openvino_sample.py · Intel/heatmap-generation at main

Update native RC1 samples and supported device documentation

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# SPDX-License-Identifier: MIT
# Copyright (C) Intel Corporation
#
# Heatmap Generation: OpenVINO Runtime sample.
# See README.md for prerequisites and inputs. Run: python3 openvino_sample.py

import cv2
import numpy as np
import openvino as ov

CONF_THRESHOLD = 0.4
# Select the model: "yolo26n" (default) or "detr-resnet-50".
MODEL = "yolo26n"
INPUT_SIZE = 800 if MODEL.startswith("detr") else 640
HEATMAP_ALPHA = 0.55
MATCH_DIST = 80.0   # max px between frames to treat detections as the same object
MAX_SPEED = 20.0    # px/frame that maps to full red


def render_speed_heatmap(frame, speed_sum, count, alpha):
    """Color traffic by average speed: blue = slow, yellow = medium,
    red = fast. Only regions where vehicles were seen are tinted, so
    empty background keeps its original color."""
    avg = np.zeros_like(speed_sum)
    seen = count > 0
    avg[seen] = speed_sum[seen] / count[seen]
    avg = cv2.GaussianBlur(avg, (0, 0), sigmaX=15)
    presence = cv2.GaussianBlur(seen.astype(np.float32), (0, 0), sigmaX=15)
    norm = np.clip(avg / MAX_SPEED, 0, 1)  # 0 = slow (blue), 1 = fast (red)
    color = cv2.applyColorMap((norm * 255).astype(np.uint8), cv2.COLORMAP_JET)
    weight = (np.clip(presence, 0, 1) * alpha)[..., np.newaxis]
    overlay = frame.astype(np.float32) * (1 - weight) + color.astype(np.float32) * weight
    return overlay.astype(np.uint8), color


core = ov.Core()
model = core.read_model(f"{MODEL}_openvino_model/{MODEL}.xml")

# Change device to "GPU" or "NPU" to run on integrated GPU or NPU.
compiled = core.compile_model(model, "CPU")

cap = cv2.VideoCapture("test_video.mp4")
fps = cap.get(cv2.CAP_PROP_FPS) or 30.0
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
writer = cv2.VideoWriter(
    "output_openvino.mp4", cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height))

speed_sum = np.zeros((height, width), dtype=np.float32)
count = np.zeros((height, width), dtype=np.float32)
prev_centroids = []
heatmap_color = None
frame_idx = 0
total_dets = 0

while True:
    ok, frame = cap.read()
    if not ok:
        break
    frame_idx += 1
    h0, w0 = frame.shape[:2]
    sx, sy = w0 / INPUT_SIZE, h0 / INPUT_SIZE

    blob = cv2.resize(frame, (INPUT_SIZE, INPUT_SIZE))
    blob = cv2.cvtColor(blob, cv2.COLOR_BGR2RGB).astype(np.float32) / 255.0
    blob = blob.transpose(2, 0, 1)[np.newaxis, ...]

    output = compiled([blob])[compiled.output(0)][0]
    dets = output[output[:, 4] >= CONF_THRESHOLD]
    total_dets += len(dets)

    cur_centroids = []
    for det in dets:
        x1, y1 = int(det[0] * sx), int(det[1] * sy)
        x2, y2 = int(det[2] * sx), int(det[3] * sy)
        x1, x2 = max(0, x1), min(width, x2)
        y1, y2 = max(0, y1), min(height, y2)
        cx, cy = (x1 + x2) / 2, (y1 + y2) / 2
        cur_centroids.append((cx, cy))

        # Speed = displacement from the nearest detection in the previous frame.
        speed = 0.0
        if prev_centroids:
            d = min(np.hypot(cx - px, cy - py) for px, py in prev_centroids)
            if d <= MATCH_DIST:
                speed = d
        speed_sum[y1:y2, x1:x2] += speed
        count[y1:y2, x1:x2] += 1.0
    prev_centroids = cur_centroids

    overlay, heatmap_color = render_speed_heatmap(frame, speed_sum, count, HEATMAP_ALPHA)
    cv2.putText(overlay, f"Detections: {len(dets)}", (10, 30),
                cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 2)
    writer.write(overlay)

cap.release()
writer.release()

if heatmap_color is not None:
    cv2.imwrite("heatmap.jpg", heatmap_color)
    print("Saved: heatmap.jpg")

print(f"Processed {frame_idx} frames, {total_dets} total detections", flush=True)

	# SPDX-License-Identifier: MIT
	# Copyright (C) Intel Corporation
	#
	# Heatmap Generation: OpenVINO Runtime sample.
	# See README.md for prerequisites and inputs. Run: python3 openvino_sample.py

	import cv2
	import numpy as np
	import openvino as ov

	CONF_THRESHOLD = 0.4
	# Select the model: "yolo26n" (default) or "detr-resnet-50".
	MODEL = "yolo26n"
	INPUT_SIZE = 800 if MODEL.startswith("detr") else 640
	HEATMAP_ALPHA = 0.55
	MATCH_DIST = 80.0 # max px between frames to treat detections as the same object
	MAX_SPEED = 20.0 # px/frame that maps to full red


	def render_speed_heatmap(frame, speed_sum, count, alpha):
	"""Color traffic by average speed: blue = slow, yellow = medium,
	red = fast. Only regions where vehicles were seen are tinted, so
	empty background keeps its original color."""
	avg = np.zeros_like(speed_sum)
	seen = count > 0
	avg[seen] = speed_sum[seen] / count[seen]
	avg = cv2.GaussianBlur(avg, (0, 0), sigmaX=15)
	presence = cv2.GaussianBlur(seen.astype(np.float32), (0, 0), sigmaX=15)
	norm = np.clip(avg / MAX_SPEED, 0, 1) # 0 = slow (blue), 1 = fast (red)
	color = cv2.applyColorMap((norm * 255).astype(np.uint8), cv2.COLORMAP_JET)
	weight = (np.clip(presence, 0, 1) * alpha)[..., np.newaxis]
	overlay = frame.astype(np.float32) * (1 - weight) + color.astype(np.float32) * weight
	return overlay.astype(np.uint8), color


	core = ov.Core()
	model = core.read_model(f"{MODEL}_openvino_model/{MODEL}.xml")

	# Change device to "GPU" or "NPU" to run on integrated GPU or NPU.
	compiled = core.compile_model(model, "CPU")

	cap = cv2.VideoCapture("test_video.mp4")
	fps = cap.get(cv2.CAP_PROP_FPS) or 30.0
	width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
	height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
	writer = cv2.VideoWriter(
	"output_openvino.mp4", cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height))

	speed_sum = np.zeros((height, width), dtype=np.float32)
	count = np.zeros((height, width), dtype=np.float32)
	prev_centroids = []
	heatmap_color = None
	frame_idx = 0
	total_dets = 0

	while True:
	ok, frame = cap.read()
	if not ok:
	break
	frame_idx += 1
	h0, w0 = frame.shape[:2]
	sx, sy = w0 / INPUT_SIZE, h0 / INPUT_SIZE

	blob = cv2.resize(frame, (INPUT_SIZE, INPUT_SIZE))
	blob = cv2.cvtColor(blob, cv2.COLOR_BGR2RGB).astype(np.float32) / 255.0
	blob = blob.transpose(2, 0, 1)[np.newaxis, ...]

	output = compiled([blob])[compiled.output(0)][0]
	dets = output[output[:, 4] >= CONF_THRESHOLD]
	total_dets += len(dets)

	cur_centroids = []
	for det in dets:
	x1, y1 = int(det[0] * sx), int(det[1] * sy)
	x2, y2 = int(det[2] * sx), int(det[3] * sy)
	x1, x2 = max(0, x1), min(width, x2)
	y1, y2 = max(0, y1), min(height, y2)
	cx, cy = (x1 + x2) / 2, (y1 + y2) / 2
	cur_centroids.append((cx, cy))

	# Speed = displacement from the nearest detection in the previous frame.
	speed = 0.0
	if prev_centroids:
	d = min(np.hypot(cx - px, cy - py) for px, py in prev_centroids)
	if d <= MATCH_DIST:
	speed = d
	speed_sum[y1:y2, x1:x2] += speed
	count[y1:y2, x1:x2] += 1.0
	prev_centroids = cur_centroids

	overlay, heatmap_color = render_speed_heatmap(frame, speed_sum, count, HEATMAP_ALPHA)
	cv2.putText(overlay, f"Detections: {len(dets)}", (10, 30),
	cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 2)
	writer.write(overlay)

	cap.release()
	writer.release()

	if heatmap_color is not None:
	cv2.imwrite("heatmap.jpg", heatmap_color)
	print("Saved: heatmap.jpg")

	print(f"Processed {frame_idx} frames, {total_dets} total detections", flush=True)