基于CNN卷积神经网络的衣服识别系统python源码+运行说明.zip

import tqdm import numpy as np from matplotlib import pyplot as plt import cv2 as cv import tensorflow as tf from tensorflow.keras.models import * from tensorflow.keras.layers import * from tensorflow.keras.losses import * from tensorflow.keras.optimizers import * from tensorflow.keras import backend as K original = cv.imread('original77.jpg') original = cv.resize(original,(224,224)) dress = cv.imread('dress77.jpg') dress = cv.resize(dress,(224,224)) body = cv.imread('body77.jpg') body = cv.resize(body,(224,224)) plt.figure(figsize=(16,8)) plt.subplot(1,3,1) plt.title('Original') plt.imshow(cv.cvtColor(original, cv.COLOR_BGRA2RGB)) plt.subplot(1,3,2) plt.title('Person') plt.imshow(cv.cvtColor(body, cv.COLOR_BGRA2RGB)) plt.subplot(1,3,3) plt.title('Dress') plt.imshow(cv.cvtColor(dress, cv.COLOR_BGRA2RGB)) dress = cv.imread('dress77.jpg',0) body = cv.imread('body77.jpg',0) ### ENCODE DRESS ### dress[dress == 255] = 0 dress[dress > 0] = 255 dress = cv.resize(dress,(224,224)) ### ENCODE BODY ### body[body == 255] = 0 body[body > 0] = 255 body = cv.resize(body,(224,224)) ### ENCODE SKIN ### skin = body - dress plt.figure(figsize=(16,8)) plt.subplot(1,3,1) plt.title('Person/Background') bg = (255 - body)/255 plt.imshow(bg) plt.subplot(1,3,2) plt.title('Skin') skin = (255 - skin)/255 plt.imshow(skin) plt.subplot(1,3,3) plt.title('Dress') dress = (255 - dress)/255 plt.imshow(dress) gt = np.zeros((224,224,3)) gt[:,:,0] = (1-skin) gt[:,:,1] = (1-dress) gt[:,:,2] = bg plt.figure(figsize=(6,6)) plt.imshow(gt) images_original = [] images_gt = [] mean = np.zeros((224,224,3)) n_img = 77 for i in tqdm.tqdm(range(1,n_img+1)): original = cv.imread('./original77.jpg') original = cv.resize(original,(224,224)) images_original.append(original) mean[:,:,0]=mean[:,:,0]+original[:,:,0] mean[:,:,1]=mean[:,:,1]+original[:,:,1] mean[:,:,2]=mean[:,:,2]+original[:,:,2] body = cv.imread('./body77.jpg',0) dress = cv.imread('./dress77.jpg',0) dress[dress == 255] = 0 dress[dress > 0] = 255 dress = cv.resize(dress,(224,224)) body[body == 255] = 0 body[body > 0] = 255 body = cv.resize(body,(224,224)) skin = body - dress bg = (255 - body)/255 skin = (255 - skin)/255 dress = (255 - dress)/255 gt = np.zeros((224,224,3)) gt[:,:,0] = (1-skin) gt[:,:,1] = (1-dress) gt[:,:,2] = bg images_gt.append(gt) mean = mean / n_img mean = mean.reshape((-1,224,224,3)) images_original = np.asarray(images_original) images_gt = np.asarray(images_gt) def get_unet(mean_pixels, do_rate=0): inputs = Input((None, None, 3)) preproc = Lambda(lambda x: (x - tf.constant(mean_pixels, dtype=tf.float32)))(inputs) conv1 = Dropout(do_rate)(Conv2D(32, (3, 3), padding='same', activation='relu')(preproc)) conv1 = Dropout(do_rate)(Conv2D(32, (3, 3), padding='same', activation='relu')(conv1)) pool1 = MaxPooling2D(pool_size=(2, 2))(conv1) conv2 = Dropout(do_rate)(Conv2D(64, (3, 3), padding='same', activation='relu')(pool1)) conv2 = Dropout(do_rate)(Conv2D(64, (3, 3), padding='same', activation='relu')(conv2)) pool2 = MaxPooling2D(pool_size=(2, 2))(conv2) conv3 = Dropout(do_rate)(Conv2D(128, (3, 3), padding='same', activation='relu')(pool2)) conv3 = Dropout(do_rate)(Conv2D(128, (3, 3), padding='same', activation='relu')(conv3)) pool3 = MaxPooling2D(pool_size=(2, 2))(conv3) conv4 = Dropout(do_rate)(Conv2D(256, (3, 3), padding='same', activation='relu')(pool3)) conv4 = Dropout(do_rate)(Conv2D(256, (3, 3), padding='same', activation='relu')(conv4)) pool4 = MaxPooling2D(pool_size=(2, 2))(conv4) conv5 = Dropout(do_rate)(Conv2D(512, (3, 3), padding='same', activation='relu')(pool4)) conv5 = Dropout(do_rate)(Conv2D(512, (3, 3), padding='same', activation='relu')(conv5)) up6 = concatenate([Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(conv5), conv4], axis=3) conv6 = Dropout(do_rate)(Conv2D(256, (3, 3), padding='same', activation='relu')(up6)) conv6 = Dropout(do_rate)(Conv2D(256, (3, 3), padding='same', activation='relu')(conv6)) up7 = concatenate([Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(conv6), conv3], axis=3) conv7 = Dropout(do_rate)(Conv2D(128, (3, 3), padding='same', activation='relu')(up7)) conv7 = Dropout(do_rate)(Conv2D(128, (3, 3), padding='same', activation='relu')(conv7)) up8 = concatenate([Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(conv7), conv2], axis=3) conv8 = Dropout(do_rate)(Conv2D(64, (3, 3), padding='same', activation='relu')(up8)) conv8 = Dropout(do_rate)(Conv2D(64, (3, 3), padding='same', activation='relu')(conv8)) up9 = concatenate([Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(conv8), conv1], axis=3) conv9 = Dropout(do_rate)(Conv2D(32, (3, 3), padding='same', activation='relu')(up9)) conv9 = Dropout(do_rate)(Conv2D(32, (3, 3), padding='same', activation='relu')(conv9)) conv10 = Conv2D(3, (1, 1), activation='sigmoid')(conv9) model = Model(inputs=inputs, outputs=conv10) model.compile(optimizer=Adam(lr=1e-3), loss='binary_crossentropy', metrics=['accuracy']) return model model = get_unet(mean) model.fit(images_original, images_gt, epochs=120) model.save('./fashion_unet.h5')