Schedule Visualizer¶
Documentation¶
- Class name:
SaltKeyframeVisualizer - Category:
SALT/AudioViz/Scheduling/Util - Output node:
True
The SaltKeyframeVisualizer node is designed to visualize the keyframe scheduling process, providing a graphical representation of keyframe values across frames. It calculates various metrics for the keyframes within a specified frame range and generates a visualization image that highlights these metrics, aiding in the analysis and adjustment of keyframe-based animations.
Input types¶
Required¶
schedule_list- A list of keyframe values to be visualized. It is crucial for understanding the distribution and variation of keyframe values across the specified frame range, impacting the visualization's accuracy and detail.
- Comfy dtype:
LIST - Python dtype:
List[float]
Optional¶
start_frame- The starting frame number for the visualization. It defines the initial point of the frame range to be visualized, affecting the scope of the keyframe analysis.
- Comfy dtype:
INT - Python dtype:
int
end_frame- The ending frame number for the visualization. It marks the boundary of the frame range to be analyzed and visualized, determining the extent of the keyframe metrics calculation.
- Comfy dtype:
INT - Python dtype:
int
simulate_stereo- Indicates whether to simulate stereo visualization for the keyframes, adding a dimensional aspect to the analysis by mirroring keyframe values.
- Comfy dtype:
BOOLEAN - Python dtype:
bool
frame_rate- The frame rate used to calculate the duration of the keyframe sequence. It influences the time-based metrics and the overall temporal context of the visualization.
- Comfy dtype:
INT - Python dtype:
float
schedule_list_a- An alternative list of keyframe values, providing additional context or variations for visualization.
- Comfy dtype:
LIST - Python dtype:
List[float]
schedule_list_b- Another variation of keyframe values list, offering further insights or comparative analysis in the visualization.
- Comfy dtype:
LIST - Python dtype:
List[float]
schedule_list_c- A third variation of keyframe values list, allowing for a broader range of comparative visualization and analysis.
- Comfy dtype:
LIST - Python dtype:
List[float]
Output types¶
ui- A UI component that displays the generated keyframe visualization image. It provides a visual summary of the keyframe metrics and their distribution across the specified frame range, facilitating easier interpretation and analysis.
Usage tips¶
- Infra type:
CPU - Common nodes: unknown
Source code¶
class SaltKeyframeVisualizer:
@classmethod
def INPUT_TYPES(cls):
input_types = {
"required": {
"schedule_list": ("LIST",),
},
"optional": {
"start_frame": ("INT", {"min": 0, "default": 0}),
"end_frame": ("INT", {"min": 0, "default": -1}),
"simulate_stereo": ("BOOLEAN", {"default": False}),
"frame_rate": ("INT", {"min": 1, "default": 24}),
"schedule_list_a": ("LIST", {"default": None}),
"schedule_list_b": ("LIST", {"default": None}),
"schedule_list_c": ("LIST", {"default": None}),
}
}
return input_types
RETURN_TYPES = ()
RETURN_NAMES = ()
OUTPUT_NODE = True
FUNCTION = "visualize_keyframes"
CATEGORY = f"{MENU_NAME}/{SUB_MENU_NAME}/Scheduling/Util"
def visualize_keyframes(self, schedule_list, start_frame=0, end_frame=-1, simulate_stereo=False, frame_rate=24.0, schedule_list_a=None, schedule_list_b=None, schedule_list_c=None):
TEMP = folder_paths.get_temp_directory()
os.makedirs(TEMP, exist_ok=True)
schedule_lists = [schedule_list, schedule_list_a, schedule_list_b, schedule_list_c]
colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red']
schedule_names = ['Schedule List', 'Schedule List A', 'Schedule List B', 'Schedule List C']
metrics_data = []
plt.figure(figsize=(10, 14 if simulate_stereo else 8))
for i, sched_list in enumerate(schedule_lists):
if sched_list is not None:
if end_frame == -1 or end_frame > len(sched_list):
end_frame = len(sched_list)
num_frames = max(2, end_frame - start_frame)
frames = np.linspace(start_frame, end_frame - 1, num=num_frames, endpoint=True)
keyframe_values = np.array(sched_list[start_frame:end_frame])
plt.plot(frames, keyframe_values, color=colors[i], linewidth=0.5, label=schedule_names[i] + ' Left')
if simulate_stereo:
plt.plot(frames, -keyframe_values, color=colors[i], linewidth=0.5, linestyle='dashed', label=schedule_names[i] + ' Right')
plt.fill_between(frames, keyframe_values, 0, color=colors[i], alpha=0.3)
plt.fill_between(frames, -keyframe_values, 0, color=colors[i], alpha=0.3)
metrics = {
"Max": np.round(np.max(keyframe_values), 2),
"Min": np.round(np.min(keyframe_values), 2),
"Sum": np.round(np.sum(keyframe_values), 2),
"Avg": np.round(np.mean(keyframe_values), 2),
"Abs Sum": np.round(np.sum(np.abs(keyframe_values)), 2),
"Abs Avg": np.round(np.mean(np.abs(keyframe_values)), 2),
"Duration": (num_frames / frame_rate)
}
metrics_data.append((schedule_names[i], metrics))
plt.title('Schedule Visualization')
plt.xlabel('Frame')
plt.ylabel('Value')
plt.legend(loc='upper right')
metrics_text = "Metric Values:\n"
for name, data in metrics_data:
metrics_text += f"{name}: "
metrics_text += ' | '.join([f"{k}: {v}" for k, v in data.items()])
metrics_text += "\n"
plt.figtext(0.5, -0.2 if simulate_stereo else -0.1, metrics_text, ha="center", fontsize=12,
bbox={"facecolor": "lightblue", "alpha": 0.5, "pad": 5}, wrap=True)
filename = str(uuid.uuid4()) + "_keyframe_visualization.png"
file_path = os.path.join(TEMP, filename)
plt.savefig(file_path, bbox_inches="tight", pad_inches=1 if simulate_stereo else 0.1)
plt.close()
return {
"ui": {
"images": [
{
"filename": filename,
"subfolder": "",
"type": "temp"
}
]
}
}
@staticmethod
def gen_hash(input_dict):
sorted_json = json.dumps(input_dict, sort_keys=True)
hash_obj = hashlib.sha256()
hash_obj.update(sorted_json.encode('utf-8'))
return hash_obj.hexdigest()
@classmethod
def IS_CHANGED(cls, **kwargs):
return cls.gen_hash(kwargs)