Update componet 4

component_4/human_action_detector.py

@@ -11,74 +11,86 @@ mp_drawing = mp.solutions.drawing_utils
 mp_holistic = mp.solutions.holistic
 mp_pose = mp.solutions.pose
 
-mp_drawing.DrawingSpec(color=(0, 0, 255), thickness=2, circle_radius=2)
-
-
-def calculate_angle(a, b, c):
-    a = np.array(a)  # First
-    b = np.array(b)  # Mid
-    c = np.array(c)  # End
+threshold = 0.25
 
-    radians = np.arctan2(c[1] - b[1], c[0] - b[0]) - np.arctan2(a[1] - b[1], a[0] - b[0])
-    angle = np.abs(radians * 180.0 / np.pi)
+mp_drawing.DrawingSpec(color=(0, 0, 255), thickness=2, circle_radius=2)
 
-    if angle > 180.0:
-        angle = 360 - angle
 
-    return angle
+def distanceCalculate(p1, p2):
+    """p1 and p2 in format (x1,y1) and (x2,y2) tuples"""
+    dis = ((p2[0] - p1[0]) ** 2 + (p2[1] - p1[1]) ** 2) ** 0.5
+    return dis
 
 
 def get_human_thred():
-    cap = cv2.VideoCapture(0)
+    cap = cv2.VideoCapture('input_videos/2.mp4')
     ## Setup mediapipe instance
+    index_num = 0
     with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:
         while cap.isOpened():
             ret, frame = cap.read()
 
-            # Recolor image to RGB
-            image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-            image.flags.writeable = False
+            if ret:
 
-            # Make detection
-            results = pose.process(image)
+                # Recolor image to RGB
+                cv2.imwrite('images/' + str(index_num) + '.jpg', frame)
+                index_num += 1
+                image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                image.flags.writeable = False
 
-            # Recolor back to BGR
-            image.flags.writeable = True
-            image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+                # Make detection
+                results = pose.process(image)
 
-            # Extract landmarks
-            try:
-                landmarks = results.pose_landmarks.landmark
-
-                # Get coordinates
-                shoulder = [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x,
-                            landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y]
-                elbow = [landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].x,
-                         landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].y]
-                wrist = [landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x,
-                         landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y]
-
-                # Calculate angle
-                angle = calculate_angle(shoulder, elbow, wrist)
-
-                # Visualize angle
-                cv2.putText(image, str(angle),
-                            tuple(np.multiply(elbow, [640, 480]).astype(int)),
-                            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA
-                            )
+                # Recolor back to BGR
+                image.flags.writeable = True
+                image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+
+                # Extract landmarks
+                try:
+                    landmarks = results.pose_landmarks.landmark
+
+                    nose = (landmarks[mp_pose.PoseLandmark.NOSE.value].x,
+                            landmarks[mp_pose.PoseLandmark.NOSE.value].y)
+
+                    left_wrist = (landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x,
+                                  landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y)
+
+                    right_wrist = (landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].x,
+                                   landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].y)
+
+                    distance_left = distanceCalculate(nose, left_wrist)
+                    distance_right = distanceCalculate(nose, right_wrist)
+
+                    print('distance  -  ', distance_left)
 
-            except:
-                pass
+                    if distance_left < threshold or distance_right < threshold:
+                        print('danger')
+                        cv2.putText(image, 'Danger Detection',
+                                    tuple(np.multiply(nose, [640, 480]).astype(int)),
+                                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2, cv2.LINE_AA
+                                    )
+                    else:
+                        print('no danger')
+                        cv2.putText(image, 'No Danger Detection',
+                                    tuple(np.multiply(nose, [640, 480]).astype(int)),
+                                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2, cv2.LINE_AA
+                                    )
 
-            # Render detections
-            mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,
-                                      mp_drawing.DrawingSpec(color=(245, 117, 66), thickness=2, circle_radius=2),
-                                      mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=2)
-                                      )
 
-            cv2.imshow('Mediapipe Feed', image)
+                except:
+                    pass
 
-            if cv2.waitKey(10) & 0xFF == ord('q'):
+                # Render detections
+                mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,
+                                          mp_drawing.DrawingSpec(color=(245, 117, 66), thickness=2, circle_radius=2),
+                                          mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=2)
+                                          )
+
+                cv2.imshow('CCTV Feed', image)
+
+                if cv2.waitKey(10) & 0xFF == ord('q'):
+                    break
+            else:
                 break
 
         cap.release()
@@ -88,38 +100,52 @@ def get_human_thred():
 def get_human_thred_temp():
     with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:
         sample_imgs = os.listdir('component_4/sample_data')
+        filename = 'component_4/sample_data/' + str(random.choice(sample_imgs))
+        frame = cv2.imread(filename)
 
-        frame = cv2.imread('component_4/sample_data/' + str(random.choice(sample_imgs)))
+        try:
 
-        # Recolor image to RGB
-        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-        image.flags.writeable = False
+            image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            image.flags.writeable = False
 
-        # Make detection
-        results = pose.process(image)
+            # Make detection
+            results = pose.process(image)
 
-        # Recolor back to BGR
-        image.flags.writeable = True
-        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+            # Recolor back to BGR
+            image.flags.writeable = True
+            image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+
+            # Extract landmarks
 
-        # Extract landmarks
-        try:
             landmarks = results.pose_landmarks.landmark
 
-            # Get coordinates
-            shoulder = [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x,
-                        landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y]
-            elbow = [landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].x,
-                     landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].y]
-            wrist = [landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x,
-                     landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y]
+            nose = (landmarks[mp_pose.PoseLandmark.NOSE.value].x,
+                    landmarks[mp_pose.PoseLandmark.NOSE.value].y)
+
+            left_wrist = (landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x,
+                          landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y)
 
-            # Calculate angle
-            angle = calculate_angle(shoulder, elbow, wrist)
+            right_wrist = (landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].x,
+                           landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].y)
 
-            if angle > 60:
-                return True
+            distance_left = distanceCalculate(nose, left_wrist)
+            distance_right = distanceCalculate(nose, right_wrist)
+
+            if distance_left < threshold or distance_right < threshold:
+                cv2.putText(image, 'Danger Detection',
+                            tuple(np.multiply(nose, [640, 480]).astype(int)),
+                            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2, cv2.LINE_AA
+                            )
+                cv2.imwrite('component_4/out/out.png', image)
+                return True, filename
             else:
-                return False
+                cv2.putText(image, 'No Danger Detection',
+                            tuple(np.multiply(nose, [640, 480]).astype(int)),
+                            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2, cv2.LINE_AA
+                            )
+                cv2.imwrite('component_4/out/out.png', image)
+                return False, filename
         except:
-            return False
+            return False, filename
+
+# get_human_thred()