chinmaydas96

import matplotlib.image as mpimg

def run_app(img_path):
    ## handle cases for a human face, dog, and neither
    if (face_detector(img_path)):
        print('hello, human!')
    else:
        print('hello, Dog!')

    img = mpimg.imread(img_path)

chinmaydas96

from extract_bottleneck_features import *

def Xception_predict_breed(img_path):
    # extract bottleneck features
    bottleneck_feature = extract_Xception(path_to_tensor(img_path))
    # obtain predicted vector
    predicted_vector = Xception_model.predict(bottleneck_feature)
    # return dog breed that is predicted by the model
    return dog_names[np.argmax(predicted_vector)]

chinmaydas96

from tensorflow.keras.callbacks import ModelCheckpoint  


model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

epochs = 25
checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.from_scratch.hdf5', 
                               verbose=1, save_best_only=True)
                               
model.fit(train_tensors, train_targets, 

chinmaydas96

from tensorflow.keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D
from tensorflow.keras.layers import Dropout, Flatten, Dense
from tensorflow.keras.models import Sequential

model = Sequential()

model.add(Conv2D(16,(2,2),activation='relu',input_shape=(224,224,3)))
model.add(MaxPooling2D(2,2))
model.add(Dropout(0.2))
model.add(Conv2D(32,(2,2),activation='relu'))

chinmaydas96

from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
from tensorflow.keras.preprocessing import image                  
from tqdm import tqdm

# define ResNet50 model
ResNet50_model = ResNet50(weights='imagenet')

def path_to_tensor(img_path):
    # loads RGB image as PIL.Image.Image type

chinmaydas96

# returns "True" if face is detected in image stored at img_path
def face_detector(img_path):
    img = cv2.imread(img_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray)
    return len(faces) > 0

chinmaydas96

import cv2                
import matplotlib.pyplot as plt                        
%matplotlib inline                               

# extract pre-trained face detector
face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')

# load color (BGR) image
img = cv2.imread(human_files[3])
# convert BGR image to grayscale

chinmaydas96

# define function to load train, test, and validation datasets
def load_dataset(path):
    data = load_files(path)
    dog_files = np.array(data['filenames'])
    dog_targets = np_utils.to_categorical(np.array(data['target']), 133)
    return dog_files, dog_targets

# load train, test, and validation datasets
train_files, train_targets = load_dataset('dogImages/train')
valid_files, valid_targets = load_dataset('dogImages/valid')

chinmaydas96

╔══════╦═════════════════╦═══════════╗
║      ║ Chopstick.Length║  F.P.E    ║
╠══════╬═════════════════╬═══════════╣
║  0   ║      180        ║ 24.935161 ║
║  1   ║      180        ║ 25.483871 ║
║  2   ║      180        ║ 26.322903 ║
║  3   ║      180        ║ 24.323871 ║
║  4   ║      180        ║ 24.968065 ║        
║  5   ║      180        ║ 23.999677 ║ 
╚══════╩═════════════════╩═══════════╝

chinmaydas96

╔══════╦══════╦═══════════════════════════════╗
║      ║F.P.E ║Indivisual ║ Chopstick.Length  ║
╠══════╬══════╬═══════════╣═══════════════════║
║  0   ║ 19.55║    1      ║        180        ║
║  1   ║ 27.24║    2      ║        180        ║
║  2   ║ 28.76║    3      ║        180        ║
║  3   ║ 31.19║    4      ║        180        ║
║  4   ║ 21.91║    5      ║        180        ║
╚══════╩══════╩═══════════╝═══════════════════╝