# https://claude.ai/public/artifacts/ab215f0c-c8d7-4974-a90b-288fe4bb43be

#!/usr/bin/env python3

"""

Select the four corner frames of a drone survey area from aerial video.

Two modes:

  telemetry -- if a DJI-style .SRT sidecar with [latitude]/[longitude] is present,

               the flight track comes straight from GPS (true north-up).

  visual -- otherwise, estimate the track by accumulating frame-to-frame

               similarity transforms (rough visual odometry on the ground plane).

In both modes the track is reduced to its minimum-area rotated rectangle; the

frame nearest each rectangle corner is emitted, ordered bottom-left then

clockwise (BL -> TL -> TR -> BR).

Usage:

    python survey_corners.py VIDEO [-o OUTDIR] [--srt FILE] [--fps 2] [--width 640]

"""

import argparse

import math

import os

import re

import sys

import cv2

import numpy as np

SRT_LAT = re.compile(r"\[latitude\s*:\s*([-\d.]+)\]")

SRT_LON = re.compile(r"\[long(?:i)?tude\s*:\s*([-\d.]+)\]")

# ---------------------------------------------------------------- telemetry

def track_from_srt(path):

    """Return (Nx2 array of local metres, Nx1 array of seconds) or None."""

    text = open(path, "r", errors="ignore").read()

    lats = [float(m) for m in SRT_LAT.findall(text)]

    lons = [float(m) for m in SRT_LON.findall(text)]

    if len(lats) < 4 or len(lats) != len(lons):

        return None

    lat0 = math.radians(np.mean(lats))

    # equirectangular projection, fine over a survey-sized area

    east = (np.array(lons) - np.mean(lons)) * 111320.0 * math.cos(lat0)

    north = (np.array(lats) - np.mean(lats)) * 110540.0

    return np.column_stack([east, north])

# ------------------------------------------------------------------ visual

def track_from_video(cap, sample_fps, width):

    """Accumulate similarity transforms into a rough ground track.

    World frame is aligned to the first frame's heading, so 'bottom-left' is

    relative to the drone's initial orientation, not to true north.

    """

    src_fps = cap.get(cv2.CAP_PROP_FPS) or 30.0

    step = max(1, int(round(src_fps / sample_fps)))

    orb = cv2.ORB_create(1500)

    matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)

    positions, frame_idx = [], []

    pos = np.zeros(2)

    heading = 0.0 # cumulative yaw, radians

    prev_kp = prev_des = None

    scale = None

    i = 0

    while True:

        ok = cap.grab()

        if not ok:

            break

        if i % step:

            i += 1

            continue

        ok, frame = cap.retrieve()

        if not ok:

            break

        if scale is None:

            scale = width / float(frame.shape[1])

        small = cv2.resize(frame, None, fx=scale, fy=scale)

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

        gray = cv2.createCLAHE(2.0, (8, 8)).apply(gray)

        kp, des = orb.detectAndCompute(gray, None)

        if prev_des is not None and des is not None and len(des) > 10:

            matches = matcher.match(prev_des, des)

            if len(matches) >= 12:

                matches = sorted(matches, key=lambda m: m.distance)[:400]

                src = np.float32([prev_kp[m.queryIdx].pt for m in matches])

                dst = np.float32([kp[m.trainIdx].pt for m in matches])

                M, _ = cv2.estimateAffinePartial2D(

                    src, dst, method=cv2.RANSAC, ransacReprojThreshold=3.0

                )

                if M is not None:

                    # scene shift in image space; camera moves the other way

                    dx, dy = -M[0, 2], -M[1, 2]

                    dyaw = math.atan2(M[1, 0], M[0, 0])

                    c, s = math.cos(heading), math.sin(heading)

                    # rotate into world frame, flip y so +y is "up" on the map

                    pos = pos + np.array([c * dx - s * dy, -(s * dx + c * dy)])

                    heading += dyaw

        positions.append(pos.copy())

        frame_idx.append(i)

        prev_kp, prev_des = kp, des

        i += 1

    return np.array(positions), np.array(frame_idx)

# ------------------------------------------------------------------ corners

def order_clockwise_from_bottom_left(box, centre):

    """Order 4 points BL -> TL -> TR -> BR in a y-up coordinate frame."""

    ang = np.array([math.atan2(p[1] - centre[1], p[0] - centre[0]) % (2 * math.pi)

                    for p in box])

    target = 5 * math.pi / 4 # 225 deg = bottom-left

    diff = np.abs((ang - target + math.pi) % (2 * math.pi) - math.pi)

    start = int(np.argmin(diff))

    order = list(np.argsort(-ang)) # clockwise = decreasing angle

    k = order.index(start)

    return [box[j] for j in order[k:] + order[:k]]

def corner_samples(track):

    """Indices into `track` of the four survey-area corners, BL-first clockwise."""

    pts = track.astype(np.float32)

    rect = cv2.minAreaRect(pts)

    box = cv2.boxPoints(rect)

    ordered = order_clockwise_from_bottom_left(box, np.array(rect[0]))

    return [int(np.argmin(np.linalg.norm(pts - c, axis=1))) for c in ordered]

def grab_frame(cap, index):

    cap.set(cv2.CAP_PROP_POS_FRAMES, index)

    ok, frame = cap.read()

    return frame if ok else None

# --------------------------------------------------------------------- main

def main():

    ap = argparse.ArgumentParser()

    ap.add_argument("video")

    ap.add_argument("-o", "--outdir", default="corners")

    ap.add_argument("--srt", help="telemetry sidecar; defaults to VIDEO.srt if present")

    ap.add_argument("--fps", type=float, default=2.0, help="sampling rate")

    ap.add_argument("--width", type=int, default=640, help="analysis width in px")

    args = ap.parse_args()

    cap = cv2.VideoCapture(args.video)

    if not cap.isOpened():

        sys.exit(f"cannot open {args.video}")

    total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))

    srt = args.srt or os.path.splitext(args.video)[0] + ".srt"

    track = track_from_srt(srt) if os.path.exists(srt) else None

    if track is not None:

        mode = "telemetry"

        frame_idx = np.linspace(0, max(total - 1, 0), len(track)).astype(int)

    else:

        mode = "visual"

        track, frame_idx = track_from_video(cap, args.fps, args.width)

        if len(track) < 8:

            sys.exit("not enough usable frames to estimate a track")

    picks = corner_samples(track)

    labels = ["1-bottom-left", "2-top-left", "3-top-right", "4-bottom-right"]

    os.makedirs(args.outdir, exist_ok=True)

    print(f"mode: {mode} samples: {len(track)}")

    for label, s in zip(labels, picks):

        fi = int(frame_idx[s])

        frame = grab_frame(cap, fi)

        if frame is None:

            print(f"{label}: frame {fi} unreadable")

            continue

        out = os.path.join(args.outdir, f"{label}.jpg")

        cv2.imwrite(out, frame)

        t = fi / (cap.get(cv2.CAP_PROP_FPS) or 30.0)

        print(f"{label}: frame {fi} t={t:7.2f}s xy=({track[s][0]:.1f}, {track[s][1]:.1f}) -> {out}")

    cap.release()

if __name__ == "__main__":

    main()

 

# Results:

mode: visual samples: 1100

1-bottom-left: frame 4065 t= 135.50s xy=(-252.0, -1591.2) -> corners\1-bottom-left.jpg

2-top-left: frame 2475 t= 82.50s xy=(-1809.0, 317.5) -> corners\2-top-left.jpg

3-top-right: frame 1275 t= 42.50s xy=(-371.8, 1783.0) -> corners\3-top-right.jpg

4-bottom-right: frame 4560 t= 152.00s xy=(261.0, -695.0) -> corners\4-bottom-right.jpg

   

  

 Pangram Model Architecture and Multi-Objective Training

The underlying software architecture has transitioned across iterations to support increasingly complex text inputs. Its modern core (manifested in Pangram 4) is built upon a large, open-weight Mixture of Experts (MoE) backbone model adapted for sequence classification. The system attaches independent, custom linear classification heads to the final sequence position of the shared backbone, exploiting causal attention mechanisms where the final hidden state vector ($\mathbf{h}_S$) retains a complete contextual representation of the input window.

The system achieves granular, single-pass evaluation by simultaneously optimizing for multiple objectives across distinct classification heads:

• 

• Segment-Level Edits: Evaluates localized adjustments within the passage.

• Mixed-Authorship Binary Classification: Determines whether a document is fully human-written, fully AI-generated, or hybrid.

• Humanizer Detection: Flags signatures typical of commercial obfuscation or adversarial paraphrasing algorithms.

• Tokenwise Provenance: Projects predictions down to individual token positions using a localized sequence head. To allow every supervised token to utilize context from the complete source sequence under a causal backbone, the framework implements a context replication format known as Repeat2, where each 512-token training window is repeated twice and loss calculations are applied only to the second instance.

• 

The software stack utilizes standard deep learning frameworks, specifically PyTorch and Hugging Face libraries, optimized via Parameter-Efficient Fine-Tuning (PEFT) methodologies like Low-Rank Adaptation (LoRA). Training is performed across distributed clusters of hardware, such as NVIDIA H100 GPUs, to handle the vast parameter scale required to map modern frontier models.

Prediction Mechanics and Platform Integration

When raw text is passed to the platform via its user interface or REST API, the system does not emit an arbitrary boolean result. Instead, it tokenizes the string, maps the tokens to vector embeddings, and processes them through the neural network to output continuous numerical scores representing spatial coordinates in "Pangram Space".

The system segmentizes documents longer than a specific threshold (e.g., 450 tokens) to assess moving windows individually. The resulting output maps text into calibrated probabilistic thresholds:

Score Range Classification Category

$\le 0.25$ Human-Written

$0.25 < \text{Score} < 0.50$ Lightly AI-Assisted

$0.50 \le \text{Score} < 0.75$ Moderately AI-Assisted

$\ge 0.75$ Fully AI-Generated

The platform's software engineering emphasizes broad downstream availability to achieve its mission of content validation across the broader internet ecosystem. The core model is exposed via high-throughput API endpoints priced dynamically by word count metrics. To operationalize these capabilities directly within existing workflows, the software is deployed via deep software integrations into learning management systems (such as Canvas LMS and Google Classroom), digital publishing platforms (such as Substack), browser extensions for real-time web monitoring, and document verification add-ons like Google Docs.

Through this multi-tiered implementation—spanning structured data engineering, sophisticated multi-head transformer architectures, and extensive platform integrations—Pangram establishes a deterministic legibility framework to handle the challenges of mixed-authorship content at scale.

 Pixel-Level Distribution Analysis in Image Detection

When expanding into visual provenance via Pangram Image, the platform abandons high-level semantic analysis in favor of tracking low-level pixel distribution anomalies. Generative adversarial networks (GANs) and diffusion models (such as Midjourney, Stable Diffusion, or DALL-E) synthesize images through mathematical upscaling or iterative denoising passes. While these techniques generate visually convincing global semantics, they introduce artificial artifacts into the high-frequency spatial frequencies and statistical structures of local pixel values. [6]

1. Statistical Discontinuity Tracking

Human-captured digital imagery is fundamentally shaped by physical sensor physics, including photon shot noise and continuous lens-transfer functions. Conversely, AI-generated images exhibit uniform statistical patterns or unnatural discontinuities due to structural upsampling operators (such as transposed convolutions). Pangram’s image model analyzes these distributions across three vectors:

                 ┌──► Spatial Co-occurrence Matrix (Pixel-to-neighbor joint distribution)

                 │

[Input Image] ───┼──► Discrete Cosine Transform (DCT) (Fourier-domain frequency profiling)

                 │

                 └──► Local Variance Descriptors (Chrominance-to-luminance edge behavior)

● Spatial Co-occurrence Matrices: The software computes joint probability distributions between neighboring pixel intensities across localized windows. Synthetic images frequently display overly uniform pixel transitions or grid-like periodicities that do not occur in natural sensor noise.

● Fourier and Frequency Domain Profiling: Images undergo automated Discrete Cosine Transforms (DCT) to isolate high-frequency bands. Diffusion models leave structural footprints—often referred to as "spectral scars"—visible as anomalous energy spikes at specific coordinates within the high-frequency spectrum.

● Chrominance-Luminance Cross-Correlation: Natural camera lenses disperse light across the RGB channels according to predictable optical behaviors (chromatic aberration). Generative software pipelines calculate these channels independently or via mathematical abstractions, creating distinct, unaligned edge-variance profiles between luminance and chrominance boundaries.

2. Robustness to Downstream Processing

To prevent these pixel-level signatures from being erased by basic compression or resizing algorithms, the classifier head is co-trained using differentiable data augmentation layers. The network learns to map local spatial variances to a normalized invariant space, ensuring the detection architecture maintains a high accuracy ceiling even when images are compressed for digital distribution or social platform publishing.

 Algorithmic Architecture and Software Implementation of the Pangram Platform 

The rapid growth of generative artificial intelligence has fundamentally altered how information is curated and presented, introducing risks associated with automated misinformation, search engine optimization (SEO) content inflation, and challenges to academic integrity. To mitigate these systemic pressures, Pangram Labs has developed a specialized software platform designed to accurately classify text and media provenance. The core mission of the organization—ensuring that powerful language models function as a net positive by introducing transparency to content generation—is executed through a highly robust software implementation. Rather than relying on fragile heuristics like hidden watermarks or basic perplexity metrics, Pangram implements an architectural framework built around dense sequence classification, specialized deep learning training loops, and granular multi-objective inference. 

Data Engineering and "Synthetic Mirroring" 

A fundamental prerequisite for high-accuracy text classification is the quality and structure of the training dataset. Traditional detection algorithms frequently suffer from high false-positive rates due to distribution shifts between human-authored text and the synthetic datasets used for training. Pangram addresses this through a proprietary data pipeline methodology known as hard negative mining with synthetic mirrors. 

1. Contextual Isolation: The software pipeline ingests a corpus of commercially licensed, verified human-written documents primarily sourced from 2021 and earlier to eliminate the risk of post-generative data poisoning. 

2. Generative Pairing: For every human-authored artifact, the system programmatically prompts frontier large language models (LLMs) to construct a "synthetic mirror"—an AI-generated text that preserves the identical length, tone, topic, and semantic intent of the original human text. 

3. Boundary Refinement: By optimizing on these tightly coupled human-AI text pairs, the system learns to map the subtle, high-dimensional boundaries of stylistic decision-making rather than shallow vocabulary choices. 

To reinforce this against adversarial attacks and "humanizer" tools designed to obfuscate AI artifacts, Pangram employs hard negative mining. The automated training infrastructure searches incoming datasets for false positives, dynamically creates synthetic mirrors of those specific failure modes, and re-injects them into the training loop, thereby programmatically lowering the platform's baseline error rate over successive iterations.  #codingexercise: CodingExercise-09-06-2026.docx

What Pangram Labs Can Teach DVSA‑API

 Pangram Labs’ approach to building cloud‑native analytics systems suggests that AI systems only create value when they unify data, models, and workflows into a single operational fabric. Their deployments show that multi‑modal intelligence—whether over financial documents, industrial sensor logs, or supply‑chain telemetry—requires more than model performance. It requires coherence, governance, and context. For dvsa‑api, which integrates video ingestion, classical CV routines, ONNX detectors, reasoning models, Azure indexing, and multi‑agent workflows, these translate to a set of lessons. 

The first lesson is that multi‑modal fusion must be a first‑class architectural principle. Pangram Labs consistently emphasizes that enterprises rarely operate on a single modality. They combine text, tables, logs, sensor readings, and images into unified analytical objects. dvsa‑api already handles video frames, geospatial metadata, detections, commentary events, and agent actions, but Pangram’s approach suggests pushing further: treat every mission as a multi‑modal object with structured relationships between modalities. This means building a unified schema where frames, detections, reasoning outputs, and external data sources (weather, maps, flight telemetry) are linked. The benefit is not aesthetic; it is operational. Multi‑modal fusion increases interpretability, improves agent decision‑making, and reduces the risk of misaligned outputs. 

A second lesson is that context engineering is more important than model engineering. Pangram Labs’ systems succeed because they embed models inside rich contextual pipelines—preprocessing, normalization, semantic linking, and post‑processing that make raw outputs meaningful. dvsa‑api already has deterministic preprocessing, tiling/NMS merging, and structured commentary events, but Pangram’s work suggests elevating context to a governing layer. For example, detections should be contextualized with altitude, camera angle, mission type, and historical patterns. Reasoning outputs should reference mission timelines, prior detections, and agent actions. This transforms dvsa‑api from a detection engine into a contextual intelligence platform. 

The third lesson is that governance and lineage must be built into the system, not added later. Pangram Labs emphasizes traceability: every transformation, model invocation, and workflow step is logged, versioned, and auditable. dvsa‑api has the beginnings of this through its observability subsystem and commentary events, but Pangram’s deployments suggest making lineage a core product feature. A unified evidence ledger—tracking frames, model versions, reasoning steps, agent decisions, and mission outcomes—would increase trust and make dvsa‑api suitable for regulated environments. This aligns with dvsa‑api’s long‑term goal of becoming a platform for enterprise‑grade aerial intelligence. 

A fourth lesson is that AI must be embedded directly into operational workflows. Pangram Labs builds systems where models trigger actions, update dashboards, and participate in real‑time decision loops. dvsa‑api’s Multi‑Agent Control Plane already reflects this philosophy, but Pangram’s experience suggests strengthening the integration between analytics and operations. For example, detections should automatically trigger agent workflows; reasoning outputs should update mission state; and agent actions should feed back into analytics. This closed‑loop architecture is essential for real‑time drone missions where latency and reliability matter. 

A fifth lesson is that deployment flexibility is a competitive advantage. Pangram Labs deploys systems across cloud, hybrid, and on‑prem environments with consistent behavior. dvsa‑api’s agent kits—local interactive, cloud autonomous, orchestration, and integrations—mirror this approach, but Pangram’s work suggests formalizing deployment contracts. Deterministic behavior across environments, consistent model loading, reproducible pipelines, and environment‑agnostic agent workflows increase reliability and reduce operational friction. This is especially important for drone systems that may operate in constrained or disconnected environments. 

The final lesson is that enterprise adoption depends on clarity, not complexity. Pangram Labs succeeds because they present complex multi‑modal systems through simple abstractions: unified objects, clear workflows, and intuitive dashboards. dvsa‑api can adopt the same strategy. Mission timelines, unified frame viewers, model registries, agent workflow editors, and structured reasoning outputs make the system approachable for operators, analysts, and supervisors. The goal is not to hide complexity but to make it navigable. 

Taken together, Pangram Labs teaches dvsa‑api that the future of aerial intelligence is not defined by model performance alone. It is defined by multi‑modal fusion, contextual governance, workflow integration, deployment consistency, and clarity of experience. dvsa‑api already embodies many of these principles; the next step is to formalize them into a coherent, enterprise‑grade platform that treats drone missions as structured, governed, multi‑modal intelligence workflows rather than isolated analytics tasks. 

#codingexercise:

Problem: Count integers appearing in a single block:

You are given an integer array nums. An integer x is special if all occurrences of x in nums appear in a single contiguous block. Return the number of distinct special integers in nums.

Solution:

class Solution {

    public int countSpecialIntegers(int[] nums) {

        int count = 0;

        for (int i = 0; i < nums.length; i++) {

            if (i > 0 && nums[i] == nums[i-1]) continue;

            int contiguous = 0;

            for (int j = i; j < nums.length; j++) {

                if (nums[j] == nums[i]) {

                    contiguous++;

                    if (j > 0 && j != i && nums[j] != nums[j-1]) {

                        contiguous = -1;

                        break;

                    }

                }

            }

            for (int j = i-1; j >= 0; j--) {

                if (nums[j] == nums[i]) {

                    contiguous++;

                    if (j+1 < nums.length && j != i && nums[j] != nums[j+1]) {

                        contiguous = -1;

                        break;

                    }

                }

            }

            if (contiguous != -1) count++;

        }

        return count;

    }

}