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85 lines
3.7 KiB
85 lines
3.7 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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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(region=region_points, model="yolo11n.pt", show=False) # Test object counter |
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heatmap = solutions.Heatmap(colormap=cv2.COLORMAP_PARULA, model="yolo11n.pt", show=False) # Test heatmaps |
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speed = solutions.SpeedEstimator(region=region_points, model="yolo11n.pt", show=False) # Test queue manager |
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queue = solutions.QueueManager(region=region_points, model="yolo11n.pt", show=False) # Test speed estimation |
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line_analytics = solutions.Analytics(analytics_type="line", model="yolo11n.pt", show=False) # line analytics |
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pie_analytics = solutions.Analytics(analytics_type="pie", model="yolo11n.pt", show=False) # line analytics |
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bar_analytics = solutions.Analytics(analytics_type="bar", model="yolo11n.pt", show=False) # line analytics |
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area_analytics = solutions.Analytics(analytics_type="area", model="yolo11n.pt", show=False) # line analytics |
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frame_count = 0 # Required for analytics |
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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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_ = counter.count(original_im0.copy()) |
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_ = heatmap.generate_heatmap(original_im0.copy()) |
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_ = speed.estimate_speed(original_im0.copy()) |
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_ = queue.process_queue(original_im0.copy()) |
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_ = line_analytics.process_data(original_im0.copy(), frame_count) |
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_ = pie_analytics.process_data(original_im0.copy(), frame_count) |
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_ = bar_analytics.process_data(original_im0.copy(), frame_count) |
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_ = area_analytics.process_data(original_im0.copy(), frame_count) |
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cap.release() |
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# Test workouts monitoring |
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safe_download(url=WORKOUTS_SOLUTION_DEMO) |
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cap1 = cv2.VideoCapture("solution_ci_pose_demo.mp4") |
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assert cap1.isOpened(), "Error reading video file" |
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gym = solutions.AIGym(line_width=2, kpts=[5, 11, 13], show=False) |
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while cap1.isOpened(): |
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success, im0 = cap1.read() |
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if not success: |
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break |
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_ = gym.monitor(im0) |
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cap1.release() |
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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("yolo11n-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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