mirror of
https://github.com/kuhyx/testsAndMisc-archive.git
synced 2026-07-04 17:03:08 +02:00
Krzysztof kuhy Rudnicki
27a1ef634c
Split 16+ files. 27 files still need splitting. See session notes.
240 lines
8.5 KiB
Python
240 lines
8.5 KiB
Python
"""NMS/IoU, detector-from-classifier, and methods comparison."""
|
|
|
|
from __future__ import annotations
|
|
|
|
from _q24_common import (
|
|
BG_COLOR,
|
|
FONT_B,
|
|
FONT_R,
|
|
FPS,
|
|
STEP_DUR,
|
|
H,
|
|
W,
|
|
_tc,
|
|
_text_slide,
|
|
)
|
|
from moviepy import ColorClip, CompositeVideoClip, VideoClip
|
|
from moviepy.video.fx import FadeIn, FadeOut
|
|
import numpy as np
|
|
|
|
|
|
# ── NMS + IoU ─────────────────────────────────────────────────────
|
|
def _nms_iou_demo() -> list[CompositeVideoClip]:
|
|
"""Animate NMS and IoU concepts."""
|
|
slides = []
|
|
|
|
def make_nms_frame(t: float) -> np.ndarray:
|
|
frame = np.zeros((H, W, 3), dtype=np.uint8)
|
|
frame[:] = BG_COLOR
|
|
|
|
progress = min(t / (STEP_DUR * 0.7), 1.0)
|
|
|
|
# Draw overlapping bounding boxes
|
|
ox, oy = 100, 200
|
|
obj_w, obj_h = 150, 120
|
|
|
|
# Multiple overlapping detections for same object
|
|
boxes = [
|
|
(ox, oy, obj_w, obj_h, 0.95, (255, 80, 80)), # best
|
|
(ox + 15, oy - 10, obj_w + 10, obj_h + 5, 0.90, (200, 60, 60)),
|
|
(ox - 10, oy + 5, obj_w - 5, obj_h + 10, 0.85, (160, 50, 50)),
|
|
]
|
|
# Different object far away
|
|
boxes.append((ox + 350, oy + 50, 100, 100, 0.40, (80, 180, 255)))
|
|
|
|
for i, (bx, by, bw, bh, _conf, color) in enumerate(boxes):
|
|
dc = color
|
|
nms_phase = 0.4
|
|
nms_limit = 3
|
|
if progress > nms_phase and i > 0 and i < nms_limit:
|
|
# After NMS, these get removed (shown as faded/crossed)
|
|
dc = (60, 40, 40)
|
|
|
|
for tt in range(2):
|
|
frame[by - tt : by + bh + tt, bx - tt : bx - tt + 2] = dc
|
|
frame[by - tt : by + bh + tt, bx + bw + tt - 2 : bx + bw + tt] = dc
|
|
frame[by - tt : by - tt + 2, bx - tt : bx + bw + tt] = dc
|
|
frame[by + bh + tt - 2 : by + bh + tt, bx - tt : bx + bw + tt] = dc
|
|
|
|
# IoU visualization on right side
|
|
iou_x, iou_y = 700, 200
|
|
# Box A
|
|
frame[iou_y : iou_y + 100, iou_x : iou_x + 100] = (80, 80, 200)
|
|
# Box B (overlapping)
|
|
frame[iou_y + 40 : iou_y + 140, iou_x + 40 : iou_x + 140] = (200, 80, 80)
|
|
# Intersection highlighted
|
|
frame[iou_y + 40 : iou_y + 100, iou_x + 40 : iou_x + 100] = (200, 150, 200)
|
|
|
|
return frame
|
|
|
|
nms_clip = VideoClip(make_nms_frame, duration=STEP_DUR).with_fps(FPS)
|
|
text_clips: list[VideoClip] = [nms_clip]
|
|
labels = [
|
|
("NMS (Non-Maximum Suppression) + IoU", 28, "#FFE082", FONT_B, (80, 20)),
|
|
(
|
|
"NMS = Najlepszy Ma Się dobrze — zachowaj najlepszą, usuń duplikaty",
|
|
18,
|
|
"#B0BEC5",
|
|
FONT_R,
|
|
(80, 65),
|
|
),
|
|
("conf=0.95 ✓", 14, "#A5D6A7", FONT_B, (100, 340)),
|
|
("0.90 ✗ IoU>0.5", 13, "#EF9A9A", FONT_R, (100, 365)),
|
|
("0.85 ✗ IoU>0.5", 13, "#EF9A9A", FONT_R, (100, 390)),
|
|
("0.40 ✓ INNY obiekt", 13, "#64B5F6", FONT_R, (100, 420)),
|
|
("IoU = Intersection over Union", 18, "#FFE082", FONT_B, (700, 160)),
|
|
("IoU = pole(∩) / pole(AUB)", 16, "white", FONT_R, (700, 380)),
|
|
("Fioletowy = intersection", 14, "#CE93D8", FONT_R, (700, 410)),
|
|
("IoU > 0.5 → TEN SAM obiekt → usuń", 14, "#EF9A9A", FONT_R, (700, 440)),
|
|
("IoU < 0.5 → INNY obiekt → zachowaj", 14, "#A5D6A7", FONT_R, (700, 470)),
|
|
(
|
|
"DETR: jedyny detektor BEZ NMS (Hungarian matching zamiast tego)",
|
|
14,
|
|
"#78909C",
|
|
FONT_R,
|
|
(80, 620),
|
|
),
|
|
]
|
|
for text, fs, color, font, pos in labels:
|
|
tc = (
|
|
_tc(text=text, font_size=fs, color=color, font=font)
|
|
.with_duration(STEP_DUR)
|
|
.with_position(pos)
|
|
)
|
|
text_clips.append(tc)
|
|
|
|
slides.append(
|
|
CompositeVideoClip(text_clips, size=(W, H)).with_effects(
|
|
[FadeIn(0.3), FadeOut(0.3)]
|
|
)
|
|
)
|
|
return slides
|
|
|
|
|
|
# ── Detector from Classifier ─────────────────────────────────────
|
|
def _detector_from_classifier() -> list[CompositeVideoClip]:
|
|
"""Show 3 approaches to building a detector from a classifier."""
|
|
slides = []
|
|
|
|
approaches = [
|
|
(
|
|
"Podejście 1: Sliding Window (NAJWOLNIEJSZE)",
|
|
[
|
|
("Okno przesuwa się po obrazie w wielu skalach", "#B0BEC5"),
|
|
("Każde okno → klasyfikator (np. ResNet) → klasa + pewność", "#B0BEC5"),
|
|
("~18 000 okien x 10ms = ~3 minuty na obraz!", "#EF9A9A"),
|
|
("Mnemonik: WYCINAJ i PYTAJ — jak wycinanie ciasteczek", "#FFE082"),
|
|
],
|
|
"SRF",
|
|
),
|
|
(
|
|
"Podejście 2: Region Proposals (= R-CNN)",
|
|
[
|
|
("Selective Search → ~2000 inteligentnych regionów", "#B0BEC5"),
|
|
("Każdy region → CNN → wektor cech → SVM klasyfikuje", "#B0BEC5"),
|
|
("~2000 x 10ms = ~20 sec — 9x szybciej!", "#64B5F6"),
|
|
(
|
|
"Mnemonik: INTELIGENTNE CIĘCIE — wytnij tylko tam gdzie wiśnie",
|
|
"#FFE082",
|
|
),
|
|
],
|
|
"SRF",
|
|
),
|
|
(
|
|
"Podejście 3: Fine-tune backbone (NAJLEPSZE)",
|
|
[
|
|
(
|
|
"Pretrained backbone (ResNet) → odetnij FC → dodaj detection head",
|
|
"#B0BEC5",
|
|
),
|
|
(
|
|
"Detection head = głowica klasyfikacji + głowica regresji bbox",
|
|
"#B0BEC5",
|
|
),
|
|
("~0.2 sec/obraz, najlepsza jakość (mAP ~42%)", "#A5D6A7"),
|
|
("Mnemonik: PRZESZCZEP GŁOWY — ten sam silnik, nowa głowa", "#FFE082"),
|
|
],
|
|
"SRF",
|
|
),
|
|
]
|
|
|
|
for title, points, _mnem in approaches:
|
|
lines = [
|
|
(title, 24, "#FFE082", FONT_B, (80, 140)),
|
|
]
|
|
for i, (text, color) in enumerate(points):
|
|
lines.append((f"• {text}", 18, color, FONT_R, (100, 220 + i * 50)))
|
|
|
|
lines.append(
|
|
(
|
|
"Detektor z klasyfikatora: SRF = Sliding → Region → Fine-tune",
|
|
16,
|
|
"#78909C",
|
|
FONT_R,
|
|
(80, 520),
|
|
)
|
|
)
|
|
lines.append(
|
|
(
|
|
"= Szukaj Ręcznie, Finalnie optymalizuj!",
|
|
16,
|
|
"#90CAF9",
|
|
FONT_R,
|
|
(80, 550),
|
|
)
|
|
)
|
|
|
|
slides.append(_text_slide(lines, duration=STEP_DUR))
|
|
|
|
return slides
|
|
|
|
|
|
# ── Methods comparison ────────────────────────────────────────────
|
|
def _methods_comparison() -> CompositeVideoClip:
|
|
"""Create a comparison table of all detection methods."""
|
|
bg = ColorClip(size=(W, H), color=BG_COLOR).with_duration(10.0)
|
|
title = (
|
|
_tc(
|
|
text="Porównanie detektorów",
|
|
font_size=36,
|
|
color="white",
|
|
font=FONT_B,
|
|
)
|
|
.with_duration(10.0)
|
|
.with_position(("center", 20))
|
|
)
|
|
|
|
rows = [
|
|
("Model", "Rok", "Typ", "Szybkość", "Kluczowe"),
|
|
("HOG+SVM", "2005", "Klasyczny", "~1 fps", "Gradient histogramy"),
|
|
("Viola-Jones", "2001", "Klasyczny", "30+ fps", "Haar+Cascade"),
|
|
("R-CNN", "2014", "Two-stage", "50 sec!", "CNN per region"),
|
|
("Fast R-CNN", "2015", "Two-stage", "2 sec", "ROI Pooling"),
|
|
("Faster R-CNN", "2015", "Two-stage", "5 fps", "RPN w sieci"),
|
|
("YOLO", "2016", "One-stage", "45+ fps", "Siatka SxS"),
|
|
("DETR", "2020", "Transformer", "~40 fps", "Bez NMS!"),
|
|
]
|
|
|
|
clips: list[VideoClip] = [bg, title]
|
|
for i, row in enumerate(rows):
|
|
y_pos = 75 + i * 72
|
|
col_x = [40, 200, 280, 400, 530]
|
|
for j, cell in enumerate(row):
|
|
fs = 16 if i > 0 else 18
|
|
color = "#64B5F6" if i == 0 else "#E0E0E0"
|
|
tc = (
|
|
_tc(
|
|
text=cell,
|
|
font_size=fs,
|
|
color=color,
|
|
font=FONT_B if i == 0 else FONT_R,
|
|
)
|
|
.with_duration(10.0)
|
|
.with_position((col_x[j], y_pos))
|
|
)
|
|
clips.append(tc)
|
|
|
|
return CompositeVideoClip(clips, size=(W, H)).with_effects(
|
|
[FadeIn(0.5), FadeOut(0.5)]
|
|
)
|