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app.py

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+import cv2
+import time
+import os
+import mediapipe as mp
+import gradio as gr
+import pyttsx3
+from threading import Thread
+#from cvzone.HandTrackingModule import HandDetector
+example_flag = False
+
+class handDetector():
+    def __init__(self, mode=True, modelComplexity=1, maxHands=2, detectionCon=0.5, trackCon=0.5):
+        self.mode = mode
+        self.maxHands = maxHands
+        self.detectionCon = detectionCon
+        self.modelComplex = modelComplexity
+        self.trackCon = trackCon
+        self.mpHands = mp.solutions.hands
+        self.hands = self.mpHands.Hands(self.mode, self.maxHands,self.modelComplex,self.detectionCon, self.trackCon)
+        self.mpDraw = mp.solutions.drawing_utils
+
+    def findHands(self, img, draw=True,flipType=True):
+        """
+        Finds hands in a BGR image.
+        :param img: Image to find the hands in.
+        :param draw: Flag to draw the output on the image.
+        :return: Image with or without drawings
+        """
+        imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        #cv2.imshow('test',imgRGB)
+        self.results = self.hands.process(imgRGB)
+        allHands = []
+        h, w, c = img.shape
+        if self.results.multi_hand_landmarks:
+            for handType, handLms in zip(self.results.multi_handedness, self.results.multi_hand_landmarks):
+                myHand = {}
+                ## lmList
+                mylmList = []
+                xList = []
+                yList = []
+                for id, lm in enumerate(handLms.landmark):
+                    px, py, pz = int(lm.x * w), int(lm.y * h), int(lm.z * w)
+                    mylmList.append([px, py, pz])
+                    xList.append(px)
+                    yList.append(py)
+
+                ## bbox
+                xmin, xmax = min(xList), max(xList)
+                ymin, ymax = min(yList), max(yList)
+                boxW, boxH = xmax - xmin, ymax - ymin
+                bbox = xmin, ymin, boxW, boxH
+                cx, cy = bbox[0] + (bbox[2] // 2), \
+                         bbox[1] + (bbox[3] // 2)
+
+                myHand["lmList"] = mylmList
+                myHand["bbox"] = bbox
+                myHand["center"] = (cx, cy)
+
+                if flipType:
+                    if handType.classification[0].label == "Right":
+                        myHand["type"] = "Left"
+                    else:
+                        myHand["type"] = "Right"
+                else:
+                    myHand["type"] = handType.classification[0].label
+                allHands.append(myHand)
+
+                ## draw
+                if draw:
+                    self.mpDraw.draw_landmarks(img, handLms,
+                                               self.mpHands.HAND_CONNECTIONS)
+                    cv2.rectangle(img, (bbox[0] - 20, bbox[1] - 20),
+                                  (bbox[0] + bbox[2] + 20, bbox[1] + bbox[3] + 20),
+                                  (255, 0, 255), 2)
+                    #cv2.putText(img, myHand["type"], (bbox[0] - 30, bbox[1] - 30), cv2.FONT_HERSHEY_PLAIN,2, (255, 0, 255), 2)
+        if draw:
+            return allHands, img
+        else:
+            return allHands
+    def findPosition(self, img, handNo=0, draw=True,flipType=False):
+
+        lmList = []
+        if self.results.multi_hand_landmarks:
+            myHand = self.results.multi_hand_landmarks[handNo]
+            for id, lm in enumerate(myHand.landmark):
+                # print(id, lm)
+                h, w, c = img.shape
+                cx, cy = int(lm.x * w), int(lm.y * h)
+                # print(id, cx, cy)
+                lmList.append([id, cx, cy])
+                if draw:
+                    cv2.circle(img, (cx, cy), 15, (255, 0, 255), cv2.FILLED)
+
+        return lmList
+
+
+def speak(text):
+    pyttsx3.speak(text)
+
+def set_example_image(example: list) -> dict:
+    return gr.inputs.Image.update(value=example[0])
+
+
+def count(im):
+  folderPath = "Count"
+  myList = os.listdir(folderPath)
+  overlayList = []
+  for imPath in sorted(myList):
+      image = cv2.imread(f'{folderPath}/{imPath}')
+      # print(f'{folderPath}/{imPath}')
+      overlayList.append(image)
+
+  #print(len(overlayList))
+  tipIds = [4, 8, 12, 16, 20]
+  detector = handDetector(detectionCon=0.75)
+
+  #img = cv2.imread('test.jpg')
+  allhands,img = detector.findHands(cv2.flip(im[:,:,::-1], 1))
+  cv2.imwrite('test3.png',img)
+  
+  lmList = detector.findPosition(img, draw=False,)
+  # print(lmList)
+
+  if len(lmList) != 0:
+      fingers = []
+
+      # Thumb
+      if lmList[tipIds[0]][1] > lmList[tipIds[0] - 1][1]:
+          fingers.append(1)
+      else:
+          fingers.append(0)
+
+      # 4 Fingers
+      for id in range(1, 5):
+          if lmList[tipIds[id]][2] < lmList[tipIds[id] - 2][2]:
+              fingers.append(1)
+          else:
+              fingers.append(0)
+
+      # print(fingers)
+      totalFingers = fingers.count(1)
+      #print(totalFingers)
+      text = f"Total finger count is {totalFingers}!"
+
+      h, w, c = overlayList[totalFingers - 1].shape
+      img = cv2.flip(img,1)
+      img[0:h, 0:w] = overlayList[totalFingers - 1]
+      
+
+      cv2.rectangle(img, (20, 225), (170, 425), (0, 255, 0), cv2.FILLED)
+      cv2.putText(img, str(totalFingers), (45, 375), cv2.FONT_HERSHEY_PLAIN,
+                  10, (255, 0, 0), 25)
+      p1=Thread(target=speak,args=(text,))
+      p1.start()
+      return img[:,:,::-1]
+  else:
+      text = f"No Hand detected, please use your right hand for correct output!"
+      p1=Thread(target=speak,args=(text,)) 
+      p1.start()
+      return cv2.flip(img[:,:,::-1],1)
+
+with gr.Blocks() as demo:
+  with gr.Tabs():
+    with gr.TabItem('Upload'):
+      with gr.Row():
+        with gr.Column():
+          img_input = gr.Image(shape=(640,480))
+          image_button = gr.Button("Submit")
+
+        with gr.Column():
+          output = gr.Image()
+      with gr.Row():
+        example_images = gr.Dataset(components=[img_input],samples=[["ex2.jpg"]])
+
+    with gr.TabItem('Webcam'):
+      with gr.Row():
+        with gr.Column():
+          img_input2 = gr.Webcam()
+          image_button2 = gr.Button("Submit")
+
+        with gr.Column():
+          output2 = gr.outputs.Image()
+
+    image_button.click(fn=count,
+        inputs = img_input,
+        outputs = output)        
+    image_button2.click(fn=count,
+        inputs = img_input2,
+        outputs = output2)
+    example_images.click(fn=set_example_image,inputs=[example_images],outputs=[img_input])
+
+ 
+demo.launch(debug=True)