| 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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|
|
|
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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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|
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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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|
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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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|
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| predicted_character = labels[prediction[0]]
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|
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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() |
