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| import gradio as gr | |
| import tensorflow as tf | |
| import numpy as np | |
| from numpy import asarray | |
| from datetime import datetime | |
| import cv2 | |
| model = tf.keras.models.load_model("simple-CNN-model.2022-8-9.hdf5") | |
| def image_predict(img): | |
| """ | |
| Displays dominant colors beyond a given threshold. | |
| img : image input, for ex 'blue-car.jpg' | |
| isize: input image size, for ex. 227 | |
| thr: chosen threshold value | |
| """ | |
| thr=0 | |
| global model | |
| if model is None: | |
| model = tf.keras.models.load_model("models/simple-CNN-model.2022-8-9.hdf5") | |
| #img = img.reshape((None, 227, 227, 3)) | |
| img = cv2.resize(img, (227, 227)) | |
| data = np.asarray(img) | |
| ndata = np.expand_dims(data, axis=0) | |
| y_prob = model.predict(ndata/255) | |
| #y_prob.argmax(axis=-1) | |
| now = datetime.now() | |
| print("--------") | |
| print("data and time: ", now) | |
| colorlabels = ['beige', 'black', 'blue', 'brown', 'gold', 'green', 'grey', 'orange', 'pink', 'purple', 'red', 'silver', 'tan', 'white', 'yellow'] | |
| coltags = [sorted(colorlabels)[i] for i in np.where(np.ravel(y_prob)>thr)[0]] | |
| colprob = [np.ravel(y_prob)[i] for i in list(np.where(np.ravel(y_prob)>thr)[0])] | |
| if len(coltags) > 0: | |
| response = [] | |
| for i,j in zip(coltags, colprob): | |
| #print(i,j) | |
| resp = {} | |
| resp[i] = float(j) | |
| response.append(resp) | |
| d = dict(map(dict.popitem, response)) | |
| print('colors: ', d) | |
| return dict(d) | |
| else: | |
| return str('No label was found') | |
| iface = gr.Interface( | |
| title = "Object color tagging", | |
| description = "App classifying objects on different colors", | |
| article = "<p style='text-align: center'><a href='https://www.rrighart.com' target='_blank'>Webpage</a></p>", | |
| fn=image_predict, | |
| inputs=gr.Image(width=227, height=227), # shape=(227,227) | |
| outputs=gr.Label(), | |
| examples=['shoes1.jpg', 'shoes2.jpg'], | |
| ) | |
| iface.launch() | |