"""
Filename: main.py
Description: BLAZES main executable
Author: Tyler de Zeeuw
License: GPL-3.0
"""
# Built-in imports
import os
import csv
import sys
import json
import glob
import shutil
import inspect
import platform
import traceback
from pathlib import Path
from datetime import datetime
from multiprocessing import current_process, freeze_support
# External library imports
import numpy as np
import pandas as pd
import psutil
import joblib
import cv2
from ultralytics import YOLO
from updater import finish_update_if_needed, UpdateManager, LocalPendingUpdateCheckThread
from predictor import GeneralPredictor
from batch_processing import BatchProcessorDialog
import PySide6
from PySide6.QtWidgets import (QApplication, QMainWindow, QWidget, QVBoxLayout, QGraphicsView, QGraphicsScene,
QHBoxLayout, QSplitter, QLabel, QPushButton, QComboBox, QInputDialog,
QFileDialog, QScrollArea, QMessageBox, QSlider, QTextEdit)
from PySide6.QtCore import Qt, QThread, Signal, QUrl, QRectF, QPointF, QRect, QSizeF
from PySide6.QtGui import QPainter, QColor, QFont, QPen, QBrush, QAction, QKeySequence, QIcon, QTextOption
from PySide6.QtMultimedia import QMediaPlayer, QAudioOutput
from PySide6.QtMultimediaWidgets import QGraphicsVideoItem
VERBOSITY = 1
CURRENT_VERSION = "0.1.0"
APP_NAME = "blazes"
API_URL = f"https://git.research.dezeeuw.ca/api/v1/repos/tyler/{APP_NAME}/releases"
API_URL_SECONDARY = f"https://git.research2.dezeeuw.ca/api/v1/repos/tyler/{APP_NAME}/releases"
PLATFORM_NAME = platform.system().lower()
def debug_print():
if VERBOSITY:
frame = inspect.currentframe().f_back
qualname = frame.f_code.co_qualname
print(qualname)
# Ordered according to YOLO docs: https://docs.ultralytics.com/tasks/pose/
JOINT_NAMES = [
"Nose", "Left Eye", "Right Eye", "Left Ear", "Right Ear",
"Left Shoulder", "Right Shoulder", "Left Elbow", "Right Elbow",
"Left Wrist", "Right Wrist", "Left Hip", "Right Hip",
"Left Knee", "Right Knee", "Left Ankle", "Right Ankle"
]
# Needs to be pointed to the FFmpeg bin folder containing avcodec-*.dll, etc.
pyside_dir = Path(PySide6.__file__).parent
if sys.platform == "win32":
# Tell Python 3.13+ where to find the FFmpeg DLLs bundled with PySide
os.add_dll_directory(str(pyside_dir))
TRACK_NAMES = ["Baseline", "Live Skeleton"] + JOINT_NAMES
NUM_TRACKS = len(TRACK_NAMES)
# TODO: Improve colors?
# Generate distinct colors for the tracks
BASE_COLORS = [QColor(180, 180, 180), QColor(0, 0, 0)] # Grey for Baseline, Black for Live
REMAINING_COLORS = [QColor.fromHsv(int((i / (NUM_TRACKS-2)) * 359), 200, 255) for i in range(NUM_TRACKS-2)]
TRACK_COLORS = BASE_COLORS + REMAINING_COLORS
class AboutWindow(QWidget):
"""
Simple About window displaying basic application information.
Args:
parent (QWidget, optional): Parent widget of this window. Defaults to None.
"""
def __init__(self, parent=None):
super().__init__(parent, Qt.WindowType.Window)
self.setWindowTitle(f"About {APP_NAME.upper()}")
self.resize(250, 100)
self.setStyleSheet("""
QVBoxLayout, QWidget {
background-color: #1e1e1e;
}
QLabel {
color: #ffffff;
}
""")
layout = QVBoxLayout()
label = QLabel(f"About {APP_NAME.upper()}", self)
label2 = QLabel("Behavioral Learning & Automated Zoned Events Suite", self)
label3 = QLabel(f"{APP_NAME.upper()} is licensed under the GPL-3.0 licence. For more information, visit https://www.gnu.org/licenses/gpl-3.0.en.html", self)
label4 = QLabel(f"Version v{CURRENT_VERSION}")
layout.addWidget(label)
layout.addWidget(label2)
layout.addWidget(label3)
layout.addWidget(label4)
self.setLayout(layout)
class UserGuideWindow(QWidget):
"""
Simple User Guide window displaying basic information on how to use the software.
Args:
parent (QWidget, optional): Parent widget of this window. Defaults to None.
"""
def __init__(self, parent=None):
super().__init__(parent, Qt.WindowType.Window)
self.setWindowTitle(f"User Guide - {APP_NAME.upper()}")
self.resize(250, 100)
self.setStyleSheet("""
QVBoxLayout, QWidget {
background-color: #1e1e1e;
}
QLabel {
color: #ffffff;
}
""")
layout = QVBoxLayout()
label = QLabel("Hmmm...", self)
label2 = QLabel("Nothing to see here yet.", self)
label3 = QLabel(f"For more information, visit the Git wiki page here.", self)
label3.setTextFormat(Qt.TextFormat.RichText)
label3.setTextInteractionFlags(Qt.TextInteractionFlag.TextBrowserInteraction)
label3.setOpenExternalLinks(True)
layout.addWidget(label)
layout.addWidget(label2)
layout.addWidget(label3)
self.setLayout(layout)
class PoseAnalyzerWorker(QThread):
progress = Signal(str)
finished_data = Signal(dict)
def __init__(self, video_path, obs_info=None, predictor=None):
debug_print()
super().__init__()
self.video_path = video_path
self.obs_info = obs_info
self.predictor = predictor
self.pose_df = pd.DataFrame()
def get_best_infant_match(self, results, w, h, prev_track_id):
debug_print()
if not results[0].boxes or results[0].boxes.id is None:
return None, None, None, None
ids = results[0].boxes.id.int().cpu().tolist()
kpts = results[0].keypoints.xy.cpu().numpy()
confs = results[0].keypoints.conf.cpu().numpy()
best_idx, best_score = -1, -1
for i, k in enumerate(kpts):
vis = np.sum(confs[i] > 0.5)
valid = k[confs[i] > 0.5]
dist = np.linalg.norm(np.mean(valid, axis=0) - [w/2, h/2]) if len(valid) > 0 else 1000
score = (vis * 10) - (dist * 0.1) + (50 if ids[i] == prev_track_id else 0)
if score > best_score:
best_score, best_idx = score, i
if best_idx == -1:
return None, None, None, None
return ids[best_idx], kpts[best_idx], confs[best_idx], best_idx
def _merge_json_observations(self, timeline_events, fps):
"""Restores the grouping and block-pairing logic from the observation files."""
debug_print()
if not self.obs_info:
return
self.progress.emit("Merging JSON Observations...")
json_path, subkey = self.obs_info
# try:
# with open(json_path, 'r') as f:
# full_json = json.load(f)
# # Extract events for the specific subkey (e.g., 'Participant_01')
# raw_obs_events = full_json["observations"][subkey]["events"]
# raw_obs_events.sort(key=lambda x: x[0]) # Sort by timestamp
# # Group frames by label
# obs_groups = {}
# for ev in raw_obs_events:
# time_sec, _, label, special = ev[0], ev[1], ev[2], ev[3]
# frame = int(time_sec * fps)
# if label not in obs_groups:
# obs_groups[label] = []
# obs_groups[label].append(frame)
# # Convert groups of frames into (Start, End) blocks
# for label, frames in obs_groups.items():
# track_name = f"OBS: {label}"
# processed_blocks = []
# # Step by 2 to create start/end pairs
# for i in range(0, len(frames) - 1, 2):
# start_f = frames[i]
# end_f = frames[i+1]
# processed_blocks.append((start_f, end_f, "Moderate", "Manual"))
# # Register the track globally if it's new
# if track_name not in TRACK_NAMES:
# TRACK_NAMES.append(track_name)
# TRACK_COLORS.append(QColor("#AA00FF")) # Purple for Observations
# timeline_events[track_name] = processed_blocks
# except Exception as e:
# print(f"Error parsing JSON Observations: {e}")
try:
with open(json_path, 'r') as f:
full_json = json.load(f)
raw_obs_events = full_json["observations"][subkey]["events"]
raw_obs_events.sort(key=lambda x: x[0])
# NEW LOGIC: Use a dictionary to store frames for specific track names
# track_name -> [list of frames]
obs_groups = {}
for ev in raw_obs_events:
# ev structure: [time_sec, unknown, label, special]
time_sec, label, special = ev[0], ev[2], ev[3]
frame = int(time_sec * fps)
# Determine which tracks this event belongs to
target_tracks = []
if special == "Left":
target_tracks.append(f"OBS: {label} (Left)")
elif special == "Right":
target_tracks.append(f"OBS: {label} (Right)")
elif special == "Both":
target_tracks.append(f"OBS: {label} (Left)")
target_tracks.append(f"OBS: {label} (Right)")
else:
# No special or unrecognized value
target_tracks.append(f"OBS: {label}")
# Add the frame to all applicable tracks
for t_name in target_tracks:
if t_name not in obs_groups:
obs_groups[t_name] = []
obs_groups[t_name].append(frame)
# Convert frame groups into (Start, End) blocks
for track_name, frames in obs_groups.items():
processed_blocks = []
# Step by 2 to create start/end pairs (ensures matching pairs per track)
if "Sync" in track_name and len(frames) == 1:
start_f = frames[0]
end_f = start_f + 1 # Give it a visible width on the timeline
processed_blocks.append((start_f, end_f, "Moderate", "Manual"))
else:
for i in range(0, len(frames) - 1, 2):
start_f = frames[i]
end_f = frames[i+1]
processed_blocks.append((start_f, end_f, "Moderate", "Manual"))
# Register the track in global lists if not already there
if track_name not in TRACK_NAMES:
TRACK_NAMES.append(track_name)
# Using Purple for Observations
TRACK_COLORS.append(QColor("#AA00FF"))
timeline_events[track_name] = processed_blocks
except Exception as e:
print(f"Error parsing JSON Observations: {e}")
def _run_existing_ml_models(self, z_kps, dirs, raw_kpts):
debug_print()
"""
Scans for trained models and generates timeline tracks for each.
"""
ai_events = {}
# 1. Match the pattern from your GeneralPredictor: {Target}_rf.pkl
model_files = glob.glob("*_rf.pkl")
print(f"DEBUG: Found model files: {model_files}")
for model_path in model_files:
try:
# Extract Target (e.g., "Mouthing" from "Mouthing_rf.pkl")
base_name = model_path.split("_rf.pkl")[0]
target = base_name.replace("ml_", "", 1)
track_name = f"AI: {target}"
self.progress.emit(f"Loading AI Observations for {target}...")
# 2. Match the Scaler naming from calculate_and_train:
# {target}_random_forest_scaler.pkl
scaler_path = f"{base_name}_rf_scaler.pkl"
if not os.path.exists(scaler_path):
print(f"DEBUG: Skipping {target}, scaler not found at {scaler_path}")
continue
# Load assets
model = joblib.load(model_path)
scaler = joblib.load(scaler_path)
# 3. Feature extraction (On-the-fly)
all_features = []
# We must set the predictor's target so format_features uses the correct ACTIVITY_MAP
self.predictor.current_target = target
for f_idx in range(len(z_kps)):
feat = self.predictor.format_features(z_kps[f_idx], dirs[f_idx], raw_kpts[f_idx])
all_features.append(feat)
# 4. Inference
X = np.array(all_features)
X_scaled = scaler.transform(X)
predictions = model.predict(X_scaled)
# 5. Convert binary 0/1 to blocks
processed_blocks = []
start_f = None
for f_idx, val in enumerate(predictions):
if val == 1 and start_f is None:
start_f = f_idx
elif val == 0 and start_f is not None:
# [start, end, severity, direction]
processed_blocks.append((start_f, f_idx - 1, "Large", "AI"))
start_f = None
if start_f is not None:
processed_blocks.append((start_f, len(predictions)-1, "Large", "AI"))
# 6. Global Registration
if track_name not in TRACK_NAMES:
TRACK_NAMES.append(track_name)
# Ensure TRACK_COLORS has an entry for this new track
TRACK_COLORS.append(QColor("#00FF00"))
ai_events[track_name] = processed_blocks
print(f"DEBUG: Successfully generated {len(processed_blocks)} blocks for {track_name}")
except Exception as e:
print(f"Inference Error for {model_path}: {e}")
return ai_events
def classify_delta(self, z):
# debug_print()
z_abs = abs(z)
if z_abs < 1: return "Rest"
elif z_abs < 2: return "Small"
elif z_abs < 3: return "Moderate"
else: return "Large"
def _save_pose_cache(self, path, data):
"""
Saves the raw YOLO keypoints and confidence scores to a CSV.
Each row represents one frame, flattened from (17, 3) to (51,).
"""
try:
with open(path, 'w', newline='') as f:
writer = csv.writer(f)
# Create the descriptive header
header = []
for joint in JOINT_NAMES:
# Replace spaces with underscores for better compatibility with other tools
header.extend([f"{joint}_x", f"{joint}_y", f"{joint}_conf"])
writer.writerow(header)
# Write the frame data
for frame_data in data:
# frame_data is (17, 3), flatten to (51,)
writer.writerow(frame_data.flatten())
print(f"DEBUG: Pose cache saved with joint headers at {path}")
except Exception as e:
print(f"ERROR: Could not save pose cache: {e}")
def run(self):
debug_print()
# --- PHASE 1: VIDEO SETUP & POSE EXTRACTION ---
cap = cv2.VideoCapture(self.video_path)
fps = cap.get(cv2.CAP_PROP_FPS) or 30.0
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
raw_kps_per_frame = []
csv_storage_data = []
valid_mask = []
pose_cache = self.video_path.rsplit('.', 1)[0] + "_pose_raw.csv"
if os.path.exists(pose_cache):
self.progress.emit("Loading cached kinematic data...")
with open(pose_cache, 'r') as f:
reader = csv.reader(f)
next(reader)
for row in reader:
full_data = np.array([float(x) for x in row]).reshape(17, 3)
kp = full_data[:, :2]
raw_kps_per_frame.append(kp)
csv_storage_data.append(full_data)
valid_mask.append(np.any(kp))
else:
self.progress.emit("Detecting poses with YOLO...")
model = YOLO("yolov8n-pose.pt")
prev_track_id = None
for i in range(total_frames):
ret, frame = cap.read()
if not ret: break
results = model.track(frame, persist=True, verbose=False)
track_id, kp, confs, _ = self.get_best_infant_match(results, width, height, prev_track_id)
if kp is not None:
prev_track_id = track_id
raw_kps_per_frame.append(kp)
csv_storage_data.append(np.column_stack((kp, confs)))
valid_mask.append(True)
else:
raw_kps_per_frame.append(np.zeros((17, 2)))
csv_storage_data.append(np.zeros((17, 3)))
valid_mask.append(False)
if i % 50 == 0: self.progress.emit(f"YOLO: {int((i/total_frames)*100)}%")
self._save_pose_cache(pose_cache, csv_storage_data)
cap.release()
actual_len = len(raw_kps_per_frame)
flattened_rows = []
for frame_array in csv_storage_data:
# frame_array is (17, 3) -> flatten to (51,)
flattened_rows.append(frame_array.flatten())
columns = []
for name in JOINT_NAMES:
columns.extend([f"{name}_x", f"{name}_y", f"{name}_conf"])
# Store this so the Inspector can access it instantly in memory
self.pose_df = pd.DataFrame(flattened_rows, columns=columns)
# --- PHASE 2: KINEMATICS & Z-SCORES ---
self.progress.emit("Calculating Kinematics...")
analysis_kpts = []
for kp in raw_kps_per_frame:
pelvis = (kp[11] + kp[12]) / 2
analysis_kpts.append(kp - pelvis)
valid_data = [analysis_kpts[i] for i, v in enumerate(valid_mask) if v]
if valid_data:
stacked = np.stack(valid_data)
baseline_mean = np.mean(stacked, axis=0)
baseline_std = np.std(np.linalg.norm(stacked - baseline_mean, axis=2), axis=0) + 1e-6
else:
baseline_mean, baseline_std = np.zeros((17, 2)), np.ones(17)
np_raw_kps = np.array(raw_kps_per_frame)
np_z_kps = np.array([np.linalg.norm(kp - baseline_mean, axis=1) / baseline_std for kp in analysis_kpts])
# Calculate directions (Assume you have a method for this or use a dummy for now)
# Using placeholder empty strings to prevent errors in track generation
np_dirs = np.full((actual_len, 17), "", dtype=object)
# --- PHASE 3: TIMELINE GENERATION ---
# Initialize dictionary with ALL global track names to prevent KeyErrors
timeline_events = {name: [] for name in TRACK_NAMES}
# 1. Kinematic Events (The joint tracks)
for j_idx, joint_name in enumerate(JOINT_NAMES):
current_block = None
for f_idx in range(actual_len):
severity = self.classify_delta(np_z_kps[f_idx, j_idx])
if severity != "Rest":
if current_block and current_block[2] == severity:
current_block[1] = f_idx
else:
current_block = [f_idx, f_idx, severity, ""]
timeline_events[joint_name].append(current_block)
else:
current_block = None
# 2. JSON Observations
self._merge_json_observations(timeline_events, fps)
# 3. AI Inferred Events
ai_events = self._run_existing_ml_models(np_z_kps, np_dirs, np_raw_kps)
timeline_events.update(ai_events)
# --- PHASE 4: EMIT ---
data = {
"video_path": self.video_path,
"fps": fps,
"total_frames": actual_len,
"width": width, "height": height,
"events": timeline_events,
"raw_kps": np_raw_kps,
"z_kps": np_z_kps,
"directions": np_dirs,
"baseline_kp_mean": baseline_mean
}
self.progress.emit("Analysis Complete!")
self.finished_data.emit(data)
# ==========================================
# TIMELINE WIDGET
# ==========================================
class TimelineWidget(QWidget):
seek_requested = Signal(int)
visibility_changed = Signal(set)
track_selected = Signal(str)
def __init__(self):
debug_print()
super().__init__()
self.data = None
self.current_frame = 0
self.zoom_factor = 1.0 # Pixels per frame
self.label_width = 160 # Fixed gutter for track names
self.track_height = 25
self.ruler_height = 20
self.scrollbar_buffer = 2 # Extra space for the horizontal scrollbar
self.hidden_tracks = set()
self.sync_offset = 0.0
self.sync_fps = 30.0
# Calculate total required height
self.total_content_height = (NUM_TRACKS * self.track_height) + self.ruler_height
self.setMinimumHeight(self.total_content_height + self.scrollbar_buffer)
def set_sync_params(self, offset_seconds, fps=None):
"""
Updates the temporal shift parameters and refreshes the UI.
"""
debug_print()
self.sync_offset = float(offset_seconds)
# Only update FPS if a valid value is provided,
# otherwise keep the existing data/video FPS
if fps and fps > 0:
self.sync_fps = float(fps)
elif self.data and "fps" in self.data:
self.sync_fps = float(self.data["fps"])
print(f"DEBUG: Timeline Sync Set - Offset: {self.sync_offset}s, FPS: {self.sync_fps}")
# Trigger paintEvent to redraw the blocks in their new shifted positions
self.update()
def set_zoom(self, factor):
debug_print()
if not self.data: return
# Calculate MIN zoom: The zoom required to make the video fit the width exactly
# (Available Width - Sidebar) / Total Frames
available_w = self.parent().width() - self.label_width if self.parent() else 800
min_zoom = available_w / self.data["total_frames"]
# Clamp: Don't zoom out past the video end, don't zoom in to infinity
self.zoom_factor = max(min_zoom, min(factor, 50.0))
self.update_geometry()
def get_all_binary_labels(self, offset_seconds=0.0, fps=30.0):
"""
Extracts binary labels for ALL tracks in self.data["events"].
Returns a dict: {'OBS: Mouthing': [0,1,0...], 'OBS: Kicking': [0,0,1...]}
"""
debug_print()
all_labels = {}
if not self.data or "events" not in self.data:
return all_labels
total_frames = self.data.get("total_frames", 0)
if total_frames == 0:
return all_labels
frame_shift = int(offset_seconds * fps)
for track_name in self.data["events"]:
sequence = np.zeros(total_frames, dtype=int)
for event in self.data["events"][track_name]:
start_f = int(event[0]) - frame_shift
end_f = int(event[1]) - frame_shift
# Clamp values
start_idx = max(0, min(start_f, total_frames - 1))
end_idx = max(0, min(end_f, total_frames))
if start_idx < end_idx:
sequence[start_idx:end_idx] = 1
all_labels[track_name] = sequence.tolist() # Convert to list for easier storage
return all_labels
def update_geometry(self):
debug_print()
if self.data:
# Width is sidebar + (frames * zoom)
total_w = self.label_width + int(self.data["total_frames"] * self.zoom_factor)
self.setFixedWidth(total_w)
self.update()
def wheelEvent(self, event):
debug_print()
if event.modifiers() == Qt.ControlModifier:
delta = event.angleDelta().y()
# Zoom by 10% per notch
zoom_change = 1.1 if delta > 0 else 0.9
self.set_zoom(self.zoom_factor * zoom_change)
else:
# Let the scroll area handle normal vertical scrolling
super().wheelEvent(event)
# --- NEW: CTRL + Plus / Minus / Zero ---
def keyPressEvent(self, event):
debug_print()
if event.modifiers() == Qt.ControlModifier:
if event.key() == Qt.Key_Plus or event.key() == Qt.Key_Equal:
self.set_zoom(self.zoom_factor * 1.2)
elif event.key() == Qt.Key_Minus:
self.set_zoom(self.zoom_factor * 0.8)
elif event.key() == Qt.Key_0:
self.set_zoom(1.0) # Reset zoom
else:
super().keyPressEvent(event)
def set_data(self, data):
debug_print()
self.data = data
self.total_content_height = (len(TRACK_NAMES) * self.track_height) + self.ruler_height
self.setMinimumHeight(self.total_content_height + self.scrollbar_buffer)
self.update_geometry()
def set_playhead(self, frame):
debug_print()
old_x = self.label_width + (self.current_frame * self.zoom_factor)
self.current_frame = frame
new_x = self.label_width + (self.current_frame * self.zoom_factor)
self.ensure_playhead_visible()
self.update(int(old_x - 5), 0, 10, self.height())
self.update(int(new_x - 5), 0, 10, self.height())
def ensure_playhead_visible(self):
debug_print()
"""Auto-scrolls the scroll area if the playhead leaves the viewport."""
# Find the QScrollArea parent
scroll_area = self.parent().parent()
if not isinstance(scroll_area, QScrollArea): return
scrollbar = scroll_area.horizontalScrollBar()
view_width = scroll_area.viewport().width()
# Playhead position in pixels
px = self.label_width + int(self.current_frame * self.zoom_factor)
# Current scroll position
scroll_x = scrollbar.value()
# If playhead is beyond the right edge of visible area
if px > (scroll_x + view_width):
# Shift scroll so playhead is at the left (plus sidebar)
scrollbar.setValue(px - self.label_width)
# If playhead is behind the left edge (e.g. user seeked backwards)
elif px < (scroll_x + self.label_width):
scrollbar.setValue(px - self.label_width)
def mousePressEvent(self, event):
debug_print()
if not self.data or event.button() != Qt.LeftButton:
return
pos_x = event.position().x()
pos_y = event.position().y()
scroll_area = self.parent().parent()
scroll_x = scroll_area.horizontalScrollBar().value()
# 1. CALCULATE FRAME
relative_x = pos_x - self.label_width
frame = int(relative_x / self.zoom_factor)
frame = max(0, min(frame, self.data["total_frames"] - 1))
# 2. IF CLICKED SIDEBAR: Toggle Visibility (No Scrubbing)
if pos_x < scroll_x + self.label_width:
relative_y = pos_y - self.ruler_height
track_idx = int(relative_y // self.track_height)
if 0 <= track_idx < len(TRACK_NAMES):
name = TRACK_NAMES[track_idx]
if name in self.hidden_tracks: self.hidden_tracks.remove(name)
else: self.hidden_tracks.add(name)
self.visibility_changed.emit(self.hidden_tracks)
self.update()
return # Exit early; don't set is_scrubbing
# 3. IF CLICKED RULER OR DATA AREA: Start Scrubbing
self.is_scrubbing = True
self.seek_requested.emit(frame)
# Handle track selection if in the data area
if pos_y >= self.ruler_height:
track_idx = int((pos_y - self.ruler_height) // self.track_height)
if 0 <= track_idx < len(TRACK_NAMES):
self.track_selected.emit(TRACK_NAMES[track_idx])
self.selected_track_idx = track_idx
self.update()
else:
# Clicked ruler
self.selected_track_idx = -1
self.track_selected.emit("")
self.update()
def mouseMoveEvent(self, event):
debug_print()
# This only fires while moving if a button is held down by default
if self.is_scrubbing:
self.update_frame_from_mouse(event.position().x())
def mouseReleaseEvent(self, event):
debug_print()
if event.button() == Qt.LeftButton:
self.is_scrubbing = False
def update_frame_from_mouse(self, x_pos):
"""Helper to calculate frame and emit the seek signal."""
debug_print()
relative_x = x_pos - self.label_width
frame = int(relative_x / self.zoom_factor)
frame = max(0, min(frame, self.data["total_frames"] - 1))
# We emit seek_requested so the Video Player and Premiere class sync up
self.seek_requested.emit(frame)
def paintEvent(self, event):
debug_print()
if not self.data: return
dirty_rect = event.rect()
painter = QPainter(self)
# 1. Determine current scroll position to keep labels sticky
scroll_area = self.parent().parent()
scroll_x = 0
if isinstance(scroll_area, QScrollArea):
scroll_x = scroll_area.horizontalScrollBar().value()
w, h = self.width(), self.height()
total_f = self.data["total_frames"]
fps = self.data.get("fps", 30)
offset_y = 20
# 2. DRAW DATA AREA (Events and Playhead)
# --- 2. DRAW DATA AREA (Muted Patterns + Events + Playhead) ---
sync_off = getattr(self, "sync_offset", 0.0)
sync_fps = getattr(self, "sync_fps", fps)
frame_shift = int(sync_off * sync_fps)
for i, name in enumerate(TRACK_NAMES):
y = offset_y + (i * self.track_height)
is_hidden = name in self.hidden_tracks
if y + self.track_height < dirty_rect.top() or y > dirty_rect.bottom():
continue
# A. Draw Muted/Disabled Background Pattern
if is_hidden:
# Calculate the visible rectangle for this track to the right of the sidebar
mute_rect = QRectF(self.label_width, y, w - self.label_width, self.track_height)
# Fill with a dark "disabled" base
painter.fillRect(mute_rect, QColor(40, 40, 40))
# Add the Cross-Hatch Pattern
pattern_brush = QBrush(QColor(60, 60, 60, 100), Qt.DiagCrossPattern)
painter.fillRect(mute_rect, pattern_brush)
# B. Draw Event Blocks
base_color = TRACK_COLORS[i]
for start_f, end_f, severity, direction in self.data["events"][name]:
# x_start = self.label_width + (start_f * self.zoom_factor)
# x_end = self.label_width + (end_f * self.zoom_factor)
if "AI:" in name:
# AI predictions are already calculated in video-time, NO SHIFT
shifted_start, shifted_end = start_f, end_f
else:
shifted_start = start_f - frame_shift
shifted_end = end_f - frame_shift
x_start = self.label_width + (shifted_start * self.zoom_factor)
x_end = self.label_width + (shifted_end * self.zoom_factor)
# Performance optimization: skip drawing if off-screen
if x_end < scroll_x or x_start > scroll_x + w:
continue
if x_end < dirty_rect.left() or x_start > dirty_rect.right():
continue
# If hidden, make the event block very faint/transparent
if is_hidden:
color = QColor(120, 120, 120, 40) # Muted Grey
else:
alpha = 80 if severity == "Small" else 160 if severity == "Moderate" else 255
color = QColor(base_color)
color.setAlpha(alpha)
painter.fillRect(QRectF(x_start, y + 2, max(1, x_end - x_start), self.track_height - 4), color)
# Draw Playhead
playhead_x = self.label_width + (self.current_frame * self.zoom_factor)
painter.setPen(QPen(QColor(255, 0, 0), 2))
painter.drawLine(playhead_x, 0, playhead_x, h)
# 3. DRAW STICKY SIDEBAR (Pinned to the left edge of the viewport)
# We draw this AFTER the data so it covers the blocks as they scroll past
sidebar_rect = QRect(scroll_x, 0, self.label_width, h)
painter.fillRect(sidebar_rect, QColor(30, 30, 30)) # Solid background
# Ruler segment for the sidebar area
painter.fillRect(scroll_x, 0, self.label_width, offset_y, QColor(45, 45, 45))
for i, name in enumerate(TRACK_NAMES):
y = offset_y + (i * self.track_height)
is_hidden = name in self.hidden_tracks
# Grid Line
painter.setPen(QColor(60, 60, 60))
painter.drawLine(scroll_x, y, scroll_x + w, y)
# Sticky Label Text
if is_hidden:
# Very dark grey to show it's "OFF"
text_color = QColor(70, 70, 70)
else:
# Bright white/grey to show it's "ON"
text_color = QColor(220, 220, 220)
painter.setPen(text_color)
painter.setFont(QFont("Arial", 8, QFont.Bold))
painter.drawText(scroll_x + 10, y + 17, name)
# 4. DRAW TIME RULER TICKS (Right of the sticky sidebar)
target_spacing_px = 120
# Available units in frames: 1, 5, 15, 30 (1s), 150 (5s), 300 (10s), 1800 (1min)
possible_units = [1, 5, 15, 30, 150, 300, 900, 1800]
# Find the smallest unit that results in at least target_spacing_px
tick_interval = possible_units[-1]
for unit in possible_units:
if (unit * self.zoom_factor) >= target_spacing_px:
tick_interval = unit
break
# 2. DRAW BACKGROUNDS
painter.fillRect(0, 0, w, 20, QColor(45, 45, 45)) # Ruler Bar
# 3. DRAW TICKS AND TIME LABELS
painter.setPen(QColor(180, 180, 180))
painter.setFont(QFont("Segoe UI", 7))
# Sub-ticks (draw 5 small lines for every 1 major interval)
sub_interval = max(1, tick_interval // 5)
# Start loop from 0 to total frames
for f in range(0, total_f + 1, sub_interval):
x = self.label_width + int(f * self.zoom_factor)
# Optimization: Don't draw if off-screen
if x < scroll_x: continue
if x > scroll_x + w: break
if f % tick_interval == 0:
# Major Tick
painter.drawLine(x, 10, x, 20)
# Format Label: MM:SS or SS:FF
total_seconds = f / fps
minutes = int(total_seconds // 60)
seconds = int(total_seconds % 60)
frames = int(f % fps)
if tick_interval < fps:
time_str = f"{seconds:02d}:{frames:02d}f"
elif minutes > 0:
time_str = f"{minutes:02d}m:{seconds:02d}s"
else:
time_str = f"{seconds}s"
painter.drawText(x + 4, 12, time_str)
else:
# Minor Tick
painter.drawLine(x, 16, x, 20)
def get_ai_extractions(self):
"""
Processes timeline data for AI tracks and specific OBS sync events.
"""
debug_print()
fps = self.data.get("fps", 30.0)
extraction_data = {
"metadata": {
"fps": fps,
"total_frames": self.data.get("total_frames", 0),
"track_summaries": {}
},
"obs": {}, # New top-level key for specific OBS events
"ai_tracks": {}
}
if not self.data or "events" not in self.data:
return extraction_data
# 1. Extract the OBS: Time Sync event specifically
sync_key = "OBS: Time Sync"
if sync_key in self.data["events"]:
sync_blocks = self.data["events"][sync_key]
# Convert blocks to a list of dicts for the JSON
extraction_data["obs"][sync_key] = [
{
"start_frame": b[0],
"end_frame": b[1],
"start_time_sec": round(b[0] / fps, 3),
"end_time_sec": round(b[1] / fps, 3)
} for b in sync_blocks
]
# 2. Process AI Tracks
for track_name, blocks in self.data["events"].items():
if track_name.startswith("AI:"):
track_results = []
track_total = 0
track_long = 0
for block in blocks:
start_f, end_f = block[0], block[1]
severity = block[2] if len(block) > 2 else "Normal"
start_sec = round(start_f / fps, 3)
end_sec = round(end_f / fps, 3)
duration = round(end_sec - start_sec, 3)
track_total += 1
if duration > 2.0:
track_long += 1
track_results.append({
"start_frame": int(start_f),
"end_frame": int(end_f),
"start_time_sec": start_sec,
"end_time_sec": end_sec,
"duration_sec": duration,
"severity": severity
})
extraction_data["ai_tracks"][track_name] = track_results
extraction_data["metadata"]["track_summaries"][track_name] = {
"event_count": track_total,
"long_events_over_2s": track_long,
"total_duration_sec": round(sum(r["duration_sec"] for r in track_results), 3)
}
return extraction_data
class SkeletonOverlay(QWidget):
def __init__(self, parent=None):
debug_print()
super().__init__(parent)
self.setAttribute(Qt.WA_TransparentForMouseEvents) # Clicks pass through to video
self.data = None
self.current_frame = 0
self.hidden_tracks = set()
# Use your saved SKELETON_CONNECTIONS logic
self.connections = [
(5, 7), (7, 9), (6, 8), (8, 10), (5, 6), (5, 11),
(6, 12), (11, 12), (11, 13), (13, 15), (12, 14), (14, 16)
]
self.KP_MAP = {
"nose": 0, "LE": 1, "RE": 2, "Lear": 3, "Rear": 4,
"Lshoulder": 5, "Rshoulder": 6, "Lelbow": 7, "Relbow": 8,
"Lwrist": 9, "Rwrist": 10, "Lhip": 11, "Rhip": 12,
"Lknee": 13, "Rknee": 14, "Lankle": 15, "Rankle": 16
}
self.CONNECTIONS = [
("nose", "LE"), ("nose", "RE"), ("LE", "Lear"), ("RE", "Rear"),
("Lshoulder", "Rshoulder"), ("Lshoulder", "Lelbow"), ("Lelbow", "Lwrist"),
("Rshoulder", "Relbow"), ("Relbow", "Rwrist"), ("Lshoulder", "Lhip"),
("Rshoulder", "Rhip"), ("Lhip", "Rhip"), ("Lhip", "Lknee"),
("Lknee", "Lankle"), ("Rhip", "Rknee"), ("Rknee", "Rankle")
]
def set_frame(self, frame_idx):
debug_print()
self.current_frame = frame_idx
self.update()
def set_hidden_tracks(self, hidden_set):
debug_print()
self.hidden_tracks = hidden_set
self.update()
def set_data(self, data):
debug_print()
self.data = data
self.update()
def paintEvent(self, event):
debug_print()
if not self.data or 'raw_kps' not in self.data:
return
painter = QPainter(self)
painter.setRenderHint(QPainter.Antialiasing)
v_w, v_h = self.data['width'], self.data['height']
w, h = self.width(), self.height()
scale_x, scale_y = w / v_w, h / v_h
current_f = self.current_frame
kp_live = self.data['raw_kps'][current_f]
# --- 1. MODIFIED TRACK STATUS (Respects Visibility) ---
def get_track_status(track_name):
# If the user greyed out this track in the timeline, act as if it's inactive
if track_name in self.hidden_tracks:
return None
if track_name not in self.data['events']:
return None
for start, end, severity, direction in self.data['events'][track_name]:
if start <= current_f <= end:
idx = TRACK_NAMES.index(track_name)
color = QColor(TRACK_COLORS[idx])
alpha = 80 if severity == "Small" else 160 if severity == "Moderate" else 255
color.setAlpha(alpha)
return color
return None
ANGLE_SEGMENTS = {
"L_sh": [("Lhip", "Lshoulder"), ("Lshoulder", "Lelbow")],
"R_sh": [("Rhip", "Rshoulder"), ("Rshoulder", "Relbow")],
"L_el": [("Lshoulder", "Lelbow"), ("Lelbow", "Lwrist")],
"R_el": [("Rshoulder", "Relbow"), ("Relbow", "Rwrist")],
"L_leg": [("Lhip", "Lknee"), ("Lknee", "Lankle")],
"R_leg": [("Rhip", "Rknee"), ("Rknee", "Rankle")]
}
# --- 2. DRAW BASELINE (Only if not hidden) ---
if "Baseline" not in self.hidden_tracks:
idx_l_hip, idx_r_hip = self.KP_MAP["Lhip"], self.KP_MAP["Rhip"]
pelvis_live = (kp_live[idx_l_hip] + kp_live[idx_r_hip]) / 2
kp_baseline = self.data['baseline_kp_mean'] + pelvis_live
painter.setPen(QPen(QColor(200, 200, 200, 200), 2, Qt.DashLine))
for s_name, e_name in self.CONNECTIONS:
p1 = QPointF(kp_baseline[self.KP_MAP[s_name]][0] * scale_x, kp_baseline[self.KP_MAP[s_name]][1] * scale_y)
p2 = QPointF(kp_baseline[self.KP_MAP[e_name]][0] * scale_x, kp_baseline[self.KP_MAP[e_name]][1] * scale_y)
painter.drawLine(p1, p2)
# --- 3. DRAW LIVE SKELETON (Only if not hidden) ---
# CONNECTIONS
for s_name, e_name in self.CONNECTIONS:
active_color = None
for angle_track, segments in ANGLE_SEGMENTS.items():
if (s_name, e_name) in segments or (e_name, s_name) in segments:
active_color = get_track_status(angle_track)
if active_color: break
p1 = QPointF(kp_live[self.KP_MAP[s_name]][0] * scale_x, kp_live[self.KP_MAP[s_name]][1] * scale_y)
p2 = QPointF(kp_live[self.KP_MAP[e_name]][0] * scale_x, kp_live[self.KP_MAP[e_name]][1] * scale_y)
if active_color:
# Active events ALWAYS draw
painter.setPen(QPen(active_color, 8, Qt.SolidLine, Qt.RoundCap))
painter.drawLine(p1, p2)
elif "Live Skeleton" not in self.hidden_tracks:
# Black lines ONLY draw if Live Skeleton is ON
painter.setPen(QPen(Qt.black, 4, Qt.SolidLine, Qt.RoundCap))
painter.drawLine(p1, p2)
# DOTS
ANGLE_VERTEX_MAP = {
"L_sh": "Lshoulder", "R_sh": "Rshoulder",
"L_el": "Lelbow", "R_el": "Relbow",
"L_leg": "Lknee", "R_leg": "Rknee"
}
for kp_name, kp_idx in self.KP_MAP.items():
pt = QPointF(kp_live[kp_idx][0] * scale_x, kp_live[kp_idx][1] * scale_y)
# Check for Point Event (Skip if hidden via get_track_status)
point_color = get_track_status(kp_name)
if point_color:
painter.setBrush(point_color)
painter.setPen(QPen(Qt.white, 0.7))
painter.drawEllipse(pt, 5, 5)
continue
# Check for Angle Event
angle_color = None
for angle_track, vertex_name in ANGLE_VERTEX_MAP.items():
if kp_name == vertex_name:
angle_color = get_track_status(angle_track)
if angle_color: break
if angle_color:
painter.setBrush(angle_color)
painter.setPen(Qt.NoPen)
painter.drawEllipse(pt, 4, 4)
elif "Live Skeleton" not in self.hidden_tracks:
painter.setBrush(Qt.black)
painter.setPen(Qt.NoPen)
painter.drawEllipse(pt, 4, 4)
class VideoView(QGraphicsView):
resized = Signal()
def __init__(self, scene, parent=None):
debug_print()
super().__init__(scene, parent)
self.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
self.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
self.setFrameStyle(0)
self.setStyleSheet("background: black; border: none;")
self.setAlignment(Qt.AlignCenter)
def resizeEvent(self, event):
debug_print()
super().resizeEvent(event)
self.resized.emit()
# ==========================================
# MAIN PREMIERE WINDOW
# ==========================================
class PremiereWindow(QMainWindow):
def __init__(self):
debug_print()
super().__init__()
self.setWindowTitle("Pose Analysis Timeline")
self.resize(1200, 900)
self.about = None
self.help = None
self.platform_suffix = "-" + PLATFORM_NAME
self.updater = UpdateManager(
main_window=self,
api_url=API_URL,
api_url_sec=API_URL_SECONDARY,
current_version=CURRENT_VERSION,
platform_name=PLATFORM_NAME,
platform_suffix=self.platform_suffix,
app_name=APP_NAME
)
# self.setStyleSheet("background-color: #1e1e1e; color: #ffffff;")
self.setStyleSheet("""
QMainWindow, QWidget#centralWidget {
background-color: #1e1e1e;
}
QLabel, QStatusBar, QMenuBar {
color: #ffffff;
}
/* Target the Timeline specifically */
TimelineWidget {
background-color: #1e1e1e;
border: 1px solid #333333;
}
/* Button styling with Grey borders */
QDialog, QMessageBox, QFileDialog {
background-color: #2b2b2b;
}
QDialog QLabel, QMessageBox QLabel {
color: #ffffff;
}
QPushButton {
background-color: #2b2b2b;
color: #ffffff;
border: 1px solid #555555; /* Subtle Grey border */
border-radius: 3px;
padding: 4px;
}
QPushButton:hover {
background-color: #3d3d3d;
border-color: #888888; /* Brightens border on hover */
}
QPushButton:pressed {
background-color: #111111;
}
QPushButton:disabled {
border-color: #333333;
color: #444444;
}
/* Splitter/Divider styling */
QSplitter::handle {
background-color: #333333; /* Dark grey dividers */
}
QSplitter::handle:horizontal {
width: 2px;
}
QSplitter::handle:vertical {
height: 2px;
}
/* ScrollArea styling to keep it dark */
QScrollArea, QScrollArea > QWidget > QWidget {
background-color: #1e1e1e;
border: none;
}
""")
self.predictor = GeneralPredictor()
self.file_path = None
self.obs_file = None
self.selected_obs_subkey = None
self.current_video_offset = 0.0
# Core Layout
main_splitter = QSplitter(Qt.Vertical)
top_splitter = QSplitter(Qt.Horizontal)
# --- Top Left: Video Player ---
video_container = QWidget()
video_layout = QVBoxLayout(video_container)
video_layout.setContentsMargins(0, 0, 0, 0)
self.scene = QGraphicsScene()
# Use our new subclass instead of standard QGraphicsView
self.view = VideoView(self.scene)
self.view.resized.connect(self.update_video_geometry)
# Video item (NOT native)
self.video_item = QGraphicsVideoItem()
self.scene.addItem(self.video_item)
# Overlay widget (normal QWidget)
self.skeleton_overlay = SkeletonOverlay(self.view.viewport())
self.skeleton_overlay.setAttribute(Qt.WA_TransparentForMouseEvents)
self.skeleton_overlay.setAttribute(Qt.WA_TranslucentBackground)
self.skeleton_overlay.show()
# Media player
self.player = QMediaPlayer()
self.audio_output = QAudioOutput()
self.player.setAudioOutput(self.audio_output)
self.player.setVideoOutput(self.video_item)
video_layout.addWidget(self.view)
# --- Control Bar Container (Vertical Stack) ---
controls_container = QWidget()
stacked_controls = QVBoxLayout(controls_container)
stacked_controls.setSpacing(5) # Tight spacing between rows
# --- ROW 1: ML & Training Controls ---
ml_row = QHBoxLayout()
ml_row.addStretch()
ml_row.addWidget(QLabel("ML Model:"))
self.ml_dropdown = QComboBox()
self.ml_dropdown.addItems(["Random Forest", "LSTM", "XGBoost", "SVM", "1D-CNN"])
ml_row.addWidget(self.ml_dropdown)
ml_row.addWidget(QLabel("Target:"))
self.target_dropdown = QComboBox()
self.target_dropdown.addItems(["Mouthing", "Head Movement", "Kick (Left)", "Kick (Right)", "Reach (Left)", "Reach (Right)"])
self.target_dropdown.currentTextChanged.connect(self.update_predictor_target)
ml_row.addWidget(self.target_dropdown)
self.btn_add_to_pool = QPushButton("Add to Pool")
self.btn_add_to_pool.clicked.connect(self.add_current_to_ml_pool)
self.btn_add_to_pool.setFixedWidth(120)
ml_row.addWidget(self.btn_add_to_pool)
self.btn_train_final = QPushButton("Train Global Model")
self.btn_train_final.setStyleSheet("background-color: #2e7d32; font-weight: bold;")
self.btn_train_final.clicked.connect(self.run_final_training)
ml_row.addWidget(self.btn_train_final)
self.lbl_pool_status = QLabel("Pool: 0 Participants")
self.lbl_pool_status.setStyleSheet("color: #00FF00; font-weight: bold; margin-left: 10px;")
self.lbl_pool_status.setFixedWidth(160)
ml_row.addWidget(self.lbl_pool_status)
self.btn_clear_pool = QPushButton("Clear Pool")
self.btn_clear_pool.setFixedWidth(100)
self.btn_clear_pool.setStyleSheet("color: #ff5555; border: 1px solid #ff5555;")
self.btn_clear_pool.clicked.connect(self.clear_ml_pool)
ml_row.addWidget(self.btn_clear_pool)
self.btn_extract_ai = QPushButton("Extract AI Data")
self.btn_extract_ai.clicked.connect(self.extract_ai_to_json)
ml_row.addWidget(self.btn_extract_ai)
ml_row.addStretch()
# --- ROW 2: Playback & Transport ---
playback_row = QHBoxLayout()
playback_row.addStretch()
# Transport Buttons
self.btn_start = QPushButton("|<")
self.btn_prev = QPushButton("<")
self.btn_play = QPushButton("Play")
self.btn_next = QPushButton(">")
self.btn_end = QPushButton(">|")
self.transport_btns = [self.btn_start, self.btn_prev, self.btn_play, self.btn_next, self.btn_end]
for btn in self.transport_btns:
btn.setEnabled(False)
btn.setFixedWidth(50)
playback_row.addWidget(btn)
self.btn_mute = QPushButton("Vol")
self.btn_mute.setFixedWidth(40)
self.btn_mute.setCheckable(True)
self.btn_mute.clicked.connect(self.toggle_mute)
self.sld_volume = QSlider(Qt.Horizontal)
self.sld_volume.setRange(0, 100)
self.sld_volume.setValue(100) # Default volume
self.sld_volume.setFixedWidth(100)
self.sld_volume.valueChanged.connect(self.update_volume)
# Initialize volume
self.audio_output.setVolume(0.7)
playback_row.addWidget(self.btn_mute)
playback_row.addWidget(self.sld_volume)
# Counters
counter_style = "font-family: 'Consolas'; font-size: 10pt; margin-left: 5px; color: #00FF00;"
self.lbl_time_counter = QLabel("Time: 00:00 / 00:00")
self.lbl_frame_counter = QLabel("Frame: 0 / 0")
self.lbl_time_counter.setFixedWidth(180)
self.lbl_frame_counter.setFixedWidth(180)
self.lbl_time_counter.setStyleSheet(counter_style)
self.lbl_frame_counter.setStyleSheet(counter_style)
playback_row.addWidget(self.lbl_time_counter)
playback_row.addWidget(self.lbl_frame_counter)
playback_row.addStretch()
# --- Add Rows to Stack ---
stacked_controls.addLayout(ml_row)
stacked_controls.addLayout(playback_row)
# Add the whole stacked container to the main video layout
video_layout.addWidget(controls_container)
# --- Button Connections ---
self.btn_play.clicked.connect(self.toggle_playback)
# Use lambda to pass the target frame to your existing seek_video method
self.btn_start.clicked.connect(lambda: self.seek_video(0))
self.btn_end.clicked.connect(lambda: self.seek_video(self.data['total_frames'] - 1))
self.btn_prev.clicked.connect(lambda: self.step_frame(-1))
self.btn_next.clicked.connect(lambda: self.step_frame(1))
# --- Top Right: Media Info & Loader ---
info_container = QWidget()
info_layout = QVBoxLayout(info_container)
# NEW: Wrap the info_label in a Scroll Area
self.inspector_scroll = QScrollArea()
self.inspector_scroll.setWidgetResizable(True)
self.inspector_scroll.setStyleSheet("border: none; background-color: transparent;")
# Create the label as the scroll area's content
self.info_label = QTextEdit()
self.info_label.setText("No video loaded.\nClick 'File' > 'Load Video' to begin.")
self.info_label.setAlignment(Qt.AlignTop | Qt.AlignLeft)
self.info_label.setWordWrapMode(QTextOption.WordWrap)
self.info_label.setReadOnly(True)
# self.info_label.setWordWrap(True) # Ensure long text wraps instead o
# f stretching horizontally
self.info_label.setStyleSheet("padding: 5px; font-family: 'Segoe UI', Arial; color: #ffffff;")
self.inspector_scroll.setWidget(self.info_label)
# Add the scroll area to the layout instead of the naked label
info_layout.addWidget(self.inspector_scroll)
top_splitter.addWidget(video_container)
top_splitter.addWidget(info_container)
top_splitter.setSizes([800, 400])
# --- Bottom: Timeline in a Scroll Area ---
self.timeline = TimelineWidget()
self.timeline.seek_requested.connect(self.seek_video)
self.timeline.visibility_changed.connect(self.skeleton_overlay.set_hidden_tracks)
self.timeline.track_selected.connect(self.on_track_selected)
scroll_area = QScrollArea()
scroll_area.setWidgetResizable(True)
scroll_area.setWidget(self.timeline)
main_splitter.addWidget(top_splitter)
main_splitter.addWidget(scroll_area)
main_splitter.setSizes([500, 400])
self.setCentralWidget(main_splitter)
self.player.positionChanged.connect(self.update_timeline_playhead)
self.player.positionChanged.connect(self.update_inspector)
self.create_menu_bar()
self.local_check_thread = LocalPendingUpdateCheckThread(CURRENT_VERSION, self.platform_suffix, PLATFORM_NAME, APP_NAME)
self.local_check_thread.pending_update_found.connect(self.updater.on_pending_update_found)
self.local_check_thread.no_pending_update.connect(self.updater.on_no_pending_update)
self.local_check_thread.start()
def create_menu_bar(self):
'''Menu Bar at the top of the screen'''
menu_bar = self.menuBar()
self.statusbar = self.statusBar()
def make_action(name, shortcut=None, slot=None, checkable=False, checked=False, icon=None):
action = QAction(name, self)
if shortcut:
action.setShortcut(QKeySequence(shortcut))
if slot:
action.triggered.connect(slot)
if checkable:
action.setCheckable(True)
action.setChecked(checked)
if icon:
action.setIcon(QIcon(icon))
return action
# File menu and actions
file_menu = menu_bar.addMenu("File")
file_actions = [
("Load Video...", "Ctrl+O", self.load_video, resource_path("icons/file_open_24dp_1F1F1F.svg")),
# ("Open Folder...", "Ctrl+Alt+O", self.not_implemented, resource_path("icons/folder_24dp_1F1F1F.svg")),
# ("Open Folders...", "Ctrl+Shift+O", self.open_folder_dialog, resource_path("icons/folder_copy_24dp_1F1F1F.svg")),
# ("Load Project...", "Ctrl+L", self.not_implemented, resource_path("icons/article_24dp_1F1F1F.svg")),
# ("Save Project...", "Ctrl+S", self.not_implemented, resource_path("icons/save_24dp_1F1F1F.svg")),
# ("Save Project As...", "Ctrl+Shift+S", self.not_implemented, resource_path("icons/save_as_24dp_1F1F1F.svg")),
]
for i, (name, shortcut, slot, icon) in enumerate(file_actions):
file_menu.addAction(make_action(name, shortcut, slot, icon=icon))
if i == 1: # after the first 3 actions (0,1,2)
file_menu.addSeparator()
file_menu.addSeparator()
file_menu.addAction(make_action("Exit", "Ctrl+Q", QApplication.instance().quit, icon=resource_path("icons/exit_to_app_24dp_1F1F1F.svg")))
# Edit menu
edit_menu = menu_bar.addMenu("Edit")
edit_actions = [
("Cut", "Ctrl+X", self.cut_text, resource_path("icons/content_cut_24dp_1F1F1F.svg")),
("Copy", "Ctrl+C", self.copy_text, resource_path("icons/content_copy_24dp_1F1F1F.svg")),
("Paste", "Ctrl+V", self.paste_text, resource_path("icons/content_paste_24dp_1F1F1F.svg"))
]
for name, shortcut, slot, icon in edit_actions:
edit_menu.addAction(make_action(name, shortcut, slot, icon=icon))
# View menu
view_menu = menu_bar.addMenu("View")
toggle_statusbar_action = make_action("Toggle Status Bar", checkable=True, checked=True, slot=None)
view_menu.addAction(toggle_statusbar_action)
toggle_statusbar_action.toggled.connect(self.statusbar.setVisible)
# Options menu (Help & About)
options_menu = menu_bar.addMenu("Options")
options_actions = [
("User Guide", "F1", self.user_guide, resource_path("icons/help_24dp_1F1F1F.svg")),
("Check for Updates", "F5", self.updater.manual_check_for_updates, resource_path("icons/update_24dp_1F1F1F.svg")),
("Batch YOLO processing...", "F6", self.open_batch_tool, resource_path("icons/upgrade_24dp_1F1F1F.svg")),
("About", "F12", self.about_window, resource_path("icons/info_24dp_1F1F1F.svg"))
]
for i, (name, shortcut, slot, icon) in enumerate(options_actions):
options_menu.addAction(make_action(name, shortcut, slot, icon=icon))
if i == 1 or i == 3: # after the first 2 actions (0,1)
options_menu.addSeparator()
preferences_menu = menu_bar.addMenu("Preferences")
preferences_actions = [
("Not Implemented", "", self.not_implemented, resource_path("icons/info_24dp_1F1F1F.svg")),
]
for name, shortcut, slot, icon in preferences_actions:
preferences_menu.addAction(make_action(name, shortcut, slot, icon=icon, checkable=True, checked=False))
terminal_menu = menu_bar.addMenu("Terminal")
terminal_actions = [
("Not Implemented", "", self.not_implemented, resource_path("icons/terminal_24dp_1F1F1F.svg")),
]
for name, shortcut, slot, icon in terminal_actions:
terminal_menu.addAction(make_action(name, shortcut, slot, icon=icon))
self.statusbar.showMessage("Ready")
def not_implemented(self):
self.statusbar.showMessage("Not Implemented.") # Show status message
def copy_text(self):
self.info_label.copy() # Trigger copy
self.statusbar.showMessage("Copied to clipboard") # Show status message
def cut_text(self):
self.info_label.cut() # Trigger cut
self.statusbar.showMessage("Cut to clipboard") # Show status message
def about_window(self):
if self.about is None or not self.about.isVisible():
self.about = AboutWindow(self)
self.about.show()
def user_guide(self):
if self.help is None or not self.help.isVisible():
self.help = UserGuideWindow(self)
self.help.show()
def paste_text(self):
self.info_label.paste() # Trigger paste
self.statusbar.showMessage("Pasted from clipboard") # Show status message
def open_batch_tool(self):
dialog = BatchProcessorDialog(self) # Pass 'self' to keep it centered
dialog.exec()
def toggle_mute(self):
is_muted = self.btn_mute.isChecked()
self.audio_output.setMuted(is_muted)
self.btn_mute.setText("Mute" if is_muted else "Vol")
# Optional: Dim the slider when muted
self.sld_volume.setEnabled(not is_muted)
def update_volume(self, value):
# QAudioOutput expects a float between 0.0 and 1.0
volume = value / 100.0
self.audio_output.setVolume(volume)
# Auto-unmute if user moves the slider
if self.btn_mute.isChecked() and value > 0:
self.btn_mute.setChecked(False)
self.toggle_mute()
def clear_ml_pool(self):
"""Removes all participants from the training buffer."""
debug_print()
# Confirm with the user first to prevent accidental deletions
reply = QMessageBox.question(self, 'Clear Pool?',
f"This will remove all {len(self.predictor.raw_participant_buffer)} "
"participants from the training memory. Continue?",
QMessageBox.Yes | QMessageBox.No, QMessageBox.No)
if reply == QMessageBox.Yes:
# 1. Clear the actual list in the predictor
self.predictor.raw_participant_buffer = []
# 2. Update the UI label
self.lbl_pool_status.setText("Pool: 0 Participants")
# 3. Optional: Visual feedback
# self.statusBar().showMessage("ML Pool cleared.", 3000)
print("DEBUG: ML Pool manually cleared.")
def update_predictor_target(self):
debug_print()
# This physically changes the string from "Mouthing" to "Head Movement"
self.predictor.current_target = self.target_dropdown.currentText()
print(f"Predictor is now targeting: {self.predictor.current_target}")
def reprocess_current_video(self):
"""Restarts the analysis worker to pick up new models."""
debug_print()
# Start the worker (passing the predictor so it can run AI models)
self.worker = PoseAnalyzerWorker(
self.file_path,
obs_info=self.selected_obs_subkey,
predictor=self.predictor
)
self.worker.progress.connect(self.update_status)
self.worker.finished_data.connect(self.setup_workspace)
self.worker.start()
def update_video_geometry(self):
debug_print()
if not hasattr(self, "video_item") or not hasattr(self, "data"):
return
viewport_rect = self.view.viewport().rect()
v_w, v_h = viewport_rect.width(), viewport_rect.height()
if v_w <= 0 or v_h <= 0: return
video_w, video_h = self.data['width'], self.data['height']
aspect = video_w / video_h
if v_w / v_h > aspect:
target_h = v_h
target_w = int(v_h * aspect)
else:
target_w = v_w
target_h = int(v_w / aspect)
x_off = (v_w - target_w) / 2
y_off = (v_h - target_h) / 2
self.scene.setSceneRect(0, 0, v_w, v_h)
self.video_item.setPos(x_off, y_off)
self.video_item.setSize(QSizeF(target_w, target_h))
self.skeleton_overlay.setGeometry(int(x_off), int(y_off), target_w, target_h)
def resizeEvent(self, event):
debug_print()
super().resizeEvent(event)
self.update_video_geometry()
if hasattr(self, 'timeline'):
self.timeline.set_zoom(self.timeline.zoom_factor)
# def eventFilter(self, source, event):
# if source is self.video_widget and event.type() == QEvent.Resize:
# self.skeleton_overlay.resize(event.size())
# return super().eventFilter(source, event)
def add_current_to_ml_pool(self):
"""Adds raw kinematic data and current OBS labels to the buffer."""
debug_print()
if not hasattr(self, 'data') or 'raw_kps' not in self.data:
QMessageBox.warning(self, "No Data", "Load a video first.")
return
# 1. Grab everything the Worker produced
payload = {
"z_kps": self.data['z_kps'],
"directions": self.data['directions'],
"raw_kps": self.data['raw_kps']
}
all_labels = self.timeline.get_all_binary_labels(self.current_video_offset, self.data["fps"])
# 3. Hand off to predictor
msg = self.predictor.add_to_raw_buffer(payload, all_labels)
self.lbl_pool_status.setText(f"Pool: {len(self.predictor.raw_participant_buffer)} Participants")
print(f"DEBUG: Added to Predictor at {hex(id(self.predictor))}")
print(f"DEBUG: Buffer size is now: {len(self.predictor.raw_participant_buffer)}")
QMessageBox.information(self, "Success", msg)
def run_final_training(self):
"""
Triggers training
"""
debug_print()
# DEBUG: Check the buffer directly before the IF statement
actual_buffer = self.predictor.raw_participant_buffer
current_count = len(actual_buffer)
if current_count < 1:
# If this triggers, let's see WHY it's empty
QMessageBox.warning(self, "Empty Pool",
f"Buffer is empty (Size: {current_count}).\n"
f"Predictor ID: {hex(id(self.predictor))}")
return
model_type = self.ml_dropdown.currentText()
current_target = self.target_dropdown.currentText()
reply = QMessageBox.question(self, 'Confirm Training',
f"Train {model_type} for '{current_target}' using "
f"{current_count} participants?",
QMessageBox.Yes | QMessageBox.No, QMessageBox.No)
if reply == QMessageBox.Yes:
self.btn_train_final.setEnabled(False)
self.btn_train_final.setText(f"Training...")
try:
# Force the target update right before training
self.predictor.current_target = current_target
report_html = self.predictor.calculate_and_train(model_type, current_target)
self.reprocess_current_video()
self.info_label.setText(report_html)
msg = QMessageBox(self)
msg.setWindowTitle("Results")
msg.setTextFormat(Qt.RichText)
msg.setText(report_html)
msg.exec()
except Exception as e:
traceback.print_exc()
QMessageBox.critical(self, "Error", f"{str(e)}")
finally:
self.btn_train_final.setEnabled(True)
self.btn_train_final.setText("Train Global Model")
# def import_json_observations(self):
# debug_print()
# file_path, _ = QFileDialog.getOpenFileName(self, "Select JSON Observations", "", "JSON Files (*.json)")
# if not file_path: return
# with open(file_path, 'r') as f:
# full_data = json.load(f)
# # Get the subkeys under "observations"
# subkeys = list(full_data.get("observations", {}).keys())
# if not subkeys:
# print("No observations found in JSON.")
# return
# item, ok = QInputDialog.getItem(self, "Select Session", "Pick an observation set:", subkeys, 0, False)
# if ok and item:
# new_obs_data = self.load_external_observations(file_path, item)
# self.append_new_tracks(new_obs_data)
def append_new_tracks(self, new_obs_data):
debug_print()
# 1. Update global TRACK_NAMES and TRACK_COLORS
for name in new_obs_data.keys():
if name not in TRACK_NAMES:
TRACK_NAMES.append(name)
# Assign a distinct color (e.g., a dark purple/magenta for observations)
TRACK_COLORS.append("#AA00FF")
# 2. Merge into existing data dictionary
self.data["events"].update(new_obs_data)
# 3. Refresh Timeline
global NUM_TRACKS
NUM_TRACKS = len(TRACK_NAMES)
self.timeline.set_data(self.data)
self.timeline.update_geometry()
# def load_external_observations(self, file_path, subkey):
# debug_print()
# with open(file_path, 'r') as f:
# data = json.load(f)
# raw_events = data["observations"][subkey]["events"]
# # We only care about: [time_seconds (0), _, label (2), _, _, _]
# new_tracks = {}
# # Sort events by time just in case the JSON is unsorted
# raw_events.sort(key=lambda x: x[0])
# # Group timestamps by their label (e.g., "Kick", "Baseline")
# temp_storage = {}
# for event in raw_events:
# time_sec = event[0]
# label = event[2]
# frame = int(time_sec * self.data["fps"])
# if label not in temp_storage:
# temp_storage[label] = []
# temp_storage[label].append(frame)
# # Convert pairs of frames into (start, end) blocks
# for label, frames in temp_storage.items():
# processed_blocks = []
# # Step through frames in pairs (start, end)
# for i in range(0, len(frames) - 1, 2):
# start = frames[i]
# end = frames[i+1]
# # Format: (start, end, severity, direction)
# processed_blocks.append((start, end, "External", "Manual Obs"))
# new_tracks[f"OBS: {label}"] = processed_blocks
# return new_tracks
def load_video(self):
debug_print()
self.file_path, _ = QFileDialog.getOpenFileName(self, "Open Video", "", "Video Files (*.mp4 *.avi *.mkv)")
if not self.file_path: return
cap = cv2.VideoCapture(self.file_path)
if cap.isOpened():
self.current_video_fps = cap.get(cv2.CAP_PROP_FPS) or 30.0
#total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
# Optional: Initialize timeline with blank data so it can at least draw the ruler
#self.timeline.data = {"total_frames": total_frames, "fps": self.current_video_fps, "events": {}}
cap.release()
else:
self.current_video_fps = 30.0 # Fallback
# --- NEW: JSON Observation Prompt ---
self.obs_file, _ = QFileDialog.getOpenFileName(self, "Select JSON Observations (Optional)", "", "JSON Files (*.json)")
if self.obs_file:
try:
with open(self.obs_file, 'r') as f:
full_json = json.load(f)
observations = full_json.get("observations", {})
subkeys = list(observations.keys())
# --- AUTO-MATCHING LOGIC ---
# 1. Get the video filename without extension (e.g., 'T4_2T_WORD_F')
video_name = os.path.splitext(os.path.basename(self.file_path))[0]
v_parts = video_name.split('_')
# Build the 'fingerprint' from the video (Blocks 1, 2, and the Last one)
# This ignores the 'WORDHERE' block in the middle
if len(v_parts) >= 3:
video_fingerprint = f"{v_parts[0]}_{v_parts[1]}_{v_parts[-1]}"
else:
video_fingerprint = video_name # Fallback
match = None
for sk in subkeys:
s_parts = sk.split('_')
# Subkeys are shorter: Block 1, 2, and 3
if len(s_parts) == 3:
sk_fingerprint = f"{s_parts[0]}_{s_parts[1]}_{s_parts[2]}"
if sk_fingerprint.lower() == video_fingerprint.lower():
match = sk
break
# 2. Decision: Use match or prompt user
if match:
self.selected_obs_subkey = (self.obs_file, match)
self.statusBar().showMessage(f"Auto-matched JSON session: {match}", 5000)
elif subkeys:
# No match found, only then show the popup
item, ok = QInputDialog.getItem(self, "Select Session",
f"Could not auto-match '{video_name}'.\nPick manually:",
subkeys, 0, False)
if ok and item:
self.selected_obs_subkey = (self.obs_file, item)
# --- NEW: Offset & File Matching Logic ---
if self.selected_obs_subkey:
_, session_key = self.selected_obs_subkey
session_data = observations.get(session_key, {})
file_map = session_data.get("file", {})
video_filename = os.path.basename(self.file_path)
found_index = None
# 1. Attempt Auto-Match by filename
for idx_str, file_list in file_map.items():
# Check if our loaded video is in this list (e.g., "Videos\\T4_2T_WORD_F.mp4")
if any(video_filename in path for path in file_list):
found_index = idx_str
print(f"DEBUG: Auto-matched video to Camera Index {idx_str}")
break
# 2. If Auto-Match fails, prompt user for Camera Index
if not found_index:
available_indices = [k for k, v in file_map.items() if v] # Only indices with files
if available_indices:
item, ok = QInputDialog.getItem(self, "Identify Camera",
f"Could not find '{video_filename}' in JSON.\n"
"Which camera index is this video?",
available_indices, 0, False)
if ok:
found_index = item
# 3. Retrieve and Print the Offset
if found_index:
offsets = session_data.get("media_info", {}).get("offset", {})
search_key = str(found_index)
# Note: offsets dict might use integers or strings as keys
# We check both to be safe
actual_offset = offsets.get(search_key)
if actual_offset is not None:
print(f"MATCHED OFFSET: {actual_offset:.4f}")
# Store this if you need it for timeline syncing later
self.current_video_offset = float(actual_offset)
self.timeline.set_sync_params(
offset_seconds=self.current_video_offset,
fps=self.current_video_fps
)
print(f"✅ Timeline synced with {actual_offset}s offset.")
else:
print(f"DEBUG: No offset found for index {found_index}")
except Exception as e:
QMessageBox.warning(self, "JSON Error", f"Could not parse JSON: {e}")
# --- Cache Logic ---
# cache_path = self.file_path.rsplit('.', 1)[0] + "_pose_cache.csv"
# use_cache = None
# if os.path.exists(cache_path):
# reply = QMessageBox.question(self, 'Cache Found',
# "Use existing pose cache?",
# QMessageBox.Yes | QMessageBox.No)
# use_cache = cache_path if reply == QMessageBox.Yes else None
self.btn_load.setEnabled(False)
# Pass the observation info to the worker
self.worker = PoseAnalyzerWorker(self.file_path, self.selected_obs_subkey, self.predictor)
self.worker.progress.connect(self.update_status)
self.worker.finished_data.connect(self.setup_workspace)
self.worker.start()
def update_status(self, msg):
debug_print()
self.info_label.setText(f"Status:\n{msg}")
def setup_workspace(self, data):
debug_print()
self.data = data
self.player.setSource(QUrl.fromLocalFile(data["video_path"]))
self.player.play()
self.player.pause()
self.timeline.set_data(data)
self.skeleton_overlay.set_data(data)
self.update_video_geometry()
for btn in self.transport_btns:
btn.setEnabled(True)
total_f = data['total_frames']
fps = data['fps']
tot_s = int(total_f / fps)
# Display 0 / Total
self.lbl_time_counter.setText(f"00:00 / {tot_s//60:02d}:{tot_s%60:02d}")
self.lbl_frame_counter.setText(f"0 / {total_f-1}")
# Sync widgets
self.timeline.set_data(data)
self.skeleton_overlay.set_data(data)
# Force a seek to frame 0 to initialize the video buffer
self.seek_video(0)
self.btn_load.setEnabled(True)
info_text = (
f"File: {os.path.basename(data['video_path'])}\n"
f"Resolution: {data['width']}x{data['height']}\n"
f"FPS: {data['fps']:.2f}\n"
f"Total Frames: {data['total_frames']}\n\n"
f"Timeline Legend (Opacity):\n"
f"255 Alpha = Large Deviation\n"
f"160 Alpha = Moderate Deviation\n"
f"80 Alpha = Small Deviation\n"
f"Empty = Rest (Baseline)"
)
self.info_label.setText(info_text)
def toggle_playback(self):
debug_print()
if not hasattr(self, 'data'): return
# If we are at the end, jump to the start first
fps = self.data["fps"]
current_frame = int((self.player.position() / 1000.0) * fps + 0.5)
if current_frame >= self.data["total_frames"] - 1:
self.seek_video(0)
if self.player.playbackState() == QMediaPlayer.PlayingState:
self.player.pause()
self.btn_play.setText("Play")
else:
self.player.play()
self.btn_play.setText("Pause")
def update_timeline_playhead(self, position_ms):
debug_print()
if hasattr(self, 'data') and self.data["fps"] > 0:
fps = self.data["fps"]
total_f = self.data["total_frames"]
# Current frame calculation
current_f = int((position_ms / 1000.0) * fps)
# --- PREVENT BLACK FRAME AT END ---
# If we are within 1 frame of the end, stop and lock to the last valid frame
if current_f >= total_f - 1:
if self.player.playbackState() == QMediaPlayer.PlayingState:
self.player.pause()
self.btn_play.setText("Play")
current_f = total_f - 1
# Seek slightly back from total duration to keep the image visible
last_valid_ms = int(((total_f - 1) / fps) * 1000)
self.player.setPosition(last_valid_ms)
# Sync UI
self.timeline.set_playhead(current_f)
self.skeleton_overlay.set_frame(current_f)
self.update_counters(current_f)
def on_track_selected(self, track_name):
debug_print()
self.selected_track = track_name
if not track_name:
self.info_label.setText("No track selected.\nClick a data track to inspect.")
self.info_label.setStyleSheet("color: #AAAAAA; font-family: 'Segoe UI'; font-size: 10pt;")
else:
self.info_label.setStyleSheet("color: #00FF00; font-family: 'Segoe UI'; font-size: 10pt;")
self.update_inspector() # Refresh immediately on click
def update_inspector(self):
debug_print()
if not hasattr(self, 'selected_track') or not self.selected_track or not self.data:
return
# 1. Temporal Logic
current_f = int((self.player.position() / 1000.0) * self.data["fps"])
current_f = max(0, min(current_f, self.data["total_frames"] - 1))
is_ai = "AI:" in self.selected_track
is_obs = "OBS:" in self.selected_track
# 2. Status/Raw Logic
if is_ai or is_obs:
# Check Activity for Behavior Tracks
events = self.data["events"].get(self.selected_track, [])
is_active = any(start <= current_f <= end for start, end, *rest in events)
active_color = "#ff5555" if is_active else "#888888"
status_line = f"ACTIVE: {'YES' if is_active else 'NO'}"
raw_line = "" # Do not display raw for AI/OBS
else:
# Kinematics Logic (No Active status)
status_line = ""
raw_info = "N/A"
cache_path = self.file_path.rsplit('.', 1)[0] + "_pose_raw.csv"
print(cache_path)
if os.path.exists(cache_path):
try:
# Row 2 in CSV is Frame 0. pandas.read_csv uses Row 1 as header.
# So Frame 0 is df.iloc[0].
print(current_f)
print(len(self.worker.pose_df))
if current_f < len(self.worker.pose_df):
row = self.worker.pose_df.iloc[current_f]
print(self.selected_track)
col_x, col_y, col_c = f"{self.selected_track}_x", f"{self.selected_track}_y", f"{self.selected_track}_conf"
print(self.worker.pose_df.columns)
if col_x in self.worker.pose_df.columns and col_y in self.worker.pose_df.columns:
print("me")
rx, ry = row[col_x], row[col_y]
rc = row[col_c] if col_c in self.worker.pose_df.columns else 0.0
raw_info = f"X: {rx:.2f} | Y: {ry:.2f} | Conf: {rc:.2f}"
except Exception as e:
print(f"Inspector CSV Error: {e}")
raw_info = "Index Error"
raw_line = f"RAW (CSV): {raw_info}"
# 3. Construct Display
display_text = (
f"TRACK: {self.selected_track}
"
f"FRAME: {current_f}
"
f"{status_line}"
f"{raw_line}"
)
# 4. Performance Report
if is_ai:
target_name = self.selected_track.replace("AI: ", "")
pattern = f"ml_{target_name}_performance_*.txt"
report_files = sorted(glob.glob(pattern))
report_content = "No report found."
if report_files:
try:
with open(report_files[-1], 'r') as f:
report_content = f.read().replace('\n', '
')
except: pass
display_text += f"
AI Performance:
{report_content}"
self.info_label.setText(display_text)
def step_frame(self, delta):
debug_print()
if not hasattr(self, 'data'): return
fps = self.data["fps"]
# Calculate current frame based on ms position
current_f = int((self.player.position() / 1000.0) * fps + 0.5)
target_f = current_f + delta
# Use your existing seek_video to handle bounds and UI updates
self.seek_video(target_f)
def seek_video(self, frame):
debug_print()
if hasattr(self, 'data') and self.data["fps"] > 0:
total_f = self.data["total_frames"]
target_frame = max(0, min(frame, total_f - 1))
# Calculate MS with a tiny offset (0.1) to ensure the player
# lands ON the frame, not slightly before it.
ms = int((target_frame / self.data["fps"]) * 1000)
self.player.setPosition(ms)
self.video_item.update()
# Update UI immediately for snappier feedback
self.timeline.set_playhead(target_frame)
self.update_counters(target_frame)
def update_counters(self, current_f):
debug_print()
# Dedicated method to refresh the labels
fps = self.data["fps"]
total_f = self.data["total_frames"]
cur_s, tot_s = int(current_f / fps), int(total_f / fps)
self.lbl_time_counter.setText(f"Time: {cur_s//60:02d}:{cur_s%60:02d} / {tot_s//60:02d}:{tot_s%60:02d}")
self.lbl_frame_counter.setText(f"Frame: {current_f} / {total_f-1}")
def extract_ai_to_json(self):
"""
Automatically saves AI extractions to the video directory
with the suffix '_events.json'.
"""
# 1. Check if a video is loaded to get the base path
video_path = getattr(self, "file_path", None)
if not video_path or not os.path.exists(video_path):
print("Error: No video loaded. Cannot determine save path.")
return
# 2. Construct the new filename
base_dir = os.path.dirname(video_path)
file_name = os.path.splitext(os.path.basename(video_path))[0]
save_path = os.path.join(base_dir, f"{file_name}_events.blaze")
# 3. Call the timeline method to get the data
try:
extraction_data = self.timeline.get_ai_extractions()
# Inject source video metadata
extraction_data["metadata"]["source_video"] = video_path
# 4. Save to disk
with open(save_path, 'w') as f:
json.dump(extraction_data, f, indent=4)
print(f"Extraction automatically saved to: {save_path}")
except Exception as e:
print(f"Error during automatic AI extraction: {e}")
def resource_path(relative_path):
"""
Get absolute path to resource regardless of running directly or packaged using PyInstaller
"""
if hasattr(sys, '_MEIPASS'):
# PyInstaller bundle path
base_path = sys._MEIPASS
else:
base_path = os.path.dirname(os.path.abspath(__file__))
return os.path.join(base_path, relative_path)
def kill_child_processes():
"""
Goodbye children
"""
try:
parent = psutil.Process(os.getpid())
children = parent.children(recursive=True)
for child in children:
try:
child.kill()
except psutil.NoSuchProcess:
pass
psutil.wait_procs(children, timeout=5)
except Exception as e:
print(f"Error killing child processes: {e}")
def exception_hook(exc_type, exc_value, exc_traceback):
"""
Method that will display a popup when the program hard crashes containg what went wrong
"""
error_msg = "".join(traceback.format_exception(exc_type, exc_value, exc_traceback))
print(error_msg) # also print to console
kill_child_processes()
# Show error message box
# Make sure QApplication exists (or create a minimal one)
app = QApplication.instance()
if app is None:
app = QApplication(sys.argv)
show_critical_error(error_msg)
# Exit the app after user acknowledges
sys.exit(1)
def show_critical_error(error_msg):
msg_box = QMessageBox()
msg_box.setIcon(QMessageBox.Icon.Critical)
msg_box.setWindowTitle("Something went wrong!")
if PLATFORM_NAME == "darwin":
log_path = os.path.join(os.path.dirname(sys.executable), "../../../flares.log")
log_path2 = os.path.join(os.path.dirname(sys.executable), "../../../flares_error.log")
save_path = os.path.join(os.path.dirname(sys.executable), "../../../flares_autosave.flare")
else:
log_path = os.path.join(os.getcwd(), "flares.log")
log_path2 = os.path.join(os.getcwd(), "flares_error.log")
save_path = os.path.join(os.getcwd(), "flares_autosave.flare")
shutil.copy(log_path, log_path2)
log_path2 = Path(log_path2).absolute().as_posix()
autosave_path = Path(save_path).absolute().as_posix()
log_link = f"file:///{log_path2}"
autosave_link = f"file:///{autosave_path}"
message = (
f"{APP_NAME.upper()} has encountered an unrecoverable error and needs to close.
"
f"We are sorry for the inconvenience. An autosave was attempted to be saved to {autosave_path}, but it may not have been saved. "
"If the file was saved, it still may not be intact, openable, or contain the correct data. Use the autosave at your discretion.
"
f"This unrecoverable error was likely due to an error with {APP_NAME.upper()} and not your data.
"
f"Please raise an issue here and attach the error file located at {log_path2}
"
f"{error_msg}"
)
msg_box.setTextFormat(Qt.TextFormat.RichText)
msg_box.setText(message)
msg_box.setTextInteractionFlags(Qt.TextInteractionFlag.TextBrowserInteraction)
msg_box.setStandardButtons(QMessageBox.StandardButton.Ok)
msg_box.exec()
if __name__ == "__main__":
# Redirect exceptions to the popup window
sys.excepthook = exception_hook
# Set up application logging
if PLATFORM_NAME == "darwin":
log_path = os.path.join(os.path.dirname(sys.executable), f"../../../{APP_NAME}.log")
else:
log_path = os.path.join(os.getcwd(), f"{APP_NAME}.log")
try:
os.remove(log_path)
except:
pass
sys.stdout = open(log_path, "a", buffering=1)
sys.stderr = sys.stdout
print(f"\n=== App started at {datetime.now()} ===\n")
freeze_support() # Required for PyInstaller + multiprocessing
# Only run GUI in the main process
if current_process().name == 'MainProcess':
app = QApplication(sys.argv)
finish_update_if_needed(PLATFORM_NAME, APP_NAME)
window = PremiereWindow()
if PLATFORM_NAME == "darwin":
app.setWindowIcon(QIcon(resource_path("icons/main.icns")))
window.setWindowIcon(QIcon(resource_path("icons/main.icns")))
else:
app.setWindowIcon(QIcon(resource_path("icons/main.ico")))
window.setWindowIcon(QIcon(resource_path("icons/main.ico")))
window.show()
sys.exit(app.exec())
# Not 6000 lines yay!