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02f1218 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 | import matplotlib.pyplot as plt
import cv2
import PIL
from PIL import Image
import random
import numpy as np
import os
import json
import argparse
def generate_shapes(num_images, num_objects_list , file):
# Set up the plot
output_dir = os.path.join(os.getcwd(), "data")
if not os.path.exists(output_dir):
os.makedirs(output_dir)
data = []
append = (file != 1)
for n in num_objects_list:
for img_index in range(num_images):
fig, ax = plt.subplots()
ax.set_aspect('equal')
plt.axis('off')
# Adjust plot limits to prevent cutoff
plt.xlim(-0.1, 1.1)
plt.ylim(-0.1, 1.1)
# Generate random positions for the shapes
positions = []
radius = 0.05
shapes = ['circle', 'triangle', 'rectangle', 'pentagon']
# Must be a list in the order given in prompts/rules.txt: gold_answer is built by
# iterating it. (A set here made the gold order depend on the process's hash seed.)
colours = ['black', 'gray', 'brown', 'maroon', 'red', 'coral', 'tan', 'orange', 'ivory', 'goldenrod', 'yellow', 'green', 'olive', 'turquoise', 'skyblue', 'blue', 'lavender', 'purple', 'pink', 'fuchsia']
colours_n = random.sample(colours, n)
gold_answer = []
for i in colours:
if i in colours_n:
gold_answer.append('Yes')
else:
gold_answer.append('No')
for i in range(n):
while True:
x, y = random.uniform(0.1, 0.9), random.uniform(0.1, 0.9)
# Ensure shapes don't overlap
if all(((x - px) ** 2 + (y - py) ** 2) ** 0.5 > 2 * radius for px, py in positions):
positions.append((x, y))
break
# Choose one odd-colored shape
#odd_index = random.randint(0, n - 1)
# Draw shapes and label them
for i, (x, y) in enumerate(positions):
color = colours_n[i]
shape_type = random.choice(shapes)
if shape_type == 'circle':
shape = plt.Circle((x, y), radius, ec='black', color=color)
elif shape_type == 'triangle':
triangle = np.array([[x, y + radius], [x - radius, y - radius], [x + radius, y - radius]])
shape = plt.Polygon(triangle, ec='black', color=color)
elif shape_type == 'rectangle':
shape = plt.Rectangle((x - radius, y - radius), 2 * radius, 2 * radius, ec='black', color=color)
elif shape_type == 'pentagon':
pentagon = np.array([
[x, y + radius], [x + 0.95 * radius, y + 0.31 * radius],
[x + 0.59 * radius, y - 0.81 * radius], [x - 0.59 * radius, y - 0.81 * radius],
[x - 0.95 * radius, y + 0.31 * radius]
])
shape = plt.Polygon(pentagon, ec='black', color=color)
ax.add_patch(shape)
#ax.text(x, y, str(i + 1), color='white', ha='center', va='center', fontsize=8, weight='bold')
# Save the plot to a file
filename = f"{file}.png"
filepath = os.path.join(output_dir, filename)
plt.savefig(filepath, bbox_inches='tight')
plt.close()
img1 = cv2.imread("data/"+f"{file}.png")
img2 = cv2.imread("swatches.png")
if img1.shape[1] != img2.shape[1]:
max_width = max(img1.shape[1], img2.shape[1])
img1 = cv2.resize(img1, (max_width, img1.shape[0]))
img2 = cv2.resize(img2, (max_width, img2.shape[0]))
merged_image = cv2.vconcat([img1, img2])
cv2.imwrite(filepath, merged_image)
file += 1
gold_output = {
"id": filename,
"colours_present": gold_answer,
"num_objects": n
}
data.append(gold_output)
if append:
with open(os.path.join(os.getcwd() , "data.json"), "r") as f:
old_data = json.load(f)
old_data.extend(data)
with open(os.path.join(os.getcwd() , "data.json"), "w") as f:
json.dump(old_data, f, indent=2)
else:
with open(os.path.join(os.getcwd() , "data.json"), "w") as f:
json.dump(data, f, indent=2)
# # Example usage
# generate_shapes(10, 'shapes.png')
if __name__ == "__main__":
# Parse command line arguments
parser = argparse.ArgumentParser(description='Create an image of different objects')
parser.add_argument(
'--num_images',
type=int,
help='Number of images to generate',
default=1
)
parser.add_argument(
'--num_sizes',
nargs='*',
type=int,
help='List of count of objects',
default=[5]
)
parser.add_argument(
'--file',
type=int,
help='Starting file number',
default=1
)
args = parser.parse_args()
file = args.file
num_object_list = args.num_sizes
append = (file != 1)
generate_shapes(args.num_images, num_object_list , file)
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