Upload app,py.py

app,py.py

@@ -0,0 +1,89 @@
+import cv2
+from cvzone.HandTrackingModule import HandDetector
+from cvzone.ClassificationModule import Classifier
+import numpy as np
+import math
+import gradio as gr
+
+#cap = cv2.VideoCapture(0)
+detector = HandDetector(maxHands=1)
+classifier = Classifier("Model/keras_model.h5", "Model/labels.txt")
+
+offset = 20
+imgSize = 300
+
+folder = "Data/C"
+counter = 0
+
+labels = ["A", "B"]
+
+
+def sign(img):
+    #img = cv2.imread("sign.jpg")
+    imgOutput = cv2.flip(img.copy(),1)
+    hands, img = detector.findHands(cv2.flip(img[:,:,::-1],1))
+    if hands:
+        print('hand detected')
+        hand = hands[0]
+        x, y, w, h = hand['bbox']
+
+        imgWhite = np.ones((imgSize, imgSize, 3), np.uint8) * 255
+        imgCrop = img[y - offset:y + h + offset, x - offset:x + w + offset]
+
+        imgCropShape = imgCrop.shape
+
+        aspectRatio = h / w
+
+        if aspectRatio > 1:
+            k = imgSize / h
+            wCal = math.ceil(k * w)
+            imgResize = cv2.resize(imgCrop, (wCal, imgSize))
+            imgResizeShape = imgResize.shape
+            wGap = math.ceil((imgSize - wCal) / 2)
+            imgWhite[:, wGap:wCal + wGap] = imgResize
+            prediction, index = classifier.getPrediction(imgWhite, draw=False)
+            print(prediction, index)
+
+        else:
+            k = imgSize / w
+            hCal = math.ceil(k * h)
+            imgResize = cv2.resize(imgCrop, (imgSize, hCal))
+            imgResizeShape = imgResize.shape
+            hGap = math.ceil((imgSize - hCal) / 2)
+            imgWhite[hGap:hCal + hGap, :] = imgResize
+            prediction, index = classifier.getPrediction(imgWhite, draw=False)
+
+        
+        cv2.rectangle(imgOutput, (x-offset, y-offset),
+                      (x + w+offset, y + h+offset), (255, 0, 255), 4)
+        imgOutput = cv2.flip(imgOutput,1)
+        #cv2.rectangle(imgOutput, (x - offset, y - offset-50),
+        #              (x - offset+90, y - offset-50+50), (255, 0, 255), cv2.FILLED)
+        #cv2.putText(imgOutput, labels[index], (x, y -26), cv2.FONT_HERSHEY_COMPLEX, 1.7, (255, 255, 255), 2)
+        cv2.rectangle(imgOutput, (30,30),
+                      (80,80), (255, 0, 255), cv2.FILLED)
+        cv2.putText(imgOutput, labels[index], (30, 80), cv2.FONT_HERSHEY_COMPLEX, 1.7, (255, 255, 255), 2)
+
+
+        #cv2.imshow("ImageCrop", imgCrop)
+        #cv2.imshow("ImageWhite", imgWhite)
+
+    #cv2.imshow("Image", imgOutput)
+    return imgOutput
+
+with gr.Blocks() as demo:
+  with gr.Tabs():
+    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_button2.click(fn=sign,
+        inputs = img_input2,
+        outputs = output2) 
+
+demo.launch(debug=True)