mirror of
https://github.com/kuhyx/testsAndMisc.git
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Krzysztof kuhy Rudnicki
c985160d17
Split 16+ files. 27 files still need splitting. See session notes.
333 lines
11 KiB
Python
333 lines
11 KiB
Python
"""Classical detection methods: detection concept, HOG+SVM, Viola-Jones."""
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from __future__ import annotations
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from _q24_common import (
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BG_COLOR,
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FONT_B,
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FONT_R,
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FPS,
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STEP_DUR,
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H,
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W,
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_tc,
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)
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from moviepy import CompositeVideoClip, VideoClip
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from moviepy.video.fx import FadeIn, FadeOut
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import numpy as np
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# ── Detection concept ────────────────────────────────────────────
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def _detection_concept() -> list[CompositeVideoClip]:
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"""Show what detection is: bounding box + class + confidence."""
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slides = []
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def make_det_frame(_t: float) -> np.ndarray:
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frame = np.zeros((H, W, 3), dtype=np.uint8)
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frame[:] = BG_COLOR
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# Draw a "scene" with colored rectangles representing objects
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# Sky background area
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frame[140:500, 100:700] = (40, 50, 70)
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# "Car" object
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frame[350:430, 150:320] = (180, 60, 60)
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# "Person" object
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frame[280:440, 450:520] = (60, 120, 180)
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# "Tree" object
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frame[200:400, 580:650] = (40, 130, 50)
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# Bounding boxes (with labels drawn as colored borders)
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# Car bbox
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for thickness in range(3):
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t = thickness
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frame[348 - t : 432 + t, 148 - t : 148 - t + 2] = (255, 80, 80)
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frame[348 - t : 432 + t, 322 + t - 2 : 322 + t] = (255, 80, 80)
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frame[348 - t : 348 - t + 2, 148 - t : 322 + t] = (255, 80, 80)
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frame[432 + t - 2 : 432 + t, 148 - t : 322 + t] = (255, 80, 80)
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# Person bbox
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for thickness in range(3):
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t = thickness
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frame[278 - t : 442 + t, 448 - t : 448 - t + 2] = (80, 180, 255)
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frame[278 - t : 442 + t, 522 + t - 2 : 522 + t] = (80, 180, 255)
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frame[278 - t : 278 - t + 2, 448 - t : 522 + t] = (80, 180, 255)
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frame[442 + t - 2 : 442 + t, 448 - t : 522 + t] = (80, 180, 255)
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# Tree bbox
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for thickness in range(3):
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t = thickness
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frame[198 - t : 402 + t, 578 - t : 578 - t + 2] = (80, 220, 100)
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frame[198 - t : 402 + t, 652 + t - 2 : 652 + t] = (80, 220, 100)
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frame[198 - t : 198 - t + 2, 578 - t : 652 + t] = (80, 220, 100)
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frame[402 + t - 2 : 402 + t, 578 - t : 652 + t] = (80, 220, 100)
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# Comparison boxes on right side
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# Classification
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frame[180:260, 800:1150] = (35, 45, 65)
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# Detection
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frame[290:370, 800:1150] = (35, 45, 65)
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# Segmentation
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frame[400:480, 800:1150] = (35, 45, 65)
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return frame
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det_clip = VideoClip(make_det_frame, duration=STEP_DUR).with_fps(FPS)
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text_clips: list[VideoClip] = [det_clip]
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labels = [
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("Detekcja obiektów — co to jest?", 28, "#FFE082", FONT_B, (100, 20)),
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("Wynik: (klasa, bounding box, pewność)", 20, "#B0BEC5", FONT_R, (100, 65)),
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("samochód 95%", 14, "#EF9A9A", FONT_B, (150, 340)),
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("osoba 88%", 14, "#64B5F6", FONT_B, (450, 268)),
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("drzewo 72%", 14, "#A5D6A7", FONT_B, (580, 188)),
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("Klasyfikacja: cały obraz → 1 etykieta", 15, "#78909C", FONT_R, (810, 210)),
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("Detekcja: bbox + klasa + pewność", 15, "#FFE082", FONT_R, (810, 320)),
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("Segmentacja: maska per piksel", 15, "#78909C", FONT_R, (810, 430)),
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("← granulacja rośnie →", 14, "#90CAF9", FONT_R, (810, 520)),
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]
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for text, fs, color, font, pos in labels:
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tc = (
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_tc(text=text, font_size=fs, color=color, font=font)
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.with_duration(STEP_DUR)
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.with_position(pos)
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)
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text_clips.append(tc)
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slides.append(
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CompositeVideoClip(text_clips, size=(W, H)).with_effects(
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[FadeIn(0.3), FadeOut(0.3)]
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)
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)
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return slides
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# ── HOG + SVM pipeline ───────────────────────────────────────────
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def _hog_svm_demo() -> list[CompositeVideoClip]:
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"""Animate HOG feature computation and SVM classification."""
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slides = []
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def make_hog_frame(t: float) -> np.ndarray:
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frame = np.zeros((H, W, 3), dtype=np.uint8)
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frame[:] = BG_COLOR
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progress = min(t / (STEP_DUR * 0.8), 1.0)
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# Pipeline stages as boxes with arrows
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stages = [
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("Gradient", (80, 250), (130, 80), (100, 160, 220)),
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("Orientacja", (260, 250), (130, 80), (80, 180, 140)),
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("Komórki 8x8", (440, 250), (130, 80), (200, 160, 80)),
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("Bloki 2x2", (620, 250), (130, 80), (200, 120, 60)),
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("Normalizacja", (800, 250), (130, 80), (180, 100, 80)),
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("SVM", (980, 250), (130, 80), (220, 80, 80)),
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]
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n_active = int(progress * len(stages)) + 1
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for i, (_label, (sx, sy), (sw, sh), color) in enumerate(stages):
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if i < n_active:
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frame[sy : sy + sh, sx : sx + sw] = color
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# Border
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frame[sy : sy + 2, sx : sx + sw] = tuple(
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min(c + 60, 255) for c in color
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)
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frame[sy + sh - 2 : sy + sh, sx : sx + sw] = tuple(
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min(c + 60, 255) for c in color
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)
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# Arrow to next
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if i < len(stages) - 1:
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ax = sx + sw + 5
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ay = sy + sh // 2
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frame[ay - 1 : ay + 2, ax : ax + 20] = (150, 150, 170)
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# Show gradient computation example at bottom
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gradient_phase = 0.2
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if progress > gradient_phase:
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# Mini pixel grid showing gradient computation
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gx, gy = 100, 430
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pixels = [50, 50, 200]
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for idx, val in enumerate(pixels):
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x = gx + idx * 50
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frame[gy : gy + 40, x : x + 40] = (val, val, val)
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return frame
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hog_clip = VideoClip(make_hog_frame, duration=STEP_DUR).with_fps(FPS)
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text_clips: list[VideoClip] = [hog_clip]
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labels = [
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("HOG + SVM — pipeline detekcji pieszych", 28, "#FFE082", FONT_B, (80, 20)),
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(
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"Mnemonik: GOKBN = Gradienty→Orientacja→Komórki→Bloki→Normalizacja",
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16,
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"#A5D6A7",
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FONT_R,
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(80, 65),
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),
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("Gradient: siła i kierunek zmiany jasności", 14, "#64B5F6", FONT_R, (80, 95)),
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(
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"Histogram: 9 binów (0°-180°, co 20°) per komórka 8x8",
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14,
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"#78909C",
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FONT_R,
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(80, 120),
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),
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(
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"[50][50][200] → Gx = 200-50 = 150 = silna krawędź!",
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16,
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"#EF9A9A",
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FONT_R,
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(80, 490),
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),
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(
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"Wektor HOG (3780 cech) → SVM: pieszy (+1) / tło (-1)",
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16,
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"white",
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FONT_R,
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(80, 540),
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),
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(
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"Sliding window 64x128 przesuwa się po obrazie → NMS → wynik",
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16,
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"#90CAF9",
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FONT_R,
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(80, 580),
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),
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(
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"SVM = LINIA MAKSYMALNEGO ODDECHU (max margines, support vectors)",
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16,
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"#FFE082",
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FONT_R,
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(80, 620),
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),
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]
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for text, fs, color, font, pos in labels:
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tc = (
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_tc(text=text, font_size=fs, color=color, font=font)
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.with_duration(STEP_DUR)
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.with_position(pos)
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)
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text_clips.append(tc)
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slides.append(
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CompositeVideoClip(text_clips, size=(W, H)).with_effects(
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[FadeIn(0.3), FadeOut(0.3)]
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)
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)
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return slides
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# ── Viola-Jones ───────────────────────────────────────────────────
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def _viola_jones_demo() -> list[CompositeVideoClip]:
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"""Animate Viola-Jones cascade concept."""
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slides = []
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def make_cascade_frame(t: float) -> np.ndarray:
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frame = np.zeros((H, W, 3), dtype=np.uint8)
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frame[:] = BG_COLOR
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progress = min(t / (STEP_DUR * 0.8), 1.0)
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# Draw cascade "funnel" — stages filtering out non-faces
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stages = 5
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start_width = 1000
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start_count = 10000
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x_center = W // 2
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for i in range(stages):
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stage_progress = min(progress * stages - i, 1.0)
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if stage_progress <= 0:
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break
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width = int(start_width * (1 - i * 0.18))
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int(start_count * (0.3**i))
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y = 150 + i * 100
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h_box = 60
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# Stage box
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x1 = x_center - width // 2
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frame[y : y + h_box, x1 : x1 + width] = (
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50 + i * 10,
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60 + i * 10,
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80 + i * 10,
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)
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# Border
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frame[y : y + 2, x1 : x1 + width] = (100 + i * 20, 130 + i * 15, 200)
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frame[y + h_box - 2 : y + h_box, x1 : x1 + width] = (
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100 + i * 20,
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130 + i * 15,
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200,
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)
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# Arrow down to next
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if i < stages - 1:
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frame[y + h_box + 5 : y + h_box + 25, x_center - 1 : x_center + 2] = (
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150,
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150,
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170,
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)
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# Red "rejected" arrows on sides
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if i > 0:
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# Left reject arrow
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rx = x1 - 30
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ry = y + h_box // 2
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frame[ry - 1 : ry + 2, rx : rx + 25] = (200, 80, 80)
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return frame
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cascade_clip = VideoClip(make_cascade_frame, duration=STEP_DUR).with_fps(FPS)
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text_clips: list[VideoClip] = [cascade_clip]
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labels = [
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(
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"Viola-Jones — kaskada klasyfikatorów (2001)",
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28,
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"#FFE082",
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FONT_B,
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(80, 20),
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),
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(
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"3 innowacje: HIC = Haar + Integral Image + Cascade",
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20,
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"#B0BEC5",
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FONT_R,
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(80, 65),
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),
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("Etap 1: 2 cechy Haar", 14, "#64B5F6", FONT_R, (170, 170)),
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("Etap 2: 10 cech", 14, "#64B5F6", FONT_R, (210, 270)),
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("Etap 3: 25 cech", 14, "#64B5F6", FONT_R, (240, 370)),
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("Etap 4: 50 cech", 14, "#64B5F6", FONT_R, (260, 470)),
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("→ TWARZ!", 16, "#A5D6A7", FONT_B, (590, 560)),
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(
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"SITO: 99% okien odpada w pierwszych 3 etapach → REAL-TIME!",
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16,
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"#EF9A9A",
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FONT_R,
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(80, 620),
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),
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(
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"Haar: kontrast jasna/ciemna | Integral Image: "
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"suma prostokąta O(1) = 4 odczyty",
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14,
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"#78909C",
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FONT_R,
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(80, 655),
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),
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("odrzucone →", 12, "#EF9A9A", FONT_R, (60, 275)),
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("odrzucone →", 12, "#EF9A9A", FONT_R, (60, 375)),
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]
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for text, fs, color, font, pos in labels:
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tc = (
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_tc(text=text, font_size=fs, color=color, font=font)
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.with_duration(STEP_DUR)
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.with_position(pos)
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)
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text_clips.append(tc)
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slides.append(
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CompositeVideoClip(text_clips, size=(W, H)).with_effects(
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[FadeIn(0.3), FadeOut(0.3)]
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)
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)
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return slides
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