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#!/usr/bin/env python3
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import pyopencl as cl
from alive_progress import alive_bar
from matplotlib.backend_bases import MouseButton
from PIL import Image
img_res_x = 1000
img_res_y = 1000
total_pixels = img_res_x * img_res_y # so we don't gotta compute it every time
periods = 1
square_x = 0
square_y = 0
x_min = (-periods * np.pi) + (square_x * np.pi)
x_max = (periods * np.pi) + (square_x * np.pi)
y_min = (-periods * np.pi) + (square_y * np.pi)
y_max = (periods * np.pi) + (square_y * np.pi)
escape = 10000
iterations = 255*3
c_x = 2 * np.pi
c_y = 2 * np.pi
animation_progres_save = "ani1_less.mp4"
single_frame_save = "asdf.png"
opencl_context = cl.create_some_context(interactive=False)
opencl_queue = cl.CommandQueue(opencl_context)
kernel_src_path = "./kernel.c"
frames = 1
rendered_frames = []
image = np.empty([img_res_x, img_res_y], np.uint32)
plt.style.use('dark_background')
# fuck this shit
fig = plt.figure(frameon=False)
fig.set_size_inches(img_res_x/fig.dpi, img_res_y/fig.dpi)
#fig.set_size_inches(width/height, 1, forward=False)
ax = plt.Axes(fig, [0., 0., 1., 1.])
#ax.set_axis_off()
fig.add_axes(ax)
cmap = plt.cm.viridis
cmap.set_bad((0,0,0))
cmap.set_over((0,0,0))
cmap.set_under((0,0,0))
mask_path = "mask.png"
bruh = None
def on_click(event):
global bruh
split_ratio = 1
if (event.button is MouseButton.MIDDLE) and (event.inaxes):
# there's probably a way to set global coordinates;
# I don't expect this to go anywhere so I don't really care
on_x = ((event.xdata / img_res_x) * abs(x_max - x_min)) + x_min
on_y = ((event.ydata / img_res_y) * abs(y_max - y_min)) + y_min
#I, uh, also don't know the best way to replicate the openCL code automaticly in python
#so ajust if nessesary
x_hops = []
y_hops = []
for i in range(iterations):
x_hops.append(((on_x - x_min) / abs(x_max - x_min)) * img_res_x)
y_hops.append(((on_y - y_min) / abs(y_max - y_min)) * img_res_y)
next_x = on_x/np.tan(on_y)
on_y = on_y/np.tan(on_x)
on_x = next_x
if on_x**2 + on_y**2 > escape:
break
print(y_hops[0])
print(on_y)
print("{} hops".format(len(x_hops)))
if bruh:
bruh.pop(0).remove()
bruh = ax.plot(x_hops, y_hops)
plt.draw()
print(on_x, on_y)
print("compiling openCL kernel...")
with open(kernel_src_path, 'r') as kernel_src:
compiled_kernel = cl.Program(opencl_context, kernel_src.read()).build()
encoding_progress = alive_bar(frames, bar = 'filling', spinner = 'waves')
def display_encoder_progress(current_frame: int, total_frames: int):
print("Encoding: frame {}/{}".format(current_frame, frames))
#open image
mask = np.asarray(Image.open(mask_path).convert("L"), dtype=np.double)
mask.setflags(write=1)
mask /= np.max(mask) # normalize
print(mask.shape)
print(mask.dtype)
#ax.imshow(mask, norm="linear")
#plt.show()
image_buffer = cl.Buffer(opencl_context, cl.mem_flags.WRITE_ONLY, image.nbytes)
mask_buffer = cl.Buffer(opencl_context, cl.mem_flags.READ_ONLY, mask.nbytes)
cl.enqueue_copy(opencl_queue, mask_buffer, mask).wait()
#mask_buffer = cl.array.Array(opencl_context,mask.shape, dtype=np.uint8, data=mask)
# TODO clean this up
print("Rendering {} frames...".format(frames))
if frames > 1:
with alive_bar(frames, bar = 'filling', spinner = 'waves') as bar_total:
for frame_i in range(0, frames):
compiled_kernel.render_frame(opencl_queue, image.shape, None, image_buffer,
np.double(abs(x_max - x_min) / img_res_x),
np.double(abs(y_max - y_min) / img_res_y),
np.double(x_min), np.double(y_min),
np.uint32(iterations), np.uint32(escape),
np.double(frame_i / frames))
cl.enqueue_copy(opencl_queue, image, image_buffer).wait()
rendered_frame = ax.imshow(image, norm="log", aspect="auto", cmap=cmap, animated="True")
rendered_frames.append([rendered_frame])
bar_total()
print("Encoding/Saving...")
ani = animation.ArtistAnimation(fig, rendered_frames, interval=30, blit=True)
ani.save(animation_progres_save, extra_args=['-preset', 'lossless'], progress_callback=display_encoder_progress, codec="h264_nvenc")
else:
compiled_kernel.render_frame(opencl_queue, image.shape, None, image_buffer, mask_buffer,
np.double(abs(x_max - x_min) / img_res_x),
np.double(abs(y_max - y_min) / img_res_y),
np.double(x_min), np.double(y_min),
np.uint32(iterations), np.uint32(escape),
np.double(1))
cl.enqueue_copy(opencl_queue, image, image_buffer).wait()
ax.imshow(image, norm="log", aspect="auto", cmap=cmap)
fig.savefig(single_frame_save)
plt.autoscale(False)
plt.connect('motion_notify_event', on_click)
plt.show()
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