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Diffstat (limited to 'display_animation_tkinter.py')
| -rw-r--r-- | display_animation_tkinter.py | 151 |
1 files changed, 0 insertions, 151 deletions
diff --git a/display_animation_tkinter.py b/display_animation_tkinter.py deleted file mode 100644 index f26202c..0000000 --- a/display_animation_tkinter.py +++ /dev/null @@ -1,151 +0,0 @@ -import argparse -import json -import math -import time -import tkinter as tk - -import numpy as np - -from utils import rgb_to_hex - - -def parse_args(): - return { - "cell_size": 8, # in pixels - "num_cells_w": 100, # TODO: w/h or r/c? - "num_cells_h": 100, - "colors_path": "configs/colors_64_v0.json", - "corners_path": "configs/corners_hollow4x4_v0.json", - "frame_delay": 1000 // 15, # time b/w frames, in millis - } - - -def seq_to_frames(bin_seq: np.ndarray, args: dict) -> np.ndarray: # TODO: Namespace - """ - Converts a binary sequence to an array of frames. - TODO: doc, expecting size of exactly one frame in the future? - - np.packbits seems relevant but limited to 8 bits - - Args: - bin_seq: a 1D array of binary values. - args: config parameters - - Returns: - array of shape (num_frames, args["num_cells_h"], args["num_cells_w"]), where each element is - a hex string for the color of that cell - """ - bin_seq = bin_seq.copy() # so that we don't mutate bin_seq - - with open(args["colors_path"], "r") as f: - colors = json.load(f) - - with open(args["corners_path"], "r") as f: - corners = json.load(f) - - assert len(corners["corner_colors"]) == 4, "Hardcoded for 4 corners" - corner_width = corners["corner_width"] - corner_height = corners["corner_height"] - - num_colors = len(colors) - bits_per_cell = int(math.log2(num_colors)) - assert 2**bits_per_cell == num_colors, "Assumed the number of colors is a power of 2." - - # bits_per_frame = bits_per_cell * args["num_cells_w"] * args["num_cells_h"] - # num_frames = int(math.ceil(len(bin_seq) / bits_per_frame)) - # bin_seq.resize(bits_per_frame * num_frames) - # # frames_bits = bin_seq.reshape(num_frames, args["num_cells_h"], args["num_cells_w"], bits_per_cell) - # - # pows = 2 ** np.arange(bits_per_cell) - # frames_vals = (frames_bits * pows).sum(axis=-1) # low to high bit order - - cells_per_frame = args["num_cells_w"] * args["num_cells_h"] - 4 * corner_width * corner_height - bits_per_frame = bits_per_cell * cells_per_frame - num_frames = int(math.ceil(len(bin_seq) / bits_per_frame)) - bin_seq.resize(bits_per_frame * num_frames) - frames_bits = bin_seq.reshape(num_frames, cells_per_frame, bits_per_cell) - pows = 2 ** np.arange(bits_per_cell) - frames_vals = (frames_bits * pows).sum(axis=-1) # low to high bit order - - # Efficiently map frame_vals to the corresponding hex colors (https://stackoverflow.com/a/55950051) - color_mapping_arr = np.empty(num_colors, dtype="<U7") - for i in range(num_colors): - color_mapping_arr[i] = rgb_to_hex(colors[str(i)]) # JSON keys stored as str - - frames_colors = color_mapping_arr[frames_vals] - - num_top_cells = (args["num_cells_w"] - 2 * corner_width) * corner_height # TODO: explain - top_cells = frames_colors[:, :num_top_cells].reshape(num_frames, corner_height, -1) - center_cells = frames_colors[:, num_top_cells:-num_top_cells].reshape(num_frames, -1, args["num_cells_w"]) - bottom_cells = frames_colors[:, -num_top_cells:].reshape(num_frames, corner_height, -1) - - corners_numpy_dict = {key: np.broadcast_to(val, (num_frames, corner_height, corner_width)) - for key, val in corners["corner_colors"].items()} - top_rows = np.concatenate([corners_numpy_dict["0"], top_cells, corners_numpy_dict["1"]], axis=2) - bottom_rows = np.concatenate([corners_numpy_dict["2"], bottom_cells, corners_numpy_dict["3"]], axis=2) - - return np.concatenate([top_rows, center_cells, bottom_rows], axis=1) - - -class AnimatedFrames: - def __init__(self, frames: np.ndarray, args: dict): - self.frames = frames - self.args = args - self.width_pixels = args["cell_size"] * args["num_cells_w"] - self.height_pixels = args["cell_size"] * args["num_cells_h"] - - self.root = tk.Tk() - self.root.columnconfigure(0, weight=1) - self.root.rowconfigure(0, weight=1) - self.canvas = tk.Canvas(self.root, width=self.width_pixels, height=self.height_pixels, - borderwidth=0, highlightthickness=0) # https://stackoverflow.com/a/63220348 - self.canvas.grid(column=0, row=0) - - # We will create each cell (rectangle) now, and then update their colors in different frames - self.inds_to_id = dict() # map from (row_ind, col_ind) to the id of the rectangle at that cell position - for i in range(args["num_cells_h"]): - for j in range(args["num_cells_w"]): - self.inds_to_id[(i, j)] = self.canvas.create_rectangle( - i * args["cell_size"], j * args["cell_size"], (i + 1) * args["cell_size"], - (j + 1) * args["cell_size"], width=0 - ) - - self.frame_ind = 0 # the index in `self.frames` of the next frame to display - self.start_time = None - self._debug_text_id = self.canvas.create_text((args["num_cells_w"] - 2) * args["cell_size"], (args["num_cells_h"] - 2) * args["cell_size"]) - - def display_frame(self): - func_start_time = time.time_ns() - for inds_tuple, color_hex in np.ndenumerate(self.frames[self.frame_ind % len(self.frames)]): - self.canvas.itemconfigure(self.inds_to_id[inds_tuple], fill=color_hex) - - self.canvas.itemconfigure(self._debug_text_id, text=self.frame_ind) - self.frame_ind += 1 - - adjusted_delay = round((self.start_time + self.args["frame_delay"] * int(1e6) - * self.frame_ind - func_start_time) / 1e6) - assert adjusted_delay > 1, adjusted_delay # o/w we lagged too far behind, assuming 1 ms for this instruction - self.canvas.after(adjusted_delay, self.display_frame) # TODO: check delay is exact - # TODO: set self time and just sleep until then - - def animate(self): - epilepsy_warning_id = self.canvas.create_text(self.width_pixels / 2, self.height_pixels / 2, - text="Warning: Epilepsy") - - def delete_and_animate(): - self.canvas.delete(epilepsy_warning_id) - self.start_time = time.time_ns() - self.display_frame() - - self.canvas.after(5000, delete_and_animate) - # self.display_frame() - self.root.mainloop() - - -if __name__ == "__main__": - args = parse_args() - - rand_bin_seq = np.random.randint(2, size=1000000) - frames = seq_to_frames(rand_bin_seq, args) - - AnimatedFrames(frames, args).animate() |