commit realTime

realTime.py

+import pyautogui
+import time
+import actgui
+
+import cv2
+import numpy as np
+from unified_detector import Fingertips
+from hand_detector.detector import SOLO, YOLO
+
+
+def main():
+    hand_detection_method = 'yolo'
+
+    if hand_detection_method is 'solo':
+        hand = SOLO(weights='weights/solo.h5', threshold=0.8)
+    elif hand_detection_method is 'yolo':
+        hand = YOLO(weights='weights/yolo.h5', threshold=0.8)
+    else:
+        assert False, "'" + hand_detection_method + \
+                      "' hand detection does not exist. use either 'solo' or 'yolo' as hand detection method"
+
+    fingertips = Fingertips(weights='weights/fingertip.h5')
+
+    cam = cv2.VideoCapture(0)
+    print('Unified Gesture & Fingertips Detection')
+
+
+    while True:
+        time.sleep(0.2)
+        ret, image = cam.read()
+    ################################################
+    # openCV CAM flip
+    ################################################
+        image = cv2.flip(image,1)
+    ################################################
+        if ret is False:
+            break
+
+        # hand detection
+        tl, br = hand.detect(image=image)
+
+        if tl and br is not None:
+            cropped_image = image[tl[1]:br[1], tl[0]: br[0]]
+            height, width, _ = cropped_image.shape
+
+            # gesture classification and fingertips regression
+            prob, pos = fingertips.classify(image=cropped_image)
+            pos = np.mean(pos, 0)
+            
+            # post-processing
+            prob = np.asarray([(p >= 0.2) * 1.0 for p in prob])
+            for i in range(0, len(pos), 2):
+                pos[i] = pos[i] * width + tl[0]
+                pos[i + 1] = pos[i + 1] * height + tl[1]
+                
+                
+#########################################################
+            # start gui
+#########################################################
+
+            # hand check
+            case = actgui.hand_check(prob)
+            print(case)
+            actgui.store_pos(pos)
+
+            # 좌표 초기화
+            actgui.initialize_coordinate()
+            
+            # 변수 초기화
+            actgui.initialize_variable()
+            
+            # 종료 확인
+            bool = actgui.terminate_check()
+            if bool: break
+            
+            ##########################
+            # 작동
+            actgui.act_gui()
+            
+#########################################################
+            # end gui
+#########################################################
+            # drawing
+            index = 0
+            color = [(15, 15, 240), (15, 240, 155), (240, 155, 15), (240, 15, 155), (240, 15, 240)]
+            # 손 범위
+            image = cv2.rectangle(image, (tl[0], tl[1]), (br[0], br[1]), (235, 26, 158), 2)
+            # 원 그리기
+            for c, p in enumerate(prob):
+                if p > 0.5:
+                    image = cv2.circle(image, (int(pos[index]), int(pos[index + 1])), radius=12,
+                                       color=color[c], thickness=-2)
+                index = index + 2
+
+        if cv2.waitKey(1) & 0xff == 27:
+            break
+
+        # display image
+        image = cv2.resize(image, dsize=(0, 0), fx=0.3, fy=0.3, interpolation=cv2.INTER_AREA)
+        cv2.imshow('Unified Gesture & Fingertips Detection', image)
+
+    cam.release()
+    cv2.destroyAllWindows()
+
+
+def mainleft():
+    hand_detection_method = 'yolo'
+
+    if hand_detection_method is 'solo':
+        hand = SOLO(weights='weights/solo.h5', threshold=0.8)
+    elif hand_detection_method is 'yolo':
+        hand = YOLO(weights='weights/yolo.h5', threshold=0.8)
+    else:
+        assert False, "'" + hand_detection_method + \
+                      "' hand detection does not exist. use either 'solo' or 'yolo' as hand detection method"
+
+    fingertips = Fingertips(weights='weights/fingertip.h5')
+
+    cam = cv2.VideoCapture(0)
+    print('Unified Gesture & Fingertips Detection')
+
+
+    while True:
+        time.sleep(0.2)
+        ret, image = cam.read()
+    ################################################
+    
+    ################################################
+        if ret is False:
+            break
+
+        # hand detection
+        tl, br = hand.detect(image=image)
+
+        if tl and br is not None:
+            cropped_image = image[tl[1]:br[1], tl[0]: br[0]]
+            height, width, _ = cropped_image.shape
+
+            # gesture classification and fingertips regression
+            prob, pos = fingertips.classify(image=cropped_image)
+            pos = np.mean(pos, 0)
+            
+            # post-processing
+            prob = np.asarray([(p >= 0.2) * 1.0 for p in prob])
+            for i in range(0, len(pos), 2):
+                pos[i] = pos[i] * width + tl[0]
+                pos[i + 1] = pos[i + 1] * height + tl[1]
+                
+                
+#########################################################
+            # start gui
+#########################################################
+
+            # hand check
+            case = actgui.hand_check(prob)
+            print(case)
+            actgui.store_pos(pos)
+
+            # 좌표 초기화
+            actgui.initialize_coordinate()
+            
+            # 변수 초기화
+            actgui.initialize_variable()
+            
+            # 종료 확인
+            bool = actgui.terminate_check()
+            if bool: break
+            
+            ##########################
+            # 작동
+            actgui.act_gui()
+            
+#########################################################
+            # end gui
+#########################################################
+            # drawing
+            index = 0
+            color = [(15, 15, 240), (15, 240, 155), (240, 155, 15), (240, 15, 155), (240, 15, 240)]
+            # 손 범위
+            image = cv2.rectangle(image, (tl[0], tl[1]), (br[0], br[1]), (235, 26, 158), 2)
+            # 원 그리기
+            for c, p in enumerate(prob):
+                if p > 0.5:
+                    image = cv2.circle(image, (int(pos[index]), int(pos[index + 1])), radius=12,
+                                       color=color[c], thickness=-2)
+                index = index + 2
+
+        if cv2.waitKey(1) & 0xff == 27:
+            break
+
+        # display image
+        image = cv2.resize(image, dsize=(0, 0), fx=0.3, fy=0.3, interpolation=cv2.INTER_AREA)
+        cv2.imshow('Unified Gesture & Fingertips Detection', image)
+
+    cam.release()
+    cv2.destroyAllWindows()
+    
+
+
+if __name__=="__main__":
+    main()