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# -*- coding: utf-8 -*-
"""
Created on Sat Nov 8 09:54:54 2025
@author: standarduser
"""
import gradio as gr
import cv2
import numpy as np
from PIL import Image
import tempfile
import os
# Import classification function
from tabs.tab_classify_image import predict_from_space
# CSS for box styling
css = """
.box {
border: 2px solid #4CAF50;
padding: 10px;
border-radius: 10px;
background-color: #f9f9f9;
}
"""
def merge_annotations(base_image, annotations, mode, current_frame_idx, global_annotation):
"""Combines base frame with annotations"""
if base_image is None:
return None
if isinstance(base_image, np.ndarray):
img = Image.fromarray(base_image)
else:
img = base_image.copy()
# Mode B: Global annotation
if mode == "B" and global_annotation is not None:
img = Image.alpha_composite(img.convert('RGBA'), global_annotation).convert('RGB')
# Mode A: Frame-specific annotation
elif mode == "A" and current_frame_idx in annotations:
img = Image.alpha_composite(img.convert('RGBA'), annotations[current_frame_idx]).convert('RGB')
return img
def apply_transformation(frame, transformation, quality, process_image_func):
"""Applies selected transformation to frame"""
if frame is None or transformation == "None":
return frame
# Convert numpy array to PIL if needed
if isinstance(frame, np.ndarray):
pil_frame = Image.fromarray(frame)
else:
pil_frame = frame
# Call process_image with the frame and quality
result = process_image_func(pil_frame, transformation, quality)
# Extract transformed image from tuple
if isinstance(result, tuple) and len(result) == 2:
transformed = result[1]
else:
transformed = result
# CRITICAL FOR GRADIO 6.x: Convert grayscale to RGB
if transformed is not None:
if isinstance(transformed, Image.Image) and transformed.mode == 'L':
transformed = transformed.convert('RGB')
elif isinstance(transformed, np.ndarray) and len(transformed.shape) == 2:
transformed = Image.fromarray(cv2.cvtColor(transformed, cv2.COLOR_GRAY2RGB))
# Convert to numpy array
return np.array(transformed)
return frame
def create_sketchpad_value(base_image, annotations, mode, current_frame_idx, global_annotation, transformation, quality, process_image_func):
"""Creates Sketchpad value (Background + Layers)"""
if base_image is None:
return None
# Apply transformation first
transformed_frame = apply_transformation(base_image, transformation, quality, process_image_func)
# Prepare base image
if isinstance(transformed_frame, np.ndarray):
background = Image.fromarray(transformed_frame)
else:
background = transformed_frame.copy()
# Extract annotation layer
annotation_layer = None
if mode == "B" and global_annotation is not None:
annotation_layer = global_annotation
elif mode == "A" and current_frame_idx in annotations:
annotation_layer = annotations[current_frame_idx]
# Create Sketchpad dict
result = {
'background': background,
'layers': [annotation_layer] if annotation_layer is not None else [],
'composite': None
}
return result
def extract_annotation_from_sketch(sketch_data):
"""Extracts only the drawing from Sketchpad data"""
if sketch_data is None:
return None
if isinstance(sketch_data, dict):
if 'layers' in sketch_data and len(sketch_data['layers']) > 0:
drawing = sketch_data['layers'][0]
if isinstance(drawing, np.ndarray):
# Check if there are actually drawings
if len(drawing.shape) == 3 and drawing.shape[2] == 4: # RGBA
alpha = drawing[:, :, 3]
if np.any(alpha > 0):
return Image.fromarray(drawing, 'RGBA')
return None
return drawing
elif 'composite' in sketch_data and sketch_data['composite'] is not None:
composite = sketch_data['composite']
if isinstance(composite, np.ndarray):
return Image.fromarray(composite, 'RGBA')
return composite
return None
def create_comparison_slider(frame, transformation, quality, process_image_func):
"""Creates ImageSlider comparison between original and transformed frame"""
if frame is None:
return None
# Convert to PIL if needed
if isinstance(frame, np.ndarray):
original = Image.fromarray(frame)
else:
original = frame
if transformation == "None":
return (original, original)
# Apply transformation
transformed_array = apply_transformation(frame, transformation, quality, process_image_func)
if isinstance(transformed_array, np.ndarray):
transformed = Image.fromarray(transformed_array)
else:
transformed = transformed_array
return (original, transformed)
# NEW: Classification functions
def classify_current_frame(frame_idx, frames, existing_classifications):
"""Classify current frame and cache result"""
frame_idx = int(frame_idx)
# Check if already classified
if frame_idx in existing_classifications:
return (
existing_classifications[frame_idx],
f"✓ Cached result (Frame {frame_idx + 1})",
existing_classifications
)
if not frames or frame_idx >= len(frames):
return None, "✗ No frame available", existing_classifications
frame = frames[frame_idx]
# Save temp file
with tempfile.NamedTemporaryFile(suffix='.jpg', delete=False) as tmp:
Image.fromarray(frame).save(tmp.name, 'JPEG', quality=95)
tmp_path = tmp.name
try:
result = predict_from_space(tmp_path)
# Cache result
new_classifications = existing_classifications.copy()
new_classifications[frame_idx] = result
return result, f"✓ Frame {frame_idx + 1} classified", new_classifications
except Exception as e:
return None, f"✗ API Error: {str(e)}", existing_classifications
finally:
if os.path.exists(tmp_path):
os.unlink(tmp_path)
def update_classification_display(frame_idx, classifications):
"""Update classification display when switching frames"""
frame_idx = int(frame_idx)
if frame_idx in classifications:
return classifications[frame_idx], f"✓ Frame {frame_idx + 1} (cached)"
else:
return None, "Not classified yet"
def update_frame_display(frame_idx, frames, fps, annotations, global_annotation, annotation_mode, transformation, quality, process_image_func):
"""Updates frame display"""
if not frames or frame_idx >= len(frames):
return (
{"background": None, "layers": [], "composite": None}, # Fixed: Added 'composite' key
None,
f"Frame {int(frame_idx)+1} / 0",
"--:--"
)
# Calculate video time
if fps > 0:
current_time = frame_idx / fps
minutes = int(current_time // 60)
seconds = current_time % 60
time_str = f"{minutes:02d}:{seconds:05.2f}"
else:
time_str = "--:--"
# Load frame
frame = frames[int(frame_idx)]
# Create Sketchpad value with transformation
sketch_value = create_sketchpad_value(frame, annotations, annotation_mode, int(frame_idx), global_annotation, transformation, quality, process_image_func)
# Create comparison slider
slider_value = create_comparison_slider(frame, transformation, quality, process_image_func)
return sketch_value, slider_value, f"Frame {int(frame_idx)+1} / {len(frames)}", time_str
def go_to_prev_frame(current_idx, steps, frames, fps, annotations, global_annotation, annotation_mode, transformation, quality, process_image_func):
"""Goes one frame back"""
if not frames:
return 0, {"background": None, "layers": []}, None, "No video loaded", "--:--"
new_idx = max(0, int(current_idx) - steps)
sketch_value, slider_value, info, time_str = update_frame_display(new_idx, frames, fps, annotations, global_annotation, annotation_mode, transformation, quality, process_image_func)
return new_idx, sketch_value, slider_value, info, time_str
def go_to_next_frame(current_idx, steps, frames, fps, annotations, global_annotation, annotation_mode, transformation, quality, process_image_func):
"""Goes one frame forward"""
if not frames:
return 0, {"background": None, "layers": []}, None, "No video loaded", "--:--"
new_idx = min(len(frames) - 1, int(current_idx) + steps)
sketch_value, slider_value, info, time_str = update_frame_display(new_idx, frames, fps, annotations, global_annotation, annotation_mode, transformation, quality, process_image_func)
return new_idx, sketch_value, slider_value, info, time_str
def load_video_frames(video_path):
"""Loads all frames from a video"""
if video_path is None:
return [], 0, gr.update(maximum=0, value=0), "No video loaded", 0, 0, {}, None, {} # Added {} for frame_classifications
cap = cv2.VideoCapture(video_path)
frames = []
fps = cap.get(cv2.CAP_PROP_FPS)
while True:
ret, frame = cap.read()
if not ret:
break
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frames.append(frame_rgb)
cap.release()
if len(frames) == 0:
return [], 0, gr.update(maximum=0, value=0), "No frames found", 0, 0, {}, None, {} # Added {} for frame_classifications
duration = len(frames) / fps if fps > 0 else 0
return (
frames,
0,
gr.update(maximum=len(frames)-1, value=0),
f"Frame 1 / {len(frames)}",
duration,
fps,
{},
None,
{} # Reset frame_classifications
)
def save_sketch_annotation(sketch_data, mode, current_frame_idx, annotations, global_annotation):
"""Saves drawing from Sketchpad"""
annotation_img = extract_annotation_from_sketch(sketch_data)
if annotation_img is None:
return annotations, global_annotation
new_annotations = annotations.copy() if annotations else {}
new_global = global_annotation
if mode == "A":
new_annotations[current_frame_idx] = annotation_img
else: # Mode B
new_global = annotation_img
return new_annotations, new_global
def clear_annotations(mode, annotations, global_annotation):
"""Deletes annotations depending on mode"""
if mode == "A":
return {}, global_annotation
else: # Mode B
return annotations, None
def toggle_accordion(accordion_name, current_active):
"""Toggles accordion visibility and returns new transformation state with button variants"""
transformation_names = [
"Laplacian High-Pass",
"FFT Spectrum",
"Error Level Analysis",
"Wavelet Decomposition",
"Noise Extraction",
"YCbCr Channels",
"Gradient Magnitude",
"Histogram Stretching"
]
if current_active == accordion_name:
# Clicking active accordion closes it -> None
new_transformation = "None"
visibility = [False] * 8
variants = ["secondary"] * 8 # All buttons secondary (gray)
else:
# Open clicked accordion, close all others
new_transformation = accordion_name
visibility = [accordion_name == name for name in transformation_names]
# Set clicked button to primary (highlighted), others to secondary
variants = ["primary" if accordion_name == name else "secondary" for name in transformation_names]
return (new_transformation,
*[gr.update(visible=v) for v in visibility],
*[gr.update(variant=var) for var in variants])
def create_tab_videoframes(tab_label, process_image, shared_video_frames=None):
"""Creates a tab for video frame processing"""
with gr.TabItem(tab_label):
# Use shared state if provided, otherwise create local state
if shared_video_frames is None:
video_frames = gr.State([])
else:
video_frames = shared_video_frames
current_frame_idx = gr.State(0)
video_duration = gr.State(0)
video_fps = gr.State(0)
frame_annotations = gr.State({})
global_annotation = gr.State(None)
annotation_mode = gr.State("A")
selected_transformation = gr.State("None")
ela_quality = gr.State(90)
frame_classifications = gr.State({}) # NEW: Store classification results
# Row 1: raw video
with gr.Accordion("Video Input", open=True):
with gr.Row():
with gr.Column():
video_input = gr.Video(label="Upload video", height=600, sources=['upload'], scale=1)
with gr.Row():
gr.Markdown("---")
# Row 2: video annotations
with gr.Row():
with gr.Column(scale=6):
with gr.Tabs():
with gr.TabItem("Comparison"):
comparison_slider = gr.ImageSlider(
label="Original vs Transformed",
height=600
)
with gr.TabItem("Annotations"):
with gr.Row():
radio_mode = gr.Radio(
choices=[("Per Frame", "A"), ("Global", "B")],
value="A",
label="Annotation Mode",
info="Per Frame: Drawings for each frame separately | Global: One drawing over all frames",
scale=3
)
btn_clear_annotations = gr.Button("Clear Annotations", variant="stop", scale=1, size="sm")
with gr.Row():
sketch_output = gr.Sketchpad(
label="Video Frame (drawing enabled)",
height=600,
brush=gr.Brush(
colors=["#FF0000", "#00FF00", "#7a7990", "#0000FF", "#FFFF00", "#FF00FF", "#00FFFF", "#FFFFFF", "#000000"],
default_size=3
),
type="numpy",
scale=2
)
with gr.Column(scale=1, min_width=1):
frame_info = gr.Textbox(label="Frame Info", value="No video loaded", interactive=False, scale=2)
video_time_display = gr.Textbox(label="Video Time", value="--:--", interactive=False, scale=1)
gr.Markdown("---")
# Accordion-based transformation selection
with gr.Column():
gr.Markdown("### Frame Transformation")
gr.Markdown("*Click to activate transformation*")
# Laplacian High-Pass
btn_laplacian = gr.Button("â–¶ Laplacian High-Pass", size="sm")
with gr.Column(visible=False) as content_laplacian:
gr.Markdown("Emphasizes high-frequency details and edges")
# FFT Spectrum
btn_fft = gr.Button("â–¶ FFT Spectrum", size="sm")
with gr.Column(visible=False) as content_fft:
gr.Markdown("Shows frequency domain representation")
# Error Level Analysis
btn_ela = gr.Button("â–¶ Error Level Analysis", size="sm")
with gr.Column(visible=False) as content_ela:
gr.Markdown("Detects JPEG compression artifacts")
quality_slider = gr.Slider(
minimum=1,
maximum=99,
value=90,
step=1,
label="JPEG Quality",
info="Higher = more subtle differences"
)
# Wavelet Decomposition
btn_wavelet = gr.Button("â–¶ Wavelet Decomposition", size="sm")
with gr.Column(visible=False) as content_wavelet:
gr.Markdown("Multi-scale frequency analysis")
# Noise Extraction
btn_noise = gr.Button("â–¶ Noise Extraction", size="sm")
with gr.Column(visible=False) as content_noise:
gr.Markdown("Isolates high-frequency noise")
# YCbCr Channels
btn_ycbcr = gr.Button("â–¶ YCbCr Channels", size="sm")
with gr.Column(visible=False) as content_ycbcr:
gr.Markdown("Separates luminance and chrominance")
# Gradient Magnitude
btn_gradient = gr.Button("â–¶ Gradient Magnitude", size="sm")
with gr.Column(visible=False) as content_gradient:
gr.Markdown("Visualizes edge strength via Sobel")
# Histogram Stretching
btn_histogram = gr.Button("â–¶ Histogram Stretching", size="sm")
with gr.Column(visible=False) as content_histogram:
gr.Markdown("Extreme contrast enhancement")
# Row: Frame Classification
with gr.Row():
gr.Markdown("---")
with gr.Accordion("Frame Classification - (optimized model for ai images)", open=False):
with gr.Row():
with gr.Column(scale=1):
with gr.Row():
btn_classify_frame = gr.Button("Classify Current Frame", size="sm", variant="primary")
btn_classify_all = gr.Button("Classify All Frames (Coming Soon)", size="sm", interactive=False)
with gr.Column(scale=2):
classification_result = gr.Label(num_top_classes=2, label="Result")
with gr.Column(scale=1):
classification_status = gr.Textbox(label="Status", value="Not classified yet", interactive=False)
# Row: Frame navigation
with gr.Row():
gr.Markdown("---")
with gr.Row():
btn_prev10_frame = gr.Button("◀◀ -10", scale=0, min_width=70)
btn_prev_frame = gr.Button("â—€ -1", scale=0, min_width=70)
frame_slider = gr.Slider(
minimum=0,
maximum=100,
step=1,
value=0,
label="Frame Navigation",
interactive=True,
scale=20
)
btn_next_frame = gr.Button("â–¶ +1", scale=0, min_width=70)
btn_next10_frame = gr.Button("▶▶ +10", scale=0, min_width=70)
with gr.Row():
gr.Markdown("---")
# Collect all content columns for visibility updates
content_columns = [
content_laplacian,
content_fft,
content_ela,
content_wavelet,
content_noise,
content_ycbcr,
content_gradient,
content_histogram
]
# Collect all buttons for variant updates
transformation_buttons = [
btn_laplacian,
btn_fft,
btn_ela,
btn_wavelet,
btn_noise,
btn_ycbcr,
btn_gradient,
btn_histogram
]
# NEW: Classification button event
btn_classify_frame.click(
fn=classify_current_frame,
inputs=[frame_slider, video_frames, frame_classifications],
outputs=[classification_result, classification_status, frame_classifications]
)
# Accordion button clicks
btn_laplacian.click(
fn=lambda current: toggle_accordion("Laplacian High-Pass", current),
inputs=[selected_transformation],
outputs=[selected_transformation] + content_columns + transformation_buttons
).then(
fn=lambda idx, frames, fps, annots, glob_annot, mode, trans, quality: update_frame_display(idx, frames, fps, annots, glob_annot, mode, trans, quality, process_image),
inputs=[frame_slider, video_frames, video_fps, frame_annotations, global_annotation, annotation_mode, selected_transformation, ela_quality],
outputs=[sketch_output, comparison_slider, frame_info, video_time_display]
)
btn_fft.click(
fn=lambda current: toggle_accordion("FFT Spectrum", current),
inputs=[selected_transformation],
outputs=[selected_transformation] + content_columns + transformation_buttons
).then(
fn=lambda idx, frames, fps, annots, glob_annot, mode, trans, quality: update_frame_display(idx, frames, fps, annots, glob_annot, mode, trans, quality, process_image),
inputs=[frame_slider, video_frames, video_fps, frame_annotations, global_annotation, annotation_mode, selected_transformation, ela_quality],
outputs=[sketch_output, comparison_slider, frame_info, video_time_display]
)
btn_ela.click(
fn=lambda current: toggle_accordion("Error Level Analysis", current),
inputs=[selected_transformation],
outputs=[selected_transformation] + content_columns + transformation_buttons
).then(
fn=lambda idx, frames, fps, annots, glob_annot, mode, trans, quality: update_frame_display(idx, frames, fps, annots, glob_annot, mode, trans, quality, process_image),
inputs=[frame_slider, video_frames, video_fps, frame_annotations, global_annotation, annotation_mode, selected_transformation, ela_quality],
outputs=[sketch_output, comparison_slider, frame_info, video_time_display]
)
btn_wavelet.click(
fn=lambda current: toggle_accordion("Wavelet Decomposition", current),
inputs=[selected_transformation],
outputs=[selected_transformation] + content_columns + transformation_buttons
).then(
fn=lambda idx, frames, fps, annots, glob_annot, mode, trans, quality: update_frame_display(idx, frames, fps, annots, glob_annot, mode, trans, quality, process_image),
inputs=[frame_slider, video_frames, video_fps, frame_annotations, global_annotation, annotation_mode, selected_transformation, ela_quality],
outputs=[sketch_output, comparison_slider, frame_info, video_time_display]
)
btn_noise.click(
fn=lambda current: toggle_accordion("Noise Extraction", current),
inputs=[selected_transformation],
outputs=[selected_transformation] + content_columns + transformation_buttons
).then(
fn=lambda idx, frames, fps, annots, glob_annot, mode, trans, quality: update_frame_display(idx, frames, fps, annots, glob_annot, mode, trans, quality, process_image),
inputs=[frame_slider, video_frames, video_fps, frame_annotations, global_annotation, annotation_mode, selected_transformation, ela_quality],
outputs=[sketch_output, comparison_slider, frame_info, video_time_display]
)
btn_ycbcr.click(
fn=lambda current: toggle_accordion("YCbCr Channels", current),
inputs=[selected_transformation],
outputs=[selected_transformation] + content_columns + transformation_buttons
).then(
fn=lambda idx, frames, fps, annots, glob_annot, mode, trans, quality: update_frame_display(idx, frames, fps, annots, glob_annot, mode, trans, quality, process_image),
inputs=[frame_slider, video_frames, video_fps, frame_annotations, global_annotation, annotation_mode, selected_transformation, ela_quality],
outputs=[sketch_output, comparison_slider, frame_info, video_time_display]
)
btn_gradient.click(
fn=lambda current: toggle_accordion("Gradient Magnitude", current),
inputs=[selected_transformation],
outputs=[selected_transformation] + content_columns + transformation_buttons
).then(
fn=lambda idx, frames, fps, annots, glob_annot, mode, trans, quality: update_frame_display(idx, frames, fps, annots, glob_annot, mode, trans, quality, process_image),
inputs=[frame_slider, video_frames, video_fps, frame_annotations, global_annotation, annotation_mode, selected_transformation, ela_quality],
outputs=[sketch_output, comparison_slider, frame_info, video_time_display]
)
btn_histogram.click(
fn=lambda current: toggle_accordion("Histogram Stretching", current),
inputs=[selected_transformation],
outputs=[selected_transformation] + content_columns + transformation_buttons
).then(
fn=lambda idx, frames, fps, annots, glob_annot, mode, trans, quality: update_frame_display(idx, frames, fps, annots, glob_annot, mode, trans, quality, process_image),
inputs=[frame_slider, video_frames, video_fps, frame_annotations, global_annotation, annotation_mode, selected_transformation, ela_quality],
outputs=[sketch_output, comparison_slider, frame_info, video_time_display]
)
# Quality slider change (only affects ELA)
quality_slider.change(
fn=lambda q: q,
inputs=[quality_slider],
outputs=[ela_quality]
).then(
fn=lambda idx, frames, fps, annots, glob_annot, mode, trans, quality: update_frame_display(idx, frames, fps, annots, glob_annot, mode, trans, quality, process_image),
inputs=[frame_slider, video_frames, video_fps, frame_annotations, global_annotation, annotation_mode, selected_transformation, ela_quality],
outputs=[sketch_output, comparison_slider, frame_info, video_time_display]
)
# Video Upload - MODIFIED: Added frame_classifications to outputs
video_input.change(
fn=load_video_frames,
inputs=[video_input],
outputs=[video_frames, current_frame_idx, frame_slider, frame_info, video_duration, video_fps, frame_annotations, global_annotation, frame_classifications]
).then(
fn=lambda idx, frames, fps, annots, glob_annot, mode, trans, quality: update_frame_display(idx, frames, fps, annots, glob_annot, mode, trans, quality, process_image),
inputs=[current_frame_idx, video_frames, video_fps, frame_annotations, global_annotation, annotation_mode, selected_transformation, ela_quality],
outputs=[sketch_output, comparison_slider, frame_info, video_time_display]
).then(
fn=lambda: (None, "Not classified yet"), # Reset classification display
inputs=[],
outputs=[classification_result, classification_status]
)
# Frame Navigation - MODIFIED: Added classification display update
frame_slider.release(
fn=lambda idx, frames, fps, annots, glob_annot, mode, trans, quality: update_frame_display(idx, frames, fps, annots, glob_annot, mode, trans, quality, process_image),
inputs=[frame_slider, video_frames, video_fps, frame_annotations, global_annotation, annotation_mode, selected_transformation, ela_quality],
outputs=[sketch_output, comparison_slider, frame_info, video_time_display]
).then(
fn=update_classification_display,
inputs=[frame_slider, frame_classifications],
outputs=[classification_result, classification_status]
)
btn_prev_frame.click(
fn=lambda idx, frames, fps, annots, glob_annot, mode, trans, quality: go_to_prev_frame(idx, 1, frames, fps, annots, glob_annot, mode, trans, quality, process_image),
inputs=[frame_slider, video_frames, video_fps, frame_annotations, global_annotation, annotation_mode, selected_transformation, ela_quality],
outputs=[frame_slider, sketch_output, comparison_slider, frame_info, video_time_display]
).then(
fn=update_classification_display,
inputs=[frame_slider, frame_classifications],
outputs=[classification_result, classification_status]
)
btn_next_frame.click(
fn=lambda idx, frames, fps, annots, glob_annot, mode, trans, quality: go_to_next_frame(idx, 1, frames, fps, annots, glob_annot, mode, trans, quality, process_image),
inputs=[frame_slider, video_frames, video_fps, frame_annotations, global_annotation, annotation_mode, selected_transformation, ela_quality],
outputs=[frame_slider, sketch_output, comparison_slider, frame_info, video_time_display]
).then(
fn=update_classification_display,
inputs=[frame_slider, frame_classifications],
outputs=[classification_result, classification_status]
)
btn_prev10_frame.click(
fn=lambda idx, frames, fps, annots, glob_annot, mode, trans, quality: go_to_prev_frame(idx, 10, frames, fps, annots, glob_annot, mode, trans, quality, process_image),
inputs=[frame_slider, video_frames, video_fps, frame_annotations, global_annotation, annotation_mode, selected_transformation, ela_quality],
outputs=[frame_slider, sketch_output, comparison_slider, frame_info, video_time_display]
).then(
fn=update_classification_display,
inputs=[frame_slider, frame_classifications],
outputs=[classification_result, classification_status]
)
btn_next10_frame.click(
fn=lambda idx, frames, fps, annots, glob_annot, mode, trans, quality: go_to_next_frame(idx, 10, frames, fps, annots, glob_annot, mode, trans, quality, process_image),
inputs=[frame_slider, video_frames, video_fps, frame_annotations, global_annotation, annotation_mode, selected_transformation, ela_quality],
outputs=[frame_slider, sketch_output, comparison_slider, frame_info, video_time_display]
).then(
fn=update_classification_display,
inputs=[frame_slider, frame_classifications],
outputs=[classification_result, classification_status]
)
# Sketchpad Change - Saves drawing
sketch_output.change(
fn=save_sketch_annotation,
inputs=[sketch_output, annotation_mode, frame_slider, frame_annotations, global_annotation],
outputs=[frame_annotations, global_annotation]
)
# Mode Change
radio_mode.change(
fn=lambda new_mode: new_mode,
inputs=[radio_mode],
outputs=[annotation_mode]
).then(
fn=lambda idx, frames, fps, annots, glob_annot, mode, trans, quality: update_frame_display(idx, frames, fps, annots, glob_annot, mode, trans, quality, process_image),
inputs=[frame_slider, video_frames, video_fps, frame_annotations, global_annotation, annotation_mode, selected_transformation, ela_quality],
outputs=[sketch_output, comparison_slider, frame_info, video_time_display]
)
# Clear Annotations
btn_clear_annotations.click(
fn=clear_annotations,
inputs=[annotation_mode, frame_annotations, global_annotation],
outputs=[frame_annotations, global_annotation]
).then(
fn=lambda idx, frames, fps, annots, glob_annot, mode, trans, quality: update_frame_display(idx, frames, fps, annots, glob_annot, mode, trans, quality, process_image),
inputs=[frame_slider, video_frames, video_fps, frame_annotations, global_annotation, annotation_mode, selected_transformation, ela_quality],
outputs=[sketch_output, comparison_slider, frame_info, video_time_display]
)
# Return video_frames state for sharing with other tabs
return video_frames |