Merge pull request TAHIR0110#36 from SAM-DEV007/Suicide_Detection

Suicide attempt detection

Suicide_Detection/.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
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+/site
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+# mypy
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+
+Model/Datasets
+
+Model/Model_Train.py
+Model/Model_Data/logs/backup.txt
+yolov8n.pt
+
+Model/rope-detection-640.ipynb
+Model/rope-detection-480.ipynb
+
+Model/Model_Data/logs/runs/detect/SuicideDetection/*
+Model/Model_Data/logs/runs/detect/SuicideDetection2/*
+
+!Model/Model_Data/logs/runs/detect/SuicideDetection/weights
+!Model/Model_Data/logs/runs/detect/SuicideDetection2/weights

Suicide_Detection/Model/Model_Data/config.yaml

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+names:
+  0: rope
+path: D:\Projects\Open Source\ThereForYou\ThereForYou\Suicide_Detection\Model\Datasets\Processed
+test: test/images
+train: train/images
+val: val/images

Suicide_Detection/demo.py

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+import detect
+import cv2
+
+
+if __name__ == '__main__':
+    vid = cv2.VideoCapture(0)
+    vid.set(3, 640)
+    vid.set(4, 640)
+
+    _, frame = vid.read()
+    h, w, _ = frame.shape
+
+    while vid.isOpened():
+        _, frame = vid.read()
+        frame = cv2.flip(frame, 1)
+
+        # Face
+        face_box = detect.face_detection(frame, w, h)
+
+        # Hands
+        hands_box = detect.hand_detection(frame, w, h)
+
+        # Rope
+        rope_box = detect.rope_detection(frame, (w, h))
+
+        # Add text
+        txt = detect.suicide_prevention(rope_box, face_box, hands_box)
+        cv2.putText(frame, txt, (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 1, cv2.LINE_AA)
+
+        cv2.imshow('Cam', frame)
+
+        if cv2.waitKey(1) & 0xFF == ord('q'): break # Exit when 'q' is pressed
+        if cv2.waitKey(1) == 27: break # Exit when 'ESC' is pressed
+        if cv2.getWindowProperty('Cam', cv2.WND_PROP_VISIBLE) < 1: break # Exit when window is closed

Suicide_Detection/detect.py

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+from ultralytics import YOLO
+from pathlib import Path
+from ultralytics.utils.plotting import Annotator
+
+import cv2
+
+import mediapipe as mp
+
+
+_face_detection = mp.solutions.face_detection.FaceDetection()
+
+mphands = mp.solutions.hands
+hands = mphands.Hands()
+
+
+def load_model():
+    '''Loads the YOLO model for detection'''
+
+    model_path = Path(__file__).resolve().parent / 'Model/Model_Data/logs/runs/detect/SuicideDetection2/weights/best.pt'
+
+    model = YOLO(model_path)
+    return model
+
+
+def detect(model, frame, frame_size):
+    '''Detects objects in the image'''
+
+    results = model.predict(
+        source=frame,
+        verbose=False,
+        imgsz=frame_size,
+        conf=0.6,
+        iou=0.7,
+        save=False,
+        save_txt=False,
+        show_conf=False,
+        show_labels=False,
+        show_boxes=True,
+        stream=True
+    )
+
+    return results
+
+
+def face_detection(frame, w, h):
+    '''Detection for face detection'''
+
+    global _face_detection
+
+    box_list = []
+
+    face_results = _face_detection.process(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+    if face_results.detections:
+        for detection in face_results.detections:
+            face_data = detection.location_data
+            box_data = face_data.relative_bounding_box
+            x = round(box_data.xmin * w)
+            y = round(box_data.ymin * h)
+            wi = round(box_data.width * w)
+            he = round(box_data.height * h)
+
+            box_list.append((x, y, wi, he))
+
+            cv2.rectangle(frame, (x, y), (x + wi, y + he), (0, 255, 0), 2)
+
+    return box_list
+
+
+def hand_detection(frame, w, h):
+    '''Detection for hand detection'''
+
+    global hands
+
+    box_list = []
+
+    hands_results = hands.process(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+    hand_landmarks = hands_results.multi_hand_landmarks
+
+    if hand_landmarks:
+        for handLMs in hand_landmarks:
+            if handLMs:
+                x1 = round(min([val.x for val in handLMs.landmark]) * w)
+                y1 = round(min([val.y for val in handLMs.landmark]) * h)
+                x2 = round(max([val.x for val in handLMs.landmark]) * w)
+                y2 = round(max([val.y for val in handLMs.landmark]) * h)
+
+                box_list.append((x1, y1, x2, y2))
+
+                cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 255), 2)
+
+    return box_list
+
+
+def rope_detection(frame, frame_size):
+    '''Processed output for the detection'''
+
+    model = load_model()
+    results = detect(model, frame, frame_size)
+
+    box_list = []
+
+    for r in results:
+        annotator = Annotator(frame)
+
+        boxes = r.boxes
+
+        for box in boxes:
+            b = box.xyxy[0]
+            annotator.box_label(b)
+
+        box_list.append(boxes.xyxy.tolist())
+
+    annotator.result()
+
+    return box_list
+
+
+def suicide_prevention(rope_box, face_box, hands_box):
+    '''Return a string specifying the detected condition of threat'''
+
+    txt = 'None'
+
+    # Condition 1: Warning
+    if rope_box and face_box:
+        for boxes in rope_box:
+            for box in boxes:
+                x1, y1, x2, y2 = box
+
+                for face in face_box:
+                    fy, fh = face[1], face[3]
+                    if y2 < fy:
+                        txt = 'Warning: Face detected below rope area'
+
+                        # Condition 2: Danger
+                        if hands_box:
+                            for hand in hands_box:
+                                hx1, hy1, hx2, hy2 = hand
+
+                                if y1 < hy1 < y2:
+                                    return 'Danger: Face below rope area and hand near rope area detected'
+
+                        return txt
+                    # Condition 3: Critical Danger
+                    elif y1 < fy + fh:
+                        return 'Critical Danger: Face detected near rope area'
+
+    return txt