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import pickle
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import cv2
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import mediapipe as mp
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import numpy as np
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from PIL import Image
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import requests
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from io import BytesIO
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import gradio as gr
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model_dict = pickle.load(open('stacked_model_new.p', 'rb'))
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labels = ['A','B','C','D','E','F','G','H','I','K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y']
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model = model_dict['model']
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def predict(url):
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response = requests.get(url)
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print(response)
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img = Image.open(BytesIO(response.content))
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img.save('image.jpg')
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mp_hands = mp.solutions.hands
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mp_drawing = mp.solutions.drawing_utils
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mp_drawing_styles = mp.solutions.drawing_styles
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hands = mp_hands.Hands(static_image_mode=False, min_detection_confidence=0.3)
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hands.maxHands = 1
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data_aux = []
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x_ = []
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y_ = []
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frame = cv2.imread('image.jpg')
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H,W, _ = frame.shape
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frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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results = hands.process(frame_rgb)
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if results.multi_hand_landmarks:
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if(len(results.multi_hand_landmarks) == 1):
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for hand_landmarks in results.multi_hand_landmarks:
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for i in range(len(hand_landmarks.landmark)):
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x = hand_landmarks.landmark[i].x
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y = hand_landmarks.landmark[i].y
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x_.append(x)
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y_.append(y)
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for i in range(len(hand_landmarks.landmark)):
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x = hand_landmarks.landmark[i].x
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y = hand_landmarks.landmark[i].y
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data_aux.append(x - min(x_))
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data_aux.append(y - min(y_))
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x1 = int(min(x_) * W) - 10
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y1 = int(min(y_) * H) - 10
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x2 = int(max(x_) * W) - 10
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y2 = int(max(y_) * H) - 10
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if(len(data_aux) == 42):
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prediction = model.predict([np.asarray(data_aux)])
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predicted_character = labels[prediction[0]]
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return {"prediction":predicted_character}
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else:
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return {"prediction": "Too many Hands"}
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iface = gr.Interface(fn=predict, inputs="image", outputs="text", title="Image to Text Model")
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iface.launch() |
