Add to the component 4 backend file

component_4/human_action_detector.py

+import cv2
+import mediapipe as mp
+import numpy as np
+import os
+import random
+
+mp_drawing = mp.solutions.drawing_utils
+mp_pose = mp.solutions.pose
+
+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
+
+    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)
+
+    if angle > 180.0:
+        angle = 360 - angle
+
+    return angle
+
+
+def get_human_thred():
+    cap = cv2.VideoCapture(0)
+    ## Setup mediapipe instance
+    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
+
+            # Make detection
+            results = pose.process(image)
+
+            # Recolor back to BGR
+            image.flags.writeable = True
+            image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+
+            # 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
+                            )
+
+            except:
+                pass
+
+            # 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)
+
+            if cv2.waitKey(10) & 0xFF == ord('q'):
+                break
+
+        cap.release()
+        cv2.destroyAllWindows()
+
+
+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')
+
+        frame = cv2.imread('component_4/sample_data/' + str(random.choice(sample_imgs)))
+
+        # Recolor image to RGB
+        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        image.flags.writeable = False
+
+        # Make detection
+        results = pose.process(image)
+
+        # Recolor back to BGR
+        image.flags.writeable = True
+        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+
+        # 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)
+
+            if angle > 60:
+                return True
+            else:
+                return False
+        except:
+            return False