辛普森一家的人物分类

Sampsons.py

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+import keras
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Activation, Flatten
+from keras.layers import Conv2D, MaxPooling2D
+from mpl_toolkits.axes_grid1 import AxesGrid
+from keras.optimizers import SGD
+import os
+
+map_characters = {0 : 'abraham_grampa_simpson',
+                  1 : 'agnes_skinner',
+                  2 : 'apu_nahasapeemapetilon',
+                  3 : 'barney_gumble',
+                  4 : 'bart_simpson',
+                  5 : 'carl_carlson',
+                  6 : 'charles_montgomery_burns',
+                  7 : 'chief_wiggum',
+                  8 : 'cletus_spuckler',
+                  9 : 'comic_book_guy',
+                  10 : 'disco_stu',
+                  11 : 'edna_krabappel',
+                  12 : 'fat_tony',
+                  13 : 'gil',
+                  14 : 'groundskeeper_willie',
+                  15 : 'homer_simpson',
+                  16 : 'kent_brockman',
+                  17 : 'krusty_the_clown',
+                  18 : 'lenny_leonard',
+                  19 : 'lionel_hutz',
+                  20 : 'lisa_simpson',
+                  21 : 'maggie_simpson',
+                  22 : 'marge_simpson',
+                  23 : 'martin_prince',
+                  24 : 'mayor_quimby',
+                  25 : 'milhouse_van_houten',
+                  26 : 'miss_hoover',
+                  27 : 'moe_szyslak',
+                  28 : 'ned_flanders',
+                  29 : 'nelson_muntz',
+                  30 : 'otto_mann',
+                  31 : 'patty_bouvier',
+                  32 : 'principal_skinner',
+                  33 : 'professor_john_frink',
+                  34 : 'rainier_wolfcastle',
+                  35 : 'ralph_wiggum',
+                  36 : 'selma_bouvier',
+                  37 : 'sideshow_bob',
+                  38 : 'sideshow_mel',
+                  39 : 'snake_jailbird',
+                  40 : 'troy_mcclure',
+                  41 : 'waylon_smithers'}
+check_characters = {v:k for k,v in map_characters.items()}
+
+pic_size = 64
+batch_size = 32
+epochs = 200
+num_classes = len(map_characters)
+
+def create_model_six_conv(input_shape):
+    model = Sequential()
+    model.add(Conv2D(32, (3,3), padding='same', input_shape=input_shape))
+    model.add(Activation('relu'))
+    model.add(Conv2D(32, (3,3)))
+    model.add(Activation('relu'))
+    model.add(MaxPooling2D(pool_size=(2,2)))
+    model.add(Dropout(0.2))
+
+    model.add(Conv2D(64, (3,3), padding='same'))
+    model.add(Activation('relu'))
+    model.add(Conv2D(64, (3,3)))
+    model.add(Activation('relu'))
+    model.add(MaxPooling2D(pool_size=(2,2)))
+    model.add(Dropout(0.2))
+
+    model.add(Conv2D(256, (3,3), padding='same'))
+    model.add(Activation('relu'))
+    model.add(Conv2D(256, (3,3)))
+    model.add(Activation('relu'))
+    model.add(MaxPooling2D(pool_size=(2,2)))
+    model.add(Dropout(0.2))
+
+    model.add(Flatten())
+    model.add(Dense(1024))
+    model.add(Activation('relu'))
+    model.add(Dropout(0.5))
+    model.add(Dense(18, activation='softmax'))
+    opt = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
+
+    return model, opt
+
+def load_model_from_checkpoint(weights_path, input_shape=(pic_size,pic_size,3)):
+    model, opt = create_model_six_conv(input_shape)
+    model.load_weights(weights_path)
+    model.compile(loss='categorical_crossentropy',
+                  optimizer=opt,
+                  metrics=['accuracy'])
+    return model
+
+def load_test_set(path):
+    pics, labels = [], []
+    reverse_dict = {v:k for k,v in map_characters.items()}
+    paths = os.listdir(path)
+    for pic in paths:
+        pic = path + pic
+        char_name = "_".join(pic.split('/')[3].split('_')[:-1])
+        if char_name in reverse_dict.keys():
+            temp = cv2.imread(pic)
+            temp = cv2.resize(temp, (pic_size, pic_size)).astype('float32')/255
+            pics.append(temp)
+            labels.append(reverse_dict[char_name])
+    X_test = np.array(pics)
+    y_test = np.array(labels)
+    y_test = keras.utils.to_categorical(y_test, num_classes)
+    print('Test set', X_test.shape, y_test.shape)
+    return X_test, y_test
+
+model = load_model_from_checkpoint(r'E:\the-simpsons-characters-dataset\weights.best.hdf5')
+#X_test, y_test = load_test_set('E:/the-simpsons-characters-dataset/kaggle_simpson_testset/')
+
+def img_pro(path):
+    F = plt.figure(1, (15,20))
+    grid = AxesGrid(F, 111, nrows_ncols=(4,4), axes_pad=0, label_mode='1')
+
+
+    for i in range(16):
+        char = map_characters[i]
+        list = [path+k for k in os.listdir(path) if char in k]
+        image = cv2.imread(np.random.choice(list))
+
+
+        img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        pic = cv2.resize(image, (pic_size,pic_size)).astype('float32')/255
+        a = model.predict(pic.reshape(1, pic_size, pic_size, 3))[0]
+        actual = char.split('_')[0].title()
+        text = sorted(['{:s} : {:.1f}%'.format(map_characters[i].split('_')[0].title(), 100*v) for k,v in enumerate(a)],
+                      key=lambda x:float(x.split(':')[1].split('%')[0]), reverse=True)[:3]
+        img = cv2.resize(img, (352, 352))
+        cv2.rectangle(img, (0,260), (215,352), (255,255,255), -1)
+        font = cv2.FONT_HERSHEY_SIMPLEX
+        cv2.putText(img, 'Actual : %s' % actual, (10,280), font, 0.7, (0,0,0), 2, cv2.LINE_AA)
+        for k,t in enumerate(text):
+            cv2.putText(img, t, (10, 300+k*18), font, 0.65, (0,0,0), 2, cv2.LINE_AA)
+        grid[i].imshow(img)
+    plt.show()
+path = 'E:/the-simpsons-characters-dataset/kaggle_simpson_testset/'     #修改为自己的路径
+img_pro(path)
+
+def video_pro(path):
+    video_capture = cv2.VideoCapture(path)
+    fps = video_capture.get(cv2.CAP_PROP_FPS)
+    size = (int(video_capture.get(cv2.CAP_PROP_FRAME_WIDTH)),
+            int(video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
+    video_write = cv2.VideoWriter('test.avi', cv2.VideoWriter_fourcc('m','p','4','v'), fps, size)
+    success, frame = video_capture.read()
+    while success:
+        img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        pic = cv2.resize(frame, (pic_size,pic_size)).astype('float32')/255
+        a = model.predict(pic.reshape(1, pic_size, pic_size, 3))[0]
+        text = sorted(['{:s} : {:.1f}%'.format(map_characters[k].split('_')[0].title(), 100*v) for k,v in enumerate(a)],
+                      key=lambda x:float(x.split(':')[1].split('%')[0]), reverse=True)[:3]
+        img = cv2.resize(img, (size[0], size[1]))
+        font = cv2.FONT_HERSHEY_SIMPLEX
+        for k,t in enumerate(text):
+            cv2.putText(img, t, (10, 680+k*18), font, 0.65, (0,0,0), 2, cv2.LINE_AA)
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        video_write.write(img)
+        success, frame = video_capture.read()
+    video_write.release()
+    video_capture.release()
+path = r'D:\app\pycharm\codes\deeplearning\HongyiLi\yyy.mp4'    #修改为自己的路径
+video_pro((path))