From 2292d6ff94a17c4e158c168de23cff0f62603dc1 Mon Sep 17 00:00:00 2001
From: SpeedProg <speedprog@googlemail.com>
Date: Thu, 02 Jan 2020 15:56:20 +0000
Subject: [PATCH] update stuff
---
transform_data.py | 579 ++++++++++++++++++++++++++++++++++++++++++++-------------
1 files changed, 440 insertions(+), 139 deletions(-)
diff --git a/transform_data.py b/transform_data.py
index 88d693e..f4fa1a9 100644
--- a/transform_data.py
+++ b/transform_data.py
@@ -1,134 +1,386 @@
-import random
-import math
+import argparse
import cv2
-import numpy as np
+import imgaug as ia
+from imgaug import augmenters as iaa
+from imgaug import parameters as iap
import imutils
+import math
+import numpy as np
+import os
import pandas as pd
-import fetch_data
+import random
+from shapely import geometry
-card_mask = cv2.imread('data/mask.png')
+import fetch_data
+import generate_data
+from config import Config
+
+
+def key_pts_to_yolo(key_pts, w_img, h_img):
+ """
+ Convert a list of keypoints into a yolo training format
+ :param key_pts: list of keypoints
+ :param w_img: width of the entire image
+ :param h_img: height of the entire image
+ :return: <x> <y> <width> <height>
+ """
+ x1 = max(0, min([pt[0] for pt in key_pts]))
+ x2 = min(w_img, max([pt[0] for pt in key_pts]))
+ y1 = max(0, min([pt[1] for pt in key_pts]))
+ y2 = min(h_img, max([pt[1] for pt in key_pts]))
+ x = (x2 + x1) / 2 / w_img
+ y = (y2 + y1) / 2 / h_img
+ width = (x2 - x1) / w_img
+ height = (y2 - y1) / h_img
+ return x, y, width, height
class ImageGenerator:
"""
- A template for generating a training image.
+ A template for generating a training image
+ An ImageGenerator contains a background image, list of cards, and other environmental parameters to
+ set up a training image for YOLO network
"""
- def __init__(self, img_bg, cards, width, height):
+ def __init__(self, img_bg, class_ids, width, height, skew=None, cards=None):
"""
:param img_bg: background (textile) image
- :param cards: list of Card objects
:param width: width of the training image
:param height: height of the training image
+ :param skew: 4 coordinates that indicates the corners (in normalized form) for perspective transform
+ :param cards: list of Card objects
"""
self.img_bg = img_bg
- self.cards = cards
+ self.class_ids = class_ids
self.img_result = None
self.width = width
self.height = height
+ if cards is None:
+ self.cards = []
+ else:
+ self.cards = cards
+
+ # Compute transform matrix for perspective transform (used for skewing the final result)
+ if skew is not None:
+ orig_corner = np.array([[0, 0], [0, height], [width, height], [width, 0]], dtype=np.float32)
+ new_corner = np.array([[width * s[0], height * s[1]] for s in skew], dtype=np.float32)
+ self.M = cv2.getPerspectiveTransform(orig_corner, new_corner)
+ pass
+ else:
+ self.M = None
pass
- def display(self):
+ def add_card(self, card, x=None, y=None, theta=0.0, scale=1.0):
"""
- Display the current state of the generator
+ Add a card to this generator scenario.
+ :param card: card to be added
+ :param x: new X-coordinate for the centre of the card
+ :param y: new Y-coordinate for the centre of the card
+ :param theta: new angle for the card
+ :param scale: new scale for the card
:return: none
"""
- img_bg = cv2.resize(self.img_bg, (self.width, self.height))
+ # If the position isn't given, push it out of the image so that it won't be visible during rendering
+ if x is None:
+ x = -len(card.img[0]) / 2
+ if y is None:
+ y = -len(card.img) / 2
+ self.cards.append(card)
+ card.x = x
+ card.y = y
+ card.theta = theta
+ card.scale = scale
+ pass
+
+ def render(self, visibility=0.5, aug=None, display=False, debug=False):
+ """
+ Display the current state of the generator.
+ :param visibility: portion of the card's image that must not be overlapped by other cards for the card to be
+ considered as visible
+ :param aug: image augmentator to apply during rendering
+ :param display: flag for displaying the rendering result
+ :param debug: flag for debug
+ :return: none
+ """
+ self.check_visibility(visibility=visibility)
+ img_result = np.zeros((self.height, self.width, 3), dtype=np.uint8)
+ card_mask = cv2.imread(Config.card_mask_path)
for card in self.cards:
- print(card.x, card.y, card.theta, card.scale)
+ card_x = int(card.x + 0.5)
+ card_y = int(card.y + 0.5)
+
# Scale & rotate card image
img_card = cv2.resize(card.img, (int(len(card.img[0]) * card.scale), int(len(card.img) * card.scale)))
+ # Add a random glaring on individual card - it happens frequently in real life as MTG cards can reflect
+ # the lights very well.
+ if aug is not None:
+ seq = iaa.Sequential([
+ iaa.SimplexNoiseAlpha(first=iaa.Add(random.randrange(128)), size_px_max=[1, 3],
+ upscale_method="cubic"), # Lighting
+ ])
+ img_card = seq.augment_image(img_card)
mask_scale = cv2.resize(card_mask, (int(len(card_mask[0]) * card.scale), int(len(card_mask) * card.scale)))
img_mask = cv2.bitwise_and(img_card, mask_scale)
img_rotate = imutils.rotate_bound(img_mask, card.theta / math.pi * 180)
- print(len(img_rotate[0]), len(img_rotate))
-
+
# Calculate the position of the card image in relation to the background
# Crop the card image if it's out of boundary
card_w = len(img_rotate[0])
card_h = len(img_rotate)
- #card_crop_x1 = min(0, card_w // 2 - card.x)
- #card_crop_x2 = min(card_w, card_w // 2 + len(img_bg[0]) - card.x)
- #card_crop_y1 = min(0, card_h // 2 - card.y)
- #card_crop_y2 = min(card_h, card_h // 2 + len(img_bg) - card.y)
- card_crop_x1 = card_w // 2 - card.x
- card_crop_x2 = card_w // 2 + len(img_bg[0]) - card.x
- card_crop_y1 = card_h // 2 - card.y
- card_crop_y2 = card_h // 2 + len(img_bg) - card.y
-
- img_card_crop = img_rotate[max(0, card_crop_y1):min(card_h, card_crop_y2),
- max(0, card_crop_x1):min(card_w, card_crop_x2)]
- #print(card_crop_x1, card_crop_x2, card_crop_y1, card_crop_y2)
- print(len(img_card_crop[0]), len(img_card_crop))
+ card_crop_x1 = max(0, card_w // 2 - card_x)
+ card_crop_x2 = min(card_w, card_w // 2 + len(img_result[0]) - card_x)
+ card_crop_y1 = max(0, card_h // 2 - card_y)
+ card_crop_y2 = min(card_h, card_h // 2 + len(img_result) - card_y)
+ img_card_crop = img_rotate[card_crop_y1:card_crop_y2, card_crop_x1:card_crop_x2]
# Calculate the position of the corresponding area in the background
- bg_crop_x1 = max(0, card.x - (card_w // 2))
- bg_crop_x2 = min(len(img_bg[0]), int(card.x + (card_w / 2) + 0.5))
- bg_crop_y1 = max(0, card.y - (card_h // 2))
- bg_crop_y2 = min(len(img_bg), int(card.y + (card_h / 2) + 0.5))
- img_bg_crop = img_bg[bg_crop_y1:bg_crop_y2, bg_crop_x1:bg_crop_x2]
- #bg_crop_x1 = card.x - (card_w // 2)
- #bg_crop_x2 = int(card.x + card_w / 2 + 0.5)
- #bg_crop_y1 = card.y - (card_h // 2)
- #bg_crop_y2 = int(card.y + card_h / 2 + 0.5)
- #img_bg_crop = img_bg[max(0, bg_crop_y1):min(len(img_bg), bg_crop_y2),
- # max(0, bg_crop_x1):min(len(img_bg[0]), bg_crop_x2)]
- #print(bg_crop_x1, bg_crop_x2, bg_crop_y1, bg_crop_y2)
- print(len(img_bg_crop[0]), len(img_bg_crop))
+ bg_crop_x1 = max(0, card_x - (card_w // 2))
+ bg_crop_x2 = min(len(img_result[0]), int(card_x + (card_w / 2) + 0.5))
+ bg_crop_y1 = max(0, card_y - (card_h // 2))
+ bg_crop_y2 = min(len(img_result), int(card_y + (card_h / 2) + 0.5))
+ img_result_crop = img_result[bg_crop_y1:bg_crop_y2, bg_crop_x1:bg_crop_x2]
- #cv2.circle(img_bg, (card.x, card.y), 2, (0, 0, 255), 3)
- #cv2.rectangle(img_bg, (0, 0), (len(img_rotate[0]), len(img_rotate)), (0, 255, 0), 3)
- #cv2.rectangle(img_bg, (bg_crop_x1, bg_crop_y1), (bg_crop_x2, bg_crop_y2), (255, 0, 0), 3)
- #cv2.imshow('test', img_bg)
- #cv2.waitKey(0)
+ # Override the background with the current card
+ img_result_crop = np.where(img_card_crop, img_card_crop, img_result_crop)
+ img_result[bg_crop_y1:bg_crop_y2, bg_crop_x1:bg_crop_x2] = img_result_crop
+
+ if debug:
+ for ext_obj in card.objects:
+ if ext_obj.visible:
+ for pt in ext_obj.key_pts:
+ cv2.circle(img_result, card.coordinate_in_generator(pt[0], pt[1]), 2, (1, 1, 255), 10)
+ bounding_box = card.bb_in_generator(ext_obj.key_pts)
+ cv2.rectangle(img_result, bounding_box[0], bounding_box[2], (1, 255, 1), 5)
- mask1 = img_card_crop[:, :, 1]
- a_mask1 = np.stack([mask1] * 3, -1)
- img_bg_crop = np.where(a_mask1, img_card_crop[:, :, 0:3], img_bg_crop)
- img_bg[bg_crop_y1:bg_crop_y2, bg_crop_x1:bg_crop_x2] = img_bg_crop
- cv2.imshow('Result', img_bg)
- cv2.waitKey(0)
+ img_result = cv2.GaussianBlur(img_result, (5, 5), 0)
+
+ # Skew the cards if it's provided
+ if self.M is not None:
+ img_result = cv2.warpPerspective(img_result, self.M, (self.width, self.height))
+ if debug:
+ for card in self.cards:
+ for ext_obj in card.objects:
+ if ext_obj.visible:
+ new_pts = np.array([[list(card.coordinate_in_generator(pt[0], pt[1]))]
+ for pt in ext_obj.key_pts], dtype=np.float32)
+ new_pts = cv2.perspectiveTransform(new_pts, self.M)
+ for pt in new_pts:
+ cv2.circle(img_result, (pt[0][0], pt[0][1]), 2, (255, 1, 1), 10)
+
+ img_bg = cv2.resize(self.img_bg, (self.width, self.height))
+ img_result = np.where(img_result, img_result, img_bg)
+
+ # Apply image augmentation
+ if aug is not None:
+ img_result = aug.augment_image(img_result)
+
+ if display or debug:
+ cv2.imshow('Result', img_result)
+ cv2.waitKey(0)
+
+ self.img_result = img_result
pass
- def generate_horizontal_span(self):
+ def generate_horizontal_span(self, gap=None, scale=None, theta=0, shift=None, jitter=None):
"""
Generating the first scenario where the cards are laid out in a straight horizontal line
- :return: none
+ :param gap: horizontal offset between each adjacent cards
+ :param scale: scale of each cards in the generator
+ :param theta: rotation of the entire span in radian
+ :param shift: range of arbitrary offset for each card
+ :param jitter: range of in-place rotation for each card in radian
+ :return: True if successfully generated, otherwise False
"""
- pass
+ # Set scale of the cards, variance of shift & jitter to be applied if they're not given
+ card_size = (len(self.cards[0].img[0]), len(self.cards[0].img))
+ if scale is None:
+ # Scale the cards so that card takes about 50% of the image's height
+ coverage_ratio = 0.5
+ scale = self.height * coverage_ratio / card_size[1]
+ if shift is None:
+ # Plus minus 5% of the card's height
+ shift = [-card_size[1] * scale * 0.05, card_size[1] * scale * 0.05]
+ pass
+ if jitter is None:
+ # Plus minus 10 degrees
+ jitter = [-math.pi / 18, math.pi / 18]
+ if gap is None:
+ # 25% of the card's width - set symbol and 1-2 mana symbols will be visible on each card
+ gap = card_size[0] * scale * 0.4
- def generate_vertical_span(self):
+ # Determine the location of the first card
+ # The cards will cover (width of a card + (# of cards - 1) * gap) pixels wide and (height of a card) pixels high
+ x_anchor = int(self.width / 2 + (len(self.cards) - 1) * gap / 2)
+ y_anchor = self.height // 2
+ for card in self.cards:
+ card.scale = scale
+ card.x = x_anchor
+ card.y = y_anchor
+ card.theta = 0
+ card.shift(shift, shift)
+ card.rotate(jitter)
+ card.rotate(theta, centre=(self.width // 2 - x_anchor, self.height // 2 - y_anchor))
+ x_anchor -= gap
+
+ return True
+
+ def generate_vertical_span(self, gap=None, scale=None, theta=0, shift=None, jitter=None):
"""
Generating the second scenario where the cards are laid out in a straight vertical line
- :return: none
+ :param gap: horizontal offset between each adjacent cards
+ :param scale: scale of each cards in the generator
+ :param theta: rotation of the entire span in radian
+ :param shift: range of arbitrary offset for each card
+ :param jitter: range of in-place rotation for each card in radian
+ :return: True if successfully generated, otherwise False
+ :return: True if successfully generated, otherwise False
"""
- pass
+ # Set scale of the cards, variance of shift & jitter to be applied if they're not given
+ card_size = (len(self.cards[0].img[0]), len(self.cards[0].img))
+ if scale is None:
+ # Scale the cards so that card takes about 50% of the image's height
+ coverage_ratio = 0.5
+ scale = self.height * coverage_ratio / card_size[1]
+ if shift is None:
+ # Plus minus 5% of the card's height
+ shift = [-card_size[1] * scale * 0.05, card_size[1] * scale * 0.05]
+ pass
+ if jitter is None:
+ # Plus minus 5 degrees
+ jitter = [-math.pi / 36, math.pi / 36]
+ if gap is None:
+ # 15% of the card's height - the title bar (with mana symbols) will be visible
+ gap = card_size[1] * scale * 0.25
- def generate_fan_out(self):
+ # Determine the location of the first card
+ # The cards will cover (width of a card) pixels wide and (height of a card + (# of cards - 1) * gap) pixels high
+ x_anchor = self.width // 2
+ y_anchor = int(self.height / 2 - (len(self.cards) - 1) * gap / 2)
+ for card in self.cards:
+ card.scale = scale
+ card.x = x_anchor
+ card.y = y_anchor
+ card.theta = 0
+ card.shift(shift, shift)
+ card.rotate(jitter)
+ card.rotate(theta, centre=(self.width // 2 - x_anchor, self.height // 2 - y_anchor))
+ y_anchor += gap
+ return True
+
+ def generate_fan_out(self, centre, theta_between_cards=None, scale=None, shift=None, jitter=None):
"""
Generating the third scenario where the cards are laid out in a fan shape
+ :return: True if successfully generated, otherwise False
+ """
+ # TODO
+ return False
+
+ def generate_non_obstructive(self, tolerance=0.90, scale=None):
+ """
+ Generating the fourth scenario where the cards are laid in arbitrary position that doesn't obstruct other cards
+ :param tolerance: minimum level of visibility for each cards
+ :param scale: scale of each cards in generator
+ :return: True if successfully generated, otherwise False
+ """
+ card_size = (len(self.cards[0].img[0]), len(self.cards[0].img))
+ if scale is None:
+ # Total area of the cards should cover about 25-40% of the entire image, depending on the number of cards
+ scale = math.sqrt(self.width * self.height * min(0.25 + 0.02 * len(self.cards), 0.4)
+ / (card_size[0] * card_size[1] * len(self.cards)))
+ # Position each card at random location that doesn't obstruct other cards
+ i = 0
+ while i < len(self.cards):
+ card = self.cards[i]
+ card.scale = scale
+ rep = 0
+ while True:
+ card.x = random.uniform(card_size[1] * scale / 2, self.width - card_size[1] * scale)
+ card.y = random.uniform(card_size[1] * scale / 2, self.height - card_size[1] * scale)
+ card.theta = random.uniform(-math.pi, math.pi)
+ self.check_visibility(self.cards[:i + 1], visibility=tolerance)
+ # This position is not obstructive if all of the cards are visible
+ is_visible = [other_card.objects[0].visible for other_card in self.cards[:i + 1]]
+ non_obstructive = all(is_visible)
+ if non_obstructive:
+ i += 1
+ break
+ rep += 1
+ if rep >= 1000:
+ # Reassign previous card's position
+ i -= 1
+ break
+ return True
+
+ def check_visibility(self, cards=None, i_check=None, visibility=0.5):
+ """
+ Check whether if extracted objects in a card is visible in the current scenario, and update their status
+ :param cards: list of cards (in a correct Z-order). All cards in this Generator are checked by default.
+ :param i_check: indices of cards that needs to be checked. Cards that aren't in this list will only be used
+ to check visibility of other cards. All cards are checked by default.
+ :param visibility: minimum ratio of the object's area that aren't covered by another card to be visible
:return: none
"""
- pass
+ if cards is None:
+ cards = self.cards
+ if i_check is None:
+ i_check = range(len(cards))
- def export_training_data(self, out_dir):
+ # Create a polygon of each card
+ card_poly_list = [geometry.Polygon([card.coordinate_in_generator(0, 0),
+ card.coordinate_in_generator(0, len(card.img)),
+ card.coordinate_in_generator(len(card.img[0]), len(card.img)),
+ card.coordinate_in_generator(len(card.img[0]), 0)]) for card in self.cards]
+ template_poly = geometry.Polygon([(0, 0), (self.width, 0), (self.width, self.height), (0, self.height)])
+
+ # First card in the list is overlaid on the bottom of the card pile
+ for i in i_check:
+ card = cards[i]
+ for ext_obj in card.objects:
+ obj_poly = geometry.Polygon([card.coordinate_in_generator(pt[0], pt[1]) for pt in ext_obj.key_pts])
+ obj_area = obj_poly.area
+ # Check if the other cards are blocking this object or if it's out of the template
+ # If there are other polygons with higher indices in the list, that card is overlapping this object
+ # We assume that no objects from the same card is on top of each other
+ for card_poly in card_poly_list[i + 1:]:
+ obj_poly = obj_poly.difference(card_poly)
+ obj_poly = obj_poly.intersection(template_poly)
+ visible_area = obj_poly.area
+ ext_obj.visible = obj_area * visibility <= visible_area
+
+ def export_training_data(self, out_name, visibility=0.5, aug=None):
"""
Export the generated training image along with the txt file for all bounding boxes
+ :param out_name: path of the output file (without extension)
+ :param visibility: portion of the card's image that must not be overlapped by other cards for the card to be
+ considered as visible
+ :param aug: image augmentator to be applied
:return: none
"""
- pass
+ self.render(visibility, aug=aug)
+ cv2.imwrite(out_name + '.jpg', self.img_result)
+ out_txt = open(out_name + '.txt', 'w')
+ for card in self.cards:
+ for ext_obj in card.objects:
+ if not ext_obj.visible:
+ continue
+ coords_in_gen = [card.coordinate_in_generator(key_pt[0], key_pt[1]) for key_pt in ext_obj.key_pts]
+ obj_yolo_info = key_pts_to_yolo(coords_in_gen, self.width, self.height)
+ if ext_obj.label == 'card':
+ #class_id = self.class_ids[card.info['name']]
+ class_id = 0 # since only the entire card is used
+ out_txt.write(str(class_id) + ' %.6f %.6f %.6f %.6f\n' % obj_yolo_info)
+ out_txt.close()
class Card:
"""
A class for storing required information about a card in relation to the ImageGenerator
"""
- def __init__(self, img, card_info, objects, generator=None, x=None, y=None, theta=None, scale=None):
+ def __init__(self, img, card_info, objects, x=None, y=None, theta=None, scale=None):
"""
:param img: image of the card
:param card_info: details like name, mana cost, type, set, etc
:param objects: list of ExtractedObjects like mana & set symbol, etc
- :param generator: ImageGenerator object that the card is bound to
:param x: X-coordinate of the card's centre in relation to the generator
:param y: Y-coordinate of the card's centre in relation to the generator
:param theta: angle of rotation of the card in relation to the generator
@@ -137,36 +389,17 @@
self.img = img
self.info = card_info
self.objects = objects
- self.generator = generator
self.x = x
self.y = y
self.theta = theta
self.scale = scale
pass
- def bind_to_generator(self, generator, x=0, y=0, theta=0.0, scale=1.0):
- """
- Bind this card to an ImageGenerator object.
- :param generator: generator to be bound with
- :param x: new X-coordinate for the centre of the card
- :param y: new Y-coordinate for the centre of the card
- :param theta: new angle for the card
- :param scale: new scale for the card
- :return: none
- """
- self.generator = generator
- self.x = x
- self.y = y
- self.theta = theta
- self.scale = scale
- generator.cards.append(self)
- pass
-
def shift(self, x, y):
"""
Apply a X/Y translation on this image
:param x: amount of X-translation. If range is given, translate by a random amount within that range
- :param y: amount of Y-translation. Refer to x when a range is given.
+ :param y: amount of Y-translation. If range is given, translate by a random amount within that range
:return: none
"""
if isinstance(x, tuple) or (isinstance(x, list) and len(x) == 2):
@@ -179,33 +412,73 @@
self.y += y
pass
- def rotate(self, centre, theta=None):
+ def rotate(self, theta, centre=(0, 0)):
"""
Apply a rotation on this image with a centre
- :param centre: coordinate of the centre of the rotation in relation to the centre of this card
:param theta: amount of rotation in radian (clockwise). If a range is given, rotate by a random amount within
- that range
+ that range
+ :param centre: coordinate of the centre of the rotation in relation to the centre of this card
:return: none
"""
if isinstance(theta, tuple) or (isinstance(theta, list) and len(theta) == 2):
theta = random.uniform(theta[0], theta[1])
- x = self.x.copy()
- y = self.y.copy()
- # Translate the coordinate to make the centre of rotation be at (0, 0)
- x -= centre[0]
- y -= centre[1]
+ # If the centre given is the centre of this card, the whole math simplifies a bit
+ # (This still works without the if statement, but let's not do useless trigs if we know the answer already)
+ if centre is not (0, 0):
+ # Rotation math
+ self.x -= -centre[1] * math.sin(theta) + centre[0] * math.cos(theta)
+ self.y -= centre[1] * math.cos(theta) + centre[0] * math.sin(theta)
- # Do the rotation math
- x_rotate = y * math.sin(theta) + x * math.cos(theta)
- y_rotate = y * math.cos(theta) - x * math.sin(theta)
+ # Offset for the coordinate translation
+ self.x += centre[0]
+ self.y += centre[1]
- # Negate the initial offset
- self.x = x_rotate + centre[0]
- self.y = y_rotate + centre[1]
self.theta += theta
pass
+ def coordinate_in_generator(self, x, y):
+ """
+ Converting coordinate within the card into the coordinate in the generator it is associated with
+ :param x: x coordinate within the card
+ :param y: y coordinate within the card
+ :return: (x, y) coordinate in the generator
+ """
+ # Relative distance in X & Y axis, if the centre of the card is at the origin (0, 0)
+ rel_x = x - len(self.img[0]) // 2
+ rel_y = y - len(self.img) // 2
+
+ # Scaling
+ rel_x *= self.scale
+ rel_y *= self.scale
+
+ # Rotation
+ rot_x = rel_x - rel_y * math.sin(self.theta) + rel_x * math.cos(self.theta)
+ rot_y = rel_y + rel_y * math.cos(self.theta) + rel_x * math.sin(self.theta)
+
+ # Negate offset
+ rot_x -= rel_x
+ rot_y -= rel_y
+
+ # Shift
+ gen_x = rot_x + self.x
+ gen_y = rot_y + self.y
+
+ return int(gen_x), int(gen_y)
+
+ def bb_in_generator(self, key_pts):
+ """
+ Convert a keypoints of bounding box in card into the coordinate in the generator
+ :param key_pts: keypoints of the bounding box
+ :return: bounding box represented by 4 points in the generator
+ """
+ coords_in_gen = [self.coordinate_in_generator(key_pt[0], key_pt[1]) for key_pt in key_pts]
+ x1 = min([pt[0] for pt in coords_in_gen])
+ x2 = max([pt[0] for pt in coords_in_gen])
+ y1 = min([pt[1] for pt in coords_in_gen])
+ y2 = max([pt[1] for pt in coords_in_gen])
+ return [(x1, y1), (x2, y1), (x2, y2), (x1, y2)]
+
class ExtractedObject:
"""
@@ -214,59 +487,87 @@
def __init__(self, label, key_pts):
self.label = label
self.key_pts = key_pts
+ self.visible = False
-def main():
+def main(args):
random.seed()
+ ia.seed(random.randrange(10000))
- img_bg = cv2.imread('data/frilly_0007.jpg')
- #img = cv2.imread('data/c16-143-burgeoning.png')
+ bg_images = generate_data.load_dtd(dtd_dir='%s/dtd/images' % Config.data_dir, dump_it=False)
+ background = generate_data.Backgrounds(images=bg_images)
- generator = ImageGenerator(img_bg, [], 1440, 960)
card_pool = pd.DataFrame()
- for set_name in fetch_data.all_set_list:
- df = fetch_data.load_all_cards_text('data/csv/%s.csv' % set_name)
- card_info = df.iloc[random.randint(0, df.shape[0] - 1)]
- # Currently ignoring planeswalker cards due to their different card layout
- is_planeswalker = 'Planeswalker' in card_info['type_line']
- if not is_planeswalker:
- card_pool = card_pool.append(card_info)
+ for set_name in Config.all_set_list:
+ df = fetch_data.load_all_cards_text('%s/csv/%s.csv' % (Config.data_dir, set_name))
+ card_pool = card_pool.append(df)
+ class_ids = {}
+ with open('%s/obj.names' % Config.data_dir) as names_file:
+ class_name_list = names_file.read().splitlines()
+ for i in range(len(class_name_list)):
+ class_ids[class_name_list[i]] = i
- for i in [random.randrange(0, card_pool.shape[0] - 1, 1) for _ in range(10)]:
- card_info = card_pool.iloc[i]
- img_name = '../usb/data/png/%s/%s_%s.png' % (card_info['set'], card_info['collector_number'],
- fetch_data.get_valid_filename(card_info['name']))
- print(img_name)
- card_img = cv2.imread(img_name)
- if card_img is None:
- fetch_data.fetch_card_image(card_info, out_dir='../usb/data/png/%s' % card_info['set'])
+ for i in range(args.num_gen):
+ # Arbitrarily select top left and right corners for perspective transformation
+ # Since the training image are generated with random rotation, don't need to skew all four sides
+ skew = [[random.uniform(0, 0.25), 0], [0, 1], [1, 1],
+ [random.uniform(0.75, 1), 0]]
+ generator = ImageGenerator(background.get_random(), class_ids, args.width, args.height, skew=skew)
+ out_name = ''
+
+ # Use 2 to 5 cards per generator
+ for _, card_info in card_pool.sample(random.randint(2, 5)).iterrows():
+ img_name = '%s/card_img/png/%s/%s_%s.png' % (Config.data_dir, card_info['set'],
+ card_info['collector_number'],
+ fetch_data.get_valid_filename(card_info['name']))
+ out_name += '%s%s_' % (card_info['set'], card_info['collector_number'])
card_img = cv2.imread(img_name)
- card = Card(card_img, card_info, None)
+ if card_img is None:
+ fetch_data.fetch_card_image(card_info, out_dir='%s/card_img/png/%s' % (Config.data_dir,
+ card_info['set']))
+ card_img = cv2.imread(img_name)
+ if card_img is None:
+ print('WARNING: card %s is not found!' % img_name)
+ detected_object_list = generate_data.apply_bounding_box(card_img, card_info)
+ card = Card(card_img, card_info, detected_object_list)
+ generator.add_card(card)
- card.bind_to_generator(generator, x=random.randint(0, generator.width), y=random.randint(0, generator.height),
- theta=random.uniform(0, math.pi / 2), scale=0.5)
- generator.display()
+ for j in range(args.num_iter):
+ seq = iaa.Sequential([
+ iaa.Multiply((0.8, 1.2)), # darken / brighten the whole image
+ iaa.SimplexNoiseAlpha(first=iaa.Add(random.randrange(64)), per_channel=0.1, size_px_max=[3, 6],
+ upscale_method="cubic"), # Lighting
+ iaa.AdditiveGaussianNoise(scale=random.uniform(0, 0.05) * 255, per_channel=0.1), # Noises
+ iaa.Dropout(p=[0, 0.05], per_channel=0.1) # Dropout
+ ])
- '''
- img_mask = cv2.bitwise_and(img, mask)
- img_rotate = imutils.rotate_bound(img_mask, 45)
+ if i % 3 == 0:
+ generator.generate_non_obstructive()
+ generator.export_training_data(visibility=0.0, out_name='%s/train/non_obstructive_update/%s%d'
+ % (Config.data_dir, out_name, j), aug=seq)
+ elif i % 3 == 1:
+ generator.generate_horizontal_span(theta=random.uniform(-math.pi, math.pi))
+ generator.export_training_data(visibility=0.0, out_name='%s/train/horizontal_span_update/%s%d'
+ % (Config.data_dir, out_name, j), aug=seq)
+ else:
+ generator.generate_vertical_span(theta=random.uniform(-math.pi, math.pi))
+ generator.export_training_data(visibility=0.0, out_name='%s/train/vertical_span_update/%s%d'
+ % (Config.data_dir, out_name, j), aug=seq)
- #cv2.imshow('rotated', img_rotate)
- #cv2.waitKey(0)
-
- mask1 = img_rotate[:, :, 1]
- cv2.imshow('mask', mask1)
- a_mask1 = np.stack([mask1] * 3, -1)
- cv2.imshow('a_mask', a_mask1)
-
- img_bg = cv2.resize(img_bg, (len(img_rotate[0]), len(img_rotate)))
- final = np.where(a_mask1, img_rotate[:, :, 0:3], img_bg)
- final = cv2.resize(final, (len(final[0]) // 2, len(final) // 2))
- cv2.imshow('final', final)
- cv2.waitKey(0)
- '''
+ #generator.generate_horizontal_span(theta=random.uniform(-math.pi, math.pi))
+ #generator.render(display=True, aug=seq, debug=True)
+ print('Generated %s%d' % (out_name, j))
+ generator.img_bg = background.get_random()
pass
if __name__ == '__main__':
- main()
+ parser = argparse.ArgumentParser()
+ parser.add_argument('-n', '--num_gen', dest='num_gen', help='Number of training images to generate',
+ type=int, required=True)
+ parser.add_argument('-ni', '--num_iter', dest='num_iter', help='Number of iterations to generate each config',
+ type=int, default=1)
+ parser.add_argument('-w', '--width', dest='width', help='Width of the training image', type=int, default=1440)
+ parser.add_argument('-ht', '--height', dest='height', help='Height of the training image', type=int, default=960)
+ args = parser.parse_args()
+ main(args)
--
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