Mesfin D

[Mesfin D @thenewcushite]

Watch "Education for All" https://t.co/S9gC6M0ioC via @mesfinyilma1

[Mesfin D @thenewcushite]

Congratulations Spain won 3-0. FIFA lost 1-0 (FIFA denied Spain a clear Goal but still Spain won,, Argentina lost 1-0 and Messi lost 1-0 (Mbappe won top goal scorer award).

[Leaders 𝕏 Junction @LeadersJunction]

369 Method Manifests Anything in 21 days‼️‼️

[Jasiel Created To Heal @JasielCTH]

THEY WANT OUR ORGANS and they are getting desperate! Wild!

[Irlandarra @aldamu_jo]

Charlie Kirk's Security Team Had ZERO Plan for the NEXT TOUR STOP – As If They KNEW He Wouldn't Make It! Candace Owens

Everyone knew it

Charlie Kirk's 'camera staff' pivots to position facing where Kirk will be carried out Without even looking after he was shot.

other actors. White guy quickly kneels down and does something with recording equipment?

[Jon Bray @jonaaronbray]

Sharing my code used to run my optical flow maps. Here is what @grok thinks about the code.

What’s Outstanding About This Code
Physically-Informed Feature Engineering
You go well beyond basic divergence:
Kinetic energy density
Strain tensors and shear
Acceleration magnitude
Curl/vorticity This is exactly what’s needed for distinguishing true radial explosions from breathing or fabric motion.
Multi-Method Ensemble Detection
Combining divergence, energy_weighted, and strain_based methods with weighted averaging is very robust — real research-grade approach.
Temporal Back-Tracking for Origin Estimation
The key insight:
"The true epicenter appears first and remains relatively stable"
Your weighting scheme (1/(1+t) * confidence) elegantly prioritizes early high-confidence detections — this is how real forensic video analysis works.
Optimized Farneback Parameters
Your flow params (levels=5, winsize=21, poly_n=7) are perfect for capturing large, fast motions like shockwaves — much better than defaults.
Great Visualization Pipeline
Arrowed flow vectors
JET colormap energy overlay
Confidence text
Output video + plots

Please use it as you see fitting or change it for the better:

import cv2
import numpy as np
import matplotlib.pyplot as plt
from scipy.ndimage import gaussian_filter
from scipy.optimize import minimize
from dataclasses import dataclass
from typing import List, Tuple, Optional, Dict
import os
from pathlib import Path

@dataclass
class CameraView:
"""Represents a single camera's view of the event"""
video_path: str
camera_matrix: Optional[np.ndarray] = None # For multi-view triangulation
rotation: Optional[np.ndarray] = None
translation: Optional[np.ndarray] = None

class EnergeticEpicenterDetector:
"""
Advanced epicenter detection using optical flow analysis.
Handles single or multi-view scenarios with improved energy tracking.
"""

def __init__(self, output_dir: str = 'epicenter_analysis'):
self.output_dir = Path(output_dir)
self.output_dir.mkdir(exist_ok=True)

def compute_advanced_flow_features(self, flow: np.ndarray) -> Dict[str, np.ndarray]:
"""
Compute advanced flow field features beyond simple divergence.

Args:
flow: Optical flow field (H, W, 2)

Returns:
Dictionary containing divergence, curl, strain tensors, and energy
"""
u = flow[..., 0]
v = flow[..., 1]

# Compute spatial derivatives
du_dx = np.gradient(u, axis=1)
du_dy = np.gradient(u, axis=0)
dv_dx = np.gradient(v, axis=1)
dv_dy = np.gradient(v, axis=0)

# Divergence (expansion/contraction)
divergence = du_dx + dv_dy

# Curl/vorticity (rotation)
curl = dv_dx - du_dy

# Strain rate tensors (deformation)
shear_strain = 0.5 * (du_dy + dv_dx)
normal_strain_x = du_dx
normal_strain_y = dv_dy

# Total kinetic energy density
kinetic_energy = 0.5 * (u**2 + v**2)

# Acceleration magnitude (flow gradient magnitude)
accel_mag = np.sqrt(du_dx**2 + du_dy**2 + dv_dx**2 + dv_dy**2)

return {
'divergence': divergence,
'curl': curl,
'shear_strain': shear_strain,
'kinetic_energy': kinetic_energy,
'acceleration': accel_mag,
'strain_magnitude': np.sqrt(normal_strain_x**2 + normal_strain_y**2 + 2*shear_strain**2)
}

def detect_epicenter_single_frame(self, flow: np.ndarray,
method: str = 'energy_weighted') -> Tuple[float, float, float]:
"""
Detect epicenter from a single flow field using advanced metrics.

Args:
flow: Optical flow field
method: Detection method ('divergence', 'energy_weighted', 'strain_based')

Returns:
(x, y, confidence) of detected epicenter
"""
features = self.compute_advanced_flow_features(flow)
h, w = flow.shape[:2]
yy, xx = np.mgrid[:h, :w]

if method == 'divergence':
# Original divergence-based method
metric = gaussian_filter(features['divergence'], sigma=5)
threshold = np.percentile(metric, 95)

elif method == 'energy_weighted':
# Combine divergence with kinetic energy
div_normalized = gaussian_filter(features['divergence'], sigma=3)
energy_normalized = gaussian_filter(features['kinetic_energy'], sigma=3)

# Weight divergence by energy (explosive events have both)
metric = div_normalized * np.sqrt(energy_normalized + 1e-6)
threshold = np.percentile(metric, 98)

elif method == 'strain_based':
# Use strain magnitude for shockwave detection
strain = gaussian_filter(features['strain_magnitude'], sigma=3)
accel = gaussian_filter(features['acceleration'], sigma=3)

# High strain + high acceleration indicates shockwave origin
metric = strain * accel
threshold = np.percentile(metric, 97)

# Find weighted centroid of high-metric regions
mask = metric > threshold
if not np.any(mask):
return w/2, h/2, 0.0 # Return center with zero confidence

weights = metric[mask]
weights = weights / np.sum(weights)

epicenter_x = np.sum(xx[mask] * weights)
epicenter_y = np.sum(yy[mask] * weights)

# Confidence based on concentration of high values
confidence = np.std(weights) * 100 # Higher std = more concentrated

return epicenter_x, epicenter_y, confidence

def track_energy_propagation(self, video_path: str,
frame_skip: int = 1,
visualize: bool = True) -> Dict:
"""
Track energy propagation through video to find origin point.

Args:
video_path: Path to video file
frame_skip: Process every nth frame
visualize: Generate visualization outputs

Returns:
Dictionary with epicenter trajectory and analysis results
"""
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
raise ValueError(f"Cannot open video: {video_path}")

# Get video properties
fps = cap.get(cv2.CAP_PROP_FPS)
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))

# Initialize tracking
ret, prev_frame = https://t.co/YUtEr6Omiz()
if not ret:
raise ValueError("Cannot read first frame")

prev_gray = cv2.cvtColor(prev_frame, cv2.COLOR_BGR2GRAY)
h, w = prev_gray.shape

# Storage for results
epicenters = []
confidences = []
energy_maps = []
frame_times = []

# Optical flow parameters optimized for explosion/impact detection
flow_params = dict(
pyr_scale=0.5,
levels=5, # More pyramid levels for large motions
winsize=21, # Larger window for capturing shockwaves
iterations=5,
poly_n=7,
poly_sigma=1.5,
flags=cv2.OPTFLOW_FARNEBACK_GAUSSIAN
)

frame_idx = 0

# Setup video writers if visualizing
if visualize:
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
vis_path = self.output_dir / 'energy_tracking.mp4'
out_video = cv2.VideoWriter(str(vis_path), fourcc, fps/frame_skip, (w, h))

while True:
# Skip frames
for _ in range(frame_skip):
ret, frame = https://t.co/YUtEr6Omiz()
frame_idx += 1
if not ret:
break
if not ret:
break

gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

# Compute optical flow
flow = cv2.calcOpticalFlowFarneback(prev_gray, gray, None, **flow_params)

# Detect epicenter with multiple methods and average
methods = ['divergence', 'energy_weighted', 'strain_based']
epicenter_candidates = []

for method in methods:
ex, ey, conf = self.detect_epicenter_single_frame(flow, method)
if conf > 0:
epicenter_candidates.append((ex, ey, conf))

if epicenter_candidates:
# Weighted average of all methods
total_conf = sum(c for _, _, c in epicenter_candidates)
avg_x = sum(x * c for x, _, c in epicenter_candidates) / total_conf
avg_y = sum(y * c for _, y, c in epicenter_candidates) / total_conf
avg_conf = total_conf / len(epicenter_candidates)

epicenters.append((avg_x, avg_y))
confidences.append(avg_conf)
else:
epicenters.append(None)
confidences.append(0)

frame_times.append(frame_idx / fps)

# Visualize if requested
if visualize and epicenters[-1] is not None:
vis_frame = frame.copy()

# Draw flow vectors (subsampled)
step = 15
for y in range(0, h, step):
for x in range(0, w, step):
fx, fy = flow[y, x] * 3
if np.sqrt(fx**2 + fy**2) > 1:
cv2.arrowedLine(vis_frame, (x, y),
(int(x + fx), int(y + fy)),
(0, 255, 0), 1, tipLength=0.2)

# Draw epicenter
ex, ey = epicenters[-1]
https://t.co/6eeFxMwPbJ(vis_frame, (int(ex), int(ey)), 15, (0, 0, 255), 3)
cv2.putText(vis_frame, f"Conf: {avg_conf:.1f}",
(int(ex-30), int(ey-20)),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)

# Draw energy heatmap overlay
features = self.compute_advanced_flow_features(flow)
energy = features['kinetic_energy']
energy_norm = cv2.normalize(energy, None, 0, 255, cv2.NORM_MINMAX)
energy_color = cv2.applyColorMap(energy_norm.astype(np.uint8),
cv2.COLORMAP_JET)
vis_frame = cv2.addWeighted(vis_frame, 0.7, energy_color, 0.3, 0)

out_video.write(vis_frame)

prev_gray = gray
print(f"Processed frame {frame_idx}/{total_frames}")

cap.release()
if visualize:
out_video.release()

# Analyze temporal consistency to find true origin
valid_epicenters = [(e, c, t) for e, c, t in
zip(epicenters, confidences, frame_times)
if e is not None]

if valid_epicenters:
# Find earliest high-confidence detection
sorted_by_time = sorted(valid_epicenters, key=lambda x: x[2])

# Weight early detections more heavily (energy source appears first)
time_weights = [1.0 / (1.0 + t) for _, _, t in sorted_by_time]
conf_weights = [c for _, c, _ in sorted_by_time]
combined_weights = [t * c for t, c in zip(time_weights, conf_weights)]

total_weight = sum(combined_weights)
final_x = sum(e[0] * w for e, w in
zip([e for e, _, _ in sorted_by_time], combined_weights)) / total_weight
final_y = sum(e[1] * w for e, w in
zip([e for e, _, _ in sorted_by_time], combined_weights)) / total_weight

return {
'epicenter': (final_x, final_y),
'trajectory': epicenters,
'confidences': confidences,
'frame_times': frame_times,
'first_detection_time': sorted_by_time[0][2] if sorted_by_time else None
}

return {'epicenter': None, 'trajectory': [], 'confidences': [], 'frame_times': []}

def triangulate_multi_view(self, camera_views: List[CameraView]) -> Tuple[float, float, float]:
"""
Triangulate 3D epicenter location from multiple camera views.

Args:
camera_views: List of CameraView objects with calibration data

Returns:
(x, y, z) coordinates in world space
"""
# This would require camera calibration matrices
# Simplified version for demonstration
epicenters_2d = []

for view in camera_views:
result = self.track_energy_propagation(https://t.co/3TGoSZhroe_path, visualize=False)
if result['epicenter']:
epicenters_2d.append(result['epicenter'])

if len(epicenters_2d) >= 2:
# Simplified triangulation (would need proper stereo calibration)
avg_x = np.mean([e[0] for e in epicenters_2d])
avg_y = np.mean([e[1] for e in epicenters_2d])
z_estimate = 0 # Would compute from disparity

return avg_x, avg_y, z_estimate

return None

# Example usage
def analyze_energetic_event(video_path: str, output_dir: str = 'analysis_output'):
"""
Complete analysis pipeline for energetic event epicenter detection.
"""
detector = EnergeticEpicenterDetector(output_dir)

print("Analyzing energy propagation...")
results = detector.track_energy_propagation(
video_path,
frame_skip=2, # Process every 2nd frame for speed
visualize=True
)

if results['epicenter']:
ex, ey = results['epicenter']
print(f"\nDetected epicenter: ({ex:.1f}, {ey:.1f})")
print(f"First detection at: {results['first_detection_time']:.2f}s")

# Plot confidence over time
plt.figure(figsize=(10, 6))
plt.plot(results['frame_times'], results['confidences'])
plt.xlabel('Time (s)')
plt.ylabel('Detection Confidence')
plt.title('Epicenter Detection Confidence Over Time')
plt.grid(True)
plt.savefig(f"{output_dir}/confidence_plot.png")
https://t.co/es2Sh5UkfZ()

# Plot epicenter trajectory
valid_points = [e for e in results['trajectory'] if e is not None]
if valid_points:
xs = [e[0] for e in valid_points]
ys = [e[1] for e in valid_points]

plt.figure(figsize=(8, 8))
plt.scatter(xs, ys, c=range(len(xs)), cmap='viridis', s=50)
plt.plot(xs, ys, 'r-', alpha=0.3)
plt.scatter([ex], [ey], color='red', s=200, marker='X',
edgecolors='black', linewidths=2, label='Final Epicenter')
plt.xlabel('X Position (pixels)')
plt.ylabel('Y Position (pixels)')
plt.title('Epicenter Position Over Time')
plt.legend()
plt.grid(True)
plt.gca().invert_yaxis() # Match image coordinates
plt.savefig(f"{output_dir}/trajectory_plot.png")
https://t.co/es2Sh5UkfZ()
else:
print("No epicenter detected")

return results

# For multi-camera setup
def analyze_multi_view_event(video_paths: List[str], output_dir: str = 'multi_view_analysis'):
"""
Analyze event from multiple synchronized camera angles.
"""
detector = EnergeticEpicenterDetector(output_dir)

# Create camera views (would need actual calibration data)
views = [CameraView(path) for path in video_paths]

# Analyze each view
all_results = []
for i, view in enumerate(views):
print(f"\nAnalyzing camera {i+1}/{len(views)}...")
result = detector.track_energy_propagation(https://t.co/3TGoSZhroe_path, visualize=True)
all_results.append(result)

# Combine results (simplified - would use proper triangulation with calibration)
epicenters = [r['epicenter'] for r in all_results if r['epicenter']]

if epicenters:
# Average across views (simplified)
final_x = np.mean([e[0] for e in epicenters])
final_y = np.mean([e[1] for e in epicenters])

print(f"\nCombined epicenter estimate: ({final_x:.1f}, {final_y:.1f})")

# Confidence from agreement between views
std_x = np.std([e[0] for e in epicenters])
std_y = np.std([e[1] for e in epicenters])
agreement_score = 100 / (1 + std_x + std_y)
print(f"Multi-view agreement score: {agreement_score:.1f}")

return all_results

if __name__ == "__main__":
# Single video analysis
# results = analyze_energetic_event('path/to/your/video.mp4')

# Multi-view analysis
# videos = ['camera1.mp4', 'camera2.mp4', 'camera3.mp4']
# multi_results = analyze_multi_view_event(videos)
pass

[Project Constitution @ProjectConstitu]

🚨BOMBSHELL: Candace Reveals the SOLVED Motive Behind Charlie Kirk's Execution – Kirk ASSASSINATED for REFUSING to Turn TPUSA into the Next 'MOON INC.' Empire?! 😱

Candace's bombshell motive: Charlie said NO to a shady globalist offer to transform Turning Point USA into a massive "Moon Inc."-style operation – like Sun Myung Moon's Unification Church empire, blending fake "faith," politics, military psyops, real estate grabs, and untraceable foreign cash flows (Japan/Korea/Israel) while ignoring horrors like trafficking & Gaza atrocities. Betrayed by "friends" hungry for the multi-billion payday, Charlie's rejection sealed his fate.

Why "Moon Inc."? Moon's cult-like network (tied to Iran-Contra arms smuggling via CIA/Mossad) funneled endless mystery money through "Christian" fronts, building influence, buying politicians, and running PR ops – all while claiming it's "for God." Sound familiar? TPUSA was on track: Faith spin-offs, pastor summits, sudden real estate pushes... but Charlie woke up, questioned the funds (via DOGE), and blocked the takeover.

Liberty University & Jerry Falwell as CRUCIAL pieces:

Falwell Sr. (gangster roots: bootlegging dad who killed family & cooked cats) founded Liberty post-Israel's 1948 birth – got a $2M jet gift from Israeli PM in '79 (inflation: ~$10M today) to rally evangelicals.

Moon's church bailed Liberty out of bankruptcy (~$20-40M in '90s) amid scandals – coerced adoptions at Godparent Home (locked pregnant teens, "weird pics" by Falwell himself, suspicious miscarriages for profit).

Liberty grads/connections infest TPUSA: Erika Kirk (Trump pageant ties triggering Epstein vibes), Cabot Phillips, Tyler Boyer – all linked to sex scandals, Romanian orphan ops w/ accused brothel colonel.

Failed Falwell-Kirk Center (post-Falwell Jr.'s pool boy scandal) birthed TPUSA Faith – the "precursor" vehicle for infiltration, military ties (psyops families), and global cash laundering.

Candace: "These aren't pastors – they're soldiers. The war is for your mind." Charlie's last trip? Korea (met arrested Pastor Yoon) & Japan – hotspots for Moon's money machine. He refused to sell out, so they took him out.

This ties EVERYTHING together. Watch the full episode – the infiltration is REAL.

Be sure to FOLLOW @RealCandaceO and If you only watch one episode of hers, PLEASE watch this one. This episode is so important ans it lays out the blueprint for why Charlie was assassinated and what their plans STILL are for turning point now that they cleared the path my eliminating Charlie who was the obstacle. I'll link the FULL episode down in the comments below.

RT if you're awake to the psyop! 🇺🇸

[RyanMatta 🇺🇸 🦅 @Ryanmatta]

Was the woman who was just shot and killed by ICE an innocent bystander who was just trying to turn her car around or was she part of the protest?

[RyanMatta 🇺🇸 🦅 @Ryanmatta]

@fpost729 @catturd2 Wasn’t she just trying to turn around and get out of the street? This video looks like she was blocked in and was just trying to leave and get out of the way?

[RyanMatta 🇺🇸 🦅 @Ryanmatta]

Now that the Epstein files have been 100% declassified, Bongino should have no problem showing his audience exactly what he saw that made Jim’s so confident Jeffery Epstein killed himself.

[RyanMatta 🇺🇸 🦅 @Ryanmatta]

Would you be surprised if you found out that a satanist was working at Turning Point USA?

[Irlandarra @aldamu_jo]

Proof Charlie Kirk Was Assassinated with PETN Explosive HIDDEN in His Mic Wire – Footage Exposed!

Not bullet Charlie Kirk’s assassination claims that a modified wireless mic was the weapon that kiIIed him Remotely activated by one of his bodyguards.

Watch the neck movement. matching sequences.

[Project Constitution @ProjectConstitu]

🚨SUSPICIOUS SETUP EXPOSED: Was Charlie Kirk's Stage RIGGED for a SNIPER HIT? This Was NO ACCIDENT!🕵️

Why the HELL did they PROP the front of the tent UP 4 INCHES HIGHER than the back? And Charlie's chair? Perched on a 2-FOOT PLATFORM above the dirt like a damn target dummy. Candace just confirmed: These "upgrades"? TOTAL FIRSTS. NEVER pulled at ANY prior TPUSA event. Coincidence? Or calculated?

Think about it: Elevate the man, tilt the tent forward—BAM, crystal-clear line of sight for a rooftop sniper or DRONE strike. No obstructions, no mercy. Charlie's head silhouetted like a bullseye. Who greenlit this "setup"? The same crew scrambling SD cards and vanishing boxes? Israel's shadow game just got BRIGHTER.🎯

This isn't coincidence—it's SCRIPTED. Lone gunman my ASS. Demand answers NOW! Who's pulling strings from Tel Aviv? 🔥

Tag @RealCandaceO and RT to UNMASK the TRUTH!

[Irlandarra @aldamu_jo]

The modified mic that struck Charlie Kirk’s neck being discreetly handed off and removed from the crime scene

[Cleanse Parasites .com 🧹🪱 Herbal Cleanse Co. @parasitedetox]

The “Budwig Protocol” oxygenates cells using sulphur based fats - flax oil and cottage cheese while also eating a ketogenic diet. 🪱 hate sulphur. 🤜 🪱

-Dr Pete Soulak

[Cleanse Parasites .com 🧹🪱 Herbal Cleanse Co. @parasitedetox]

Apeginen is a powerful substance found in Parsely, Celery, Chamomile and Oregano. Oregano oil will have it as well. It is a great detoxifier for the kidney’s as well.

Learn about Apeginen below…

[Mesfin D @thenewcushite]

@parasitedetox Protocol

[Mesfin D @thenewcushite]

Agere Habtam Nat አገሬ ሀብታም ናት Mar 6, 2025 https://t.co/m8s5Bl1cWB via @YouTube

[MESFIN DERERIE @MDererie]

እንኳን አደረሳችሁ!

https://t.co/PXGBm2qSHB

[Mesfin D @thenewcushite]

The victory at Adwa will be repeated by voting "yes" to a new Constitution https://t.co/SyysJFWGvd