Day 74: Combining detection + pose + segmentation + parsing
The full four-model pipeline
Assemble all four components into one flow: detect the person → estimate pose → segment the cutout → parse regions → fuse into measurements. Because you deployed and tested each stage as you built it, today is orchestration, not firefighting. The output is a rich, multi-source measurement set, each dimension backed by one or more models and a confidence.
def measurement_pipeline(img_bgr):
person = detect_person(img_bgr)
if person is None:
return {"error": "no_person"}
crop = crop_to(img_bgr, person)
pose = estimate_pose(crop) # keypoints
mask = segment_person(crop, person) # silhouette
parsing = parse_regions(crop, mask) # body/clothing regions
return fuse_measurements(pose, mask, parsing) # multi-source + confidenceThis is what "pipelines, not phase-lists" means
The roadmap's design decision #4 was to build detection/pose/segmentation/parsing as one Measurement Pipeline, the way production systems are built — not four separate tutorials. Today that pays off: the components were designed from the start to feed each other, so assembly is clean. That systems-first framing is exactly the senior instinct that transfers from your backend career.
Key terms
- Pipeline orchestration
- Coordinating multiple models so each runs in sequence, consuming prior outputs, to produce one result.
- Measurement fusion
- Combining pose, silhouette, and parsing estimates into a single measurement set with confidence.
Why is assembling the four-model pipeline on Day 74 mostly orchestration rather than debugging?
