You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
87 lines
3.2 KiB
87 lines
3.2 KiB
5 months ago
|
# 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()
|