start CV

projects/CV/.gitignore

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+test/
+capture.png
+yolov8*.pt

projects/CV/Assets/cat_dog.jpg

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+../../../assets/cat_dog.jpg

projects/CV/Assets/girl_faces.png

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+../../../assets/girl_faces.png

projects/CV/DetectFaces/__main__.py

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+import cv2
+
+image_path = './Assets/girl_faces.png'
+face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
+
+
+def viewImage(image, name_of_window):
+    cv2.namedWindow(name_of_window, cv2.WINDOW_NORMAL)
+    cv2.imshow(name_of_window, image)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+
+if __name__ == '__main__':
+    image = cv2.imread(image_path)
+
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+    faces = face_cascade.detectMultiScale(
+        gray,
+        scaleFactor=1.1,
+        minNeighbors=5,
+        minSize=(10, 10),
+    )
+
+    faces_detected_string = 'Лиц обнаружено: ' + format(len(faces))
+    print(faces_detected_string)
+
+    # Рисуем квадраты вокруг лиц
+    for x, y, w, h in faces:
+        cv2.rectangle(image, (x, y), (x + w, y + h), (255, 255, 0), 2)
+
+    viewImage(image, faces_detected_string)

projects/CV/README.md

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+Python модули
+-------------
+
+* `VideoCapture` — модуль для проверки захвата RTSP потока
+* `DetectFaces` — пример распознования лиц на основе каскада Хаара
+* `Stream` - пример с сайта `ultralytics` распозования на видео

projects/CV/Stream/__main__.py

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+import cv2
+from ultralytics import YOLO
+
+# https://docs.ultralytics.com/modes/predict/#streaming-source-for-loop
+
+# Load the YOLOv8 model
+model = YOLO('yolov8m.pt')
+
+# Open the video file
+video_path = 'run.mp4'
+cap = cv2.VideoCapture(video_path)
+
+# Loop through the video frames
+while cap.isOpened():
+    # Read a frame from the video
+    success, frame = cap.read()
+
+    if success:
+        # Run YOLOv8 inference on the frame
+        results = model(frame)
+
+        # Visualize the results on the frame
+        annotated_frame = results[0].plot()
+
+        # Display the annotated frame
+        cv2.imshow('YOLOv8 Inference', annotated_frame)
+
+        # Break the loop if 'q' is pressed
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+    else:
+        # Break the loop if the end of the video is reached
+        break
+
+# Release the video capture object and close the display window
+cap.release()
+cv2.destroyAllWindows()

projects/CV/VideoCapture/__main__.py

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+import cv2
+
+RTSP_ADDR = "rtsp://localhost:8554/cam"
+
+cam = cv2.VideoCapture(RTSP_ADDR)
+
+while True:
+    result, image = cam.read()
+
+    cv2.imshow("Camera", image)
+
+    if cv2.waitKey(1) & 0xFF == ord("s"):
+        cv2.imwrite("capture.png", image)
+
+    if cv2.waitKey(1) & 0xFF == ord("q"):
+        break
+
+cam.release()
+
+cv2.destroyWindow("Camera")
+cv2.destroyAllWindows()

projects/CV/ex.py

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+from ultralytics import YOLO
+
+model = YOLO('yolov8m.pt')
+model.save_dir = './predict/'
+
+result = model.predict(
+    './Assets/cat_dog.jpg',
+    save=True,
+    device='cpu',
+    show=False,
+    classes=[16, 15],
+)[0]
+result = model(
+    './Assets/cat_dog.jpg',
+    save=True,
+    project='test',
+    name='ololo',
+    device='cpu',
+    show=False,
+)[0]
+
+# print(len(result.boxes))
+
+# for _ in result.boxes:
+#     box = result.boxes[_]
+#     print("Object type:", box.cls)
+#     print("Coordinates:", box.xyxy)
+# print("Probability:", box.conf)

projects/CV/haarcascade_frontalface_default.xml

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+/usr/share/opencv4/haarcascades/haarcascade_frontalface_default.xml

projects/CV/pyproject.toml

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+[tool.black]
+skip-string-normalization = true
+pycodestyle = true
+line-length = 100

projects/CV/requirements.txt

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+notebook==7.0.4
+ultralytics==8.0.188