range(3)]) |
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buffer = 20 |
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y = random.randint(buffer, height - buffer - 1) |
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x = random.randint(buffer, width - buffer - 1) |
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s = random.randint(buffer, height // 4) |
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return shape, color, (x, y, s) |
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def random_image(self, height, width): |
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"""Creates random specifications of an image with multiple shapes. |
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Returns the background color of the image and a list of shape |
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specifications that can be used to draw the image. |
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""" |
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bg_color = np.array([random.randint(0, 255) for _ in range(3)]) |
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shapes = [] |
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boxes = [] |
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N = random.randint(1, 4) |
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for _ in range(N): |
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shape, color, dims = self.random_shape(height, width) |
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shapes.append((shape, color, dims)) |
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x, y, s = dims |
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boxes.append([y - s, x - s, y + s, x + s]) |
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keep_ixs = utils.non_max_suppression( |
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np.array(boxes), np.arange(N), 0.3) |
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shapes = [s for i, s in enumerate(shapes) if i in keep_ixs] |
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return bg_color, shapes |
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