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# Ultralytics YOLO 🚀, AGPL-3.0 license
import cv2
import pytest
from ultralytics import YOLO, solutions
from ultralytics.utils.downloads import safe_download
MAJOR_SOLUTIONS_DEMO = "https://github.com/ultralytics/assets/releases/download/v0.0.0/solutions_ci_demo.mp4"
WORKOUTS_SOLUTION_DEMO = "https://github.com/ultralytics/assets/releases/download/v0.0.0/solution_ci_pose_demo.mp4"
@pytest.mark.slow
def test_major_solutions():
"""Test the object counting, heatmap, speed estimation and queue management solution."""
safe_download(url=MAJOR_SOLUTIONS_DEMO)
model = YOLO("yolov8n.pt")
names = model.names
cap = cv2.VideoCapture("solutions_ci_demo.mp4")
assert cap.isOpened(), "Error reading video file"
region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
counter = solutions.ObjectCounter(reg_pts=region_points, names=names, view_img=False)
heatmap = solutions.Heatmap(colormap=cv2.COLORMAP_PARULA, names=names, view_img=False)
speed = solutions.SpeedEstimator(reg_pts=region_points, names=names, view_img=False)
queue = solutions.QueueManager(names=names, reg_pts=region_points, view_img=False)
while cap.isOpened():
success, im0 = cap.read()
if not success:
break
original_im0 = im0.copy()
tracks = model.track(im0, persist=True, show=False)
_ = counter.start_counting(original_im0.copy(), tracks)
_ = heatmap.generate_heatmap(original_im0.copy(), tracks)
_ = speed.estimate_speed(original_im0.copy(), tracks)
_ = queue.process_queue(original_im0.copy(), tracks)
cap.release()
cv2.destroyAllWindows()
@pytest.mark.slow
def test_aigym():
"""Test the workouts monitoring solution."""
safe_download(url=WORKOUTS_SOLUTION_DEMO)
model = YOLO("yolov8n-pose.pt")
cap = cv2.VideoCapture("solution_ci_pose_demo.mp4")
assert cap.isOpened(), "Error reading video file"
gym_object = solutions.AIGym(line_thickness=2, pose_type="squat", kpts_to_check=[5, 11, 13])
while cap.isOpened():
success, im0 = cap.read()
if not success:
break
results = model.track(im0, verbose=False)
_ = gym_object.start_counting(im0, results)
cap.release()
cv2.destroyAllWindows()
@pytest.mark.slow
def test_instance_segmentation():
"""Test the instance segmentation solution."""
from ultralytics.utils.plotting import Annotator, colors
model = YOLO("yolov8n-seg.pt")
names = model.names
cap = cv2.VideoCapture("solutions_ci_demo.mp4")
assert cap.isOpened(), "Error reading video file"
while cap.isOpened():
success, im0 = cap.read()
if not success:
break
results = model.predict(im0)
annotator = Annotator(im0, line_width=2)
if results[0].masks is not None:
clss = results[0].boxes.cls.cpu().tolist()
masks = results[0].masks.xy
for mask, cls in zip(masks, clss):
color = colors(int(cls), True)
annotator.seg_bbox(mask=mask, mask_color=color, label=names[int(cls)])
cap.release()
cv2.destroyAllWindows()
@pytest.mark.slow
def test_streamlit_predict():
"""Test streamlit predict live inference solution."""
solutions.inference()
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