पायथन + ओपनसीवी + केर: आधे घंटे में पाठ की पहचान करें

 # Code source: dmitryelj@gmail.com import os # Force CPU # os.environ["CUDA_VISIBLE_DEVICES"] = "-1" # Debug messages # 0 = all messages are logged (default behavior) # 1 = INFO messages are not printed # 2 = INFO and WARNING messages are not printed # 3 = INFO, WARNING, and ERROR messages are not printed os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' import cv2 import imghdr import numpy as np import pathlib from tensorflow import keras from keras.models import Sequential from keras import optimizers from keras.layers import Convolution2D, MaxPooling2D, Dropout, Flatten, Dense, Reshape, LSTM, BatchNormalization from keras.optimizers import SGD, RMSprop, Adam from keras import backend as K from keras.constraints import maxnorm import tensorflow as tf from scipy import io as spio import idx2numpy # sudo pip3 install idx2numpy from matplotlib import pyplot as plt from typing import * import time # Dataset: # https://www.nist.gov/node/1298471/emnist-dataset # https://www.itl.nist.gov/iaui/vip/cs_links/EMNIST/gzip.zip def cnn_print_digit(d): print(d.shape) for x in range(28): s = "" for y in range(28): s += "{0:.1f} ".format(d[28*y + x]) print(s) def cnn_print_digit_2d(d): print(d.shape) for y in range(d.shape[0]): s = "" for x in range(d.shape[1]): s += "{0:.1f} ".format(d[x][y]) print(s) emnist_labels = [48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122] def emnist_model(): model = Sequential() model.add(Convolution2D(filters=32, kernel_size=(3, 3), padding='valid', input_shape=(28, 28, 1), activation='relu')) model.add(Convolution2D(filters=64, kernel_size=(3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(512, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(len(emnist_labels), activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy']) return model def emnist_model2(): model = Sequential() # In Keras there are two options for padding: same or valid. Same means we pad with the number on the edge and valid means no padding. model.add(Convolution2D(filters=32, kernel_size=(3, 3), activation='relu', padding='same', input_shape=(28, 28, 1))) model.add(MaxPooling2D((2, 2))) model.add(Convolution2D(64, (3, 3), activation='relu', padding='same')) model.add(MaxPooling2D((2, 2))) model.add(Convolution2D(128, (3, 3), activation='relu', padding='same')) model.add(MaxPooling2D((2, 2))) # model.add(Conv2D(128, (3, 3), activation='relu', padding='same')) # model.add(MaxPooling2D((2, 2))) ## model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(512, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(len(emnist_labels), activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy']) return model def emnist_model3(): model = Sequential() model.add(Convolution2D(filters=32, kernel_size=(3, 3), padding='same', input_shape=(28, 28, 1), activation='relu')) model.add(Convolution2D(filters=32, kernel_size=(3, 3), padding='same', activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Convolution2D(filters=64, kernel_size=(3, 3), padding='same', activation='relu')) model.add(Convolution2D(filters=64, kernel_size=(3, 3), padding='same', activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(512, activation="relu")) model.add(Dropout(0.5)) model.add(Dense(len(emnist_labels), activation="softmax")) model.compile(loss='categorical_crossentropy', optimizer=RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0), metrics=['accuracy']) return model def emnist_train(model): t_start = time.time() emnist_path = 'D:\\Temp\\1\\' X_train = idx2numpy.convert_from_file(emnist_path + 'emnist-byclass-train-images-idx3-ubyte') y_train = idx2numpy.convert_from_file(emnist_path + 'emnist-byclass-train-labels-idx1-ubyte') X_test = idx2numpy.convert_from_file(emnist_path + 'emnist-byclass-test-images-idx3-ubyte') y_test = idx2numpy.convert_from_file(emnist_path + 'emnist-byclass-test-labels-idx1-ubyte') X_train = np.reshape(X_train, (X_train.shape[0], 28, 28, 1)) X_test = np.reshape(X_test, (X_test.shape[0], 28, 28, 1)) print(X_train.shape, y_train.shape, X_test.shape, y_test.shape, len(emnist_labels)) # Test: k = 10 X_train = X_train[:X_train.shape[0] // k] y_train = y_train[:y_train.shape[0] // k] X_test = X_test[:X_test.shape[0] // k] y_test = y_test[:y_test.shape[0] // k] # Normalize X_train = X_train.astype(np.float32) X_train /= 255.0 X_test = X_test.astype(np.float32) X_test /= 255.0 x_train_cat = keras.utils.to_categorical(y_train, len(emnist_labels)) y_test_cat = keras.utils.to_categorical(y_test, len(emnist_labels)) # Set a learning rate reduction learning_rate_reduction = keras.callbacks.ReduceLROnPlateau(monitor='val_acc', patience=3, verbose=1, factor=0.5, min_lr=0.00001) # Required for learning_rate_reduction: keras.backend.get_session().run(tf.global_variables_initializer()) model.fit(X_train, x_train_cat, validation_data=(X_test, y_test_cat), callbacks=[learning_rate_reduction], batch_size=64, epochs=30) print("Training done, dT:", time.time() - t_start) def emnist_predict(model, image_file): img = keras.preprocessing.image.load_img(image_file, target_size=(28, 28), color_mode='grayscale') emnist_predict_img(model, img) def emnist_predict_img(model, img): img_arr = np.expand_dims(img, axis=0) img_arr = 1 - img_arr/255.0 img_arr[0] = np.rot90(img_arr[0], 3) img_arr[0] = np.fliplr(img_arr[0]) img_arr = img_arr.reshape((1, 28, 28, 1)) result = model.predict_classes([img_arr]) return chr(emnist_labels[result[0]]) def letters_extract(image_file: str, out_size=28): img = cv2.imread(image_file) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY) img_erode = cv2.erode(thresh, np.ones((3, 3), np.uint8), iterations=1) # Get contours contours, hierarchy = cv2.findContours(img_erode, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) output = img.copy() letters = [] for idx, contour in enumerate(contours): (x, y, w, h) = cv2.boundingRect(contour) # print("R", idx, x, y, w, h, cv2.contourArea(contour), hierarchy[0][idx]) # hierarchy[i][0]: the index of the next contour of the same level # hierarchy[i][1]: the index of the previous contour of the same level # hierarchy[i][2]: the index of the first child # hierarchy[i][3]: the index of the parent if hierarchy[0][idx][3] == 0: cv2.rectangle(output, (x, y), (x + w, y + h), (70, 0, 0), 1) letter_crop = gray[y:y + h, x:x + w] # print(letter_crop.shape) # Resize letter canvas to square size_max = max(w, h) letter_square = 255 * np.ones(shape=[size_max, size_max], dtype=np.uint8) if w > h: # Enlarge image top-bottom # ------ # ====== # ------ y_pos = size_max//2 - h//2 letter_square[y_pos:y_pos + h, 0:w] = letter_crop elif w < h: # Enlarge image left-right # --||-- x_pos = size_max//2 - w//2 letter_square[0:h, x_pos:x_pos + w] = letter_crop else: letter_square = letter_crop # Resize letter to 28x28 and add letter and its X-coordinate letters.append((x, w, cv2.resize(letter_square, (out_size, out_size), interpolation=cv2.INTER_AREA))) # Sort array in place by X-coordinate letters.sort(key=lambda x: x[0], reverse=False) # cv2.imshow("Input", img) # # cv2.imshow("Gray", thresh) # cv2.imshow("Enlarged", img_erode) # cv2.imshow("Output", output) # cv2.imshow("0", letters[0][2]) # cv2.imshow("1", letters[1][2]) # cv2.imshow("2", letters[2][2]) # cv2.imshow("3", letters[3][2]) # cv2.imshow("4", letters[4][2]) # cv2.waitKey(0) return letters def img_to_str(model: Any, image_file: str): letters = letters_extract(image_file) s_out = "" for i in range(len(letters)): dn = letters[i+1][0] - letters[i][0] - letters[i][1] if i < len(letters) - 1 else 0 s_out += emnist_predict_img(model, letters[i][2]) if (dn > letters[i][1]/4): s_out += ' ' return s_out if __name__ == "__main__": # model = emnist_model() # emnist_train(model) # model.save('emnist_letters.h5') model = keras.models.load_model('emnist_letters.h5') s_out = img_to_str(model, "hello_world.png") print(s_out)