From 1bbade48848174a5caebe1c7d157306df2f667b3 Mon Sep 17 00:00:00 2001
From: Michal Maciaszek <maciaszekm@wp.pl>
Date: Tue, 29 Dec 2020 21:42:16 +0100
Subject: [PATCH] recognize finger straight

---
 ...gle_solution.py => gesture_recognition.py} | 33 ++++++++++++++++++-
 1 file changed, 32 insertions(+), 1 deletion(-)
 rename gestures/{google_solution.py => gesture_recognition.py} (50%)

diff --git a/gestures/google_solution.py b/gestures/gesture_recognition.py
similarity index 50%
rename from gestures/google_solution.py
rename to gestures/gesture_recognition.py
index 75fa9dd..8ad8ff5 100644
--- a/gestures/google_solution.py
+++ b/gestures/gesture_recognition.py
@@ -2,6 +2,13 @@ import cv2
 import mediapipe as mp
 mp_drawing = mp.solutions.drawing_utils
 mp_hands = mp.solutions.hands
+from math import sqrt
+
+def calculate_distance(ax, ay, bx,by):
+  distance = sqrt(((bx - ax) ** 2 + (by - ay) ** 2))
+  return distance
+
+
 
 hands = mp_hands.Hands(
     min_detection_confidence=0.5, min_tracking_confidence=0.5)
@@ -18,7 +25,7 @@ while cap.isOpened():
   results = hands.process(image)
 
 
-  # Draw the hand annotations on the image.
+  # Draw the hand annotations on the image.g
   image.flags.writeable = True
   image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
   if results.multi_hand_landmarks:
@@ -26,6 +33,30 @@ while cap.isOpened():
       mp_drawing.draw_landmarks(
           image, hand_landmarks, mp_hands.HAND_CONNECTIONS)
       #print(hand_landmarks)        ## spójrz na to
+  if cv2.waitKey(33) == ord('s'):
+    if results.multi_hand_landmarks:
+      i = 0
+      for hand_landmarks in results.multi_hand_landmarks:
+        ax = hand_landmarks.landmark[8].x
+        ay = hand_landmarks.landmark[8].y
+        bx = hand_landmarks.landmark[5].x
+        by = hand_landmarks.landmark[5].y
+        odleglosc_8_5 = calculate_distance(ax,ay, bx, by)
+        print(odleglosc_8_5)
+        ax = hand_landmarks.landmark[5].x
+        ay = hand_landmarks.landmark[5].y
+        bx = hand_landmarks.landmark[0].x
+        by = hand_landmarks.landmark[0].y
+        odleglosc_5_0 = calculate_distance(ax, ay, bx, by)
+        print(odleglosc_5_0)
+        if(odleglosc_5_0 < odleglosc_8_5 + 0.1):
+          print("wyprostowany")
+        else:
+          print("niewyprostowany")
+
+
+        # i += 1
+        # print(hand_landmarks.)
 
   cv2.imshow('MediaPipe Hands', image)
   if cv2.waitKey(5) & 0xFF == 27: