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