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130 lines
4.0 KiB
130 lines
4.0 KiB
# import matplotlib.patches as patches
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import matplotlib.patches as patches
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import matplotlib.pyplot as plt
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import numpy as np
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import torch
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from dataloader import extract_colors, preprocess_data
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from model import ColorTransformerModel
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# import matplotlib.colors as mcolors
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def make_image(ckpt: str, fname: str, color=True):
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M = ColorTransformerModel.load_from_checkpoint(ckpt)
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# preds = M(rgb_tensor)
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if not color:
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N = 949
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linear_space = torch.linspace(0, 1, N)
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rgb_tensor = linear_space.unsqueeze(1).repeat(1, 3)
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else:
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rgb_tensor, names = extract_colors()
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rgb_values = rgb_tensor.detach().numpy()
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rgb_tensor = preprocess_data(rgb_tensor)
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preds = M(rgb_tensor)
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sorted_inds = np.argsort(preds.detach().numpy().ravel())
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fig, ax = plt.subplots()
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for i in range(len(sorted_inds)):
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idx = sorted_inds[i]
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color = rgb_values[idx]
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ax.plot([i + 0.5, i + 0.5], [0, 1], lw=1, c=color, antialiased=True, alpha=1)
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# rect = patches.Rectangle((i, 0), 1, 5, linewidth=0.1, edgecolor=None, facecolor=None, alpha=1)
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# ax.add_patch(rect)
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ax.axis("off")
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# ax.axis("square")
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plt.savefig(f"{fname}.png", dpi=300)
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def create_circle(ckpt: str, fname: str, dpi: int = 150):
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if isinstance(ckpt, str):
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M = ColorTransformerModel.load_from_checkpoint(ckpt)
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else:
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M = ckpt
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rgb_tensor, _ = extract_colors()
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preds = M(rgb_tensor.to(M.device))
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plot_preds(preds, fname=fname, dpi=dpi)
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def plot_preds(preds, fname: str, roll: bool = False, dpi: int = 150, figsize=(3, 3)):
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rgb_tensor, _ = extract_colors()
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rgb_values = rgb_tensor.detach().numpy()
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rgb_tensor = preprocess_data(rgb_tensor)
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if isinstance(preds, torch.Tensor):
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preds = preds.detach().cpu().numpy()
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sorted_inds = np.argsort(preds.ravel())
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colors = rgb_values[sorted_inds]
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if roll:
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# find white in colors, put it first.
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white = np.array([1, 1, 1])
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white_idx = np.where((colors == white).all(axis=1))[0][0]
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colors = np.roll(colors, -white_idx, axis=0)
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# print(white_idx, colors[:2])
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N = len(colors)
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# Create a plot with these hues in a circle
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fig, ax = plt.subplots(figsize=figsize, subplot_kw=dict(polar=True))
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# Each wedge in the circle
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theta = np.linspace(0, 2 * np.pi, N, endpoint=False) + np.pi / 2
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width = 2 * np.pi / (N) # equal size for each wedge
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for i in range(N):
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ax.bar(
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theta[i],
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1,
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width=width,
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edgecolor="none",
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facecolor=colors[i],
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bottom=0.0,
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zorder=1,
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alpha=1,
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)
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ax.set_xticks([])
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ax.set_yticks([])
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ax.axis("off")
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ax.set_aspect("equal")
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# Overlay white circle
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radius = 1 / 3
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circle = patches.Circle(
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(0, 0), radius, transform=ax.transData._b, color="white", zorder=2
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)
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ax.add_patch(circle)
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fig.tight_layout(pad=0)
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plt.savefig(f"{fname}.png", dpi=dpi, transparent=False)
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plt.close()
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if __name__ == "__main__":
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# name = "color_128_0.3_1.00e-06"
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import argparse
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import glob
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parser = argparse.ArgumentParser()
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# make the following accept a list of arguments
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parser.add_argument("-v", "--version", type=int, nargs="+", default=[0, 1])
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parser.add_argument(
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"--dpi", type=int, default=150, help="Resolution for saved image."
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)
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args = parser.parse_args()
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versions = args.version
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for v in versions:
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name = f"out/v{v}"
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# ckpt = f"/teamspace/jobs/{name}/work/colors/lightning_logs/version_2/checkpoints/epoch=999-step=8000.ckpt"
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ckpt_path = f"/teamspace/studios/this_studio/colors/lightning_logs/version_{v}/checkpoints/*.ckpt"
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ckpt = glob.glob(ckpt_path)
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if len(ckpt) > 0:
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ckpt = ckpt[-1]
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print(f"Generating image for checkpoint: {ckpt}")
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create_circle(ckpt, fname=name, dpi=args.dpi)
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else:
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print(f"No checkpoint found for version {v}")
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# make_image(ckpt, fname=name + "b", color=False)
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