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