app.py

import cv2
from cvzone.HandTrackingModule import HandDetector
from cvzone.ClassificationModule import Classifier
import numpy as np
import math
import gradio as gr

detector = HandDetector(maxHands=1)
classifier = Classifier("ModelFull/keras_model.h5", "ModelFull/labels.txt")

offset = 20
imgSize = 300

folder = "Data/C"
counter = 0

labels = ["A", "B","C","D","E","F","G","H","I","J","K","L","M","N", "O","P","Q","R","S","T","U","V","W","X","Y","Z"]

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']
        imlist = hand['lmList']
        print(imlist)
        if ((imlist[10][0] < imlist[4][0] < imlist[6][0]) or (imlist[6][0] < imlist[4][0] < imlist[10][0])):
          if ((imlist[4][1] < imlist[8][1]) and (imlist[4][1] < imlist[12][1]) ):
            print('In T')
            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, (0,30),(80,80), (255, 0, 255), cv2.FILLED)
            cv2.putText(imgOutput, 'T', (20, 75), cv2.FONT_HERSHEY_COMPLEX, 1.7, (255, 255, 255), 2)
            return imgOutput
          else:
            print('In K')
            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, (0,30),(80,80), (255, 0, 255), cv2.FILLED)
            cv2.putText(imgOutput, 'K', (20, 75), cv2.FONT_HERSHEY_COMPLEX, 1.7, (255, 255, 255), 2)
            return imgOutput            
        '''if imlist[4][0]>imlist[8][0] and imlist[4][0]>imlist[12][0] and imlist[4][0]>imlist[16][0] and imlist[4][0]>imlist[20][0]:
          print('In M')
          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, (0,30),(80,80), (255, 0, 255), cv2.FILLED)
          cv2.putText(imgOutput, 'M', (20, 75), cv2.FONT_HERSHEY_COMPLEX, 1.7, (255, 255, 255), 2)
          return imgOutput'''   

        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.imwrite("check.jpg",imgWhite)
        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, (0,30),
                      (80,80), (255, 0, 255), cv2.FILLED)
        cv2.putText(imgOutput, labels[index], (20, 75), cv2.FONT_HERSHEY_COMPLEX, 1.7, (255, 255, 255), 2)


        #cv2.imshow("ImageCrop", imgCrop)
        #cv2.imshow("ImageWhite", imgWhite)

    #cv2.imshow("Image", imgOutput)
    return imgOutput

def set_example_image(example: list) -> dict:
    return gr.inputs.Image.update(value=example[0])


css = """
.gr-button-lg {
    z-index: 14;
    width: 113px;
    height: 30px;
    left: 0px;
    top: 0px;
    padding: 0px;
    cursor: pointer !important; 
    background: none rgb(17, 20, 45) !important;
    border: none !important;
    text-align: center !important;
    font-size: 14px !important;
    font-weight: 500 !important;
    color: rgb(255, 255, 255) !important;
    line-height: 1 !important;
    border-radius: 6px !important;
    transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important;
    box-shadow: none !important;
}
.gr-button-lg:hover{
    z-index: 14;
    width: 113px;
    height: 30px;
    left: 0px;
    top: 0px;
    padding: 0px;
    cursor: pointer !important; 
    background: none rgb(66, 133, 244) !important;
    border: none !important;
    text-align: center !important;
    font-size: 14px !important;
    font-weight: 500 !important;
    color: rgb(255, 255, 255) !important;
    line-height: 1 !important;
    border-radius: 6px !important;
    transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important;
    box-shadow: rgb(0 0 0 / 23%) 0px 1px 7px 0px !important;
}

footer {display:none !important} 
.output-markdown{display:none !important} 
#out_image {height: 22rem !important;}
"""

with gr.Blocks(title="American Sign Language Detection | Data Science Dojo", css=css) 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(shape=(640,480), elem_id="out_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_button2.click(fn=sign,
        inputs = img_input2,
        outputs = output2)
    image_button.click(fn=sign,
        inputs = img_input,
        outputs = output)
    example_images.click(fn=set_example_image,inputs=[example_images],outputs=[img_input])
 

demo.launch(debug=True)