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| import cv2 | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from PIL import Image | |
| from keras_vggface.vggface import VGGFace | |
| from scipy.spatial.distance import cosine | |
| from mtcnn.mtcnn import MTCNN | |
| import gradio as gr | |
| from tensorflow.keras.models import Model | |
| from tensorflow.keras.models import load_model | |
| import pathlib | |
| plt = platform.system() | |
| if plt == 'Linux': | |
| pathlib.WindowsPath = pathlib.PosixPath | |
| model = load_model("senet50.h5") | |
| def img_to_encoding(image_path, model): | |
| img = Image.open(image_path) | |
| if img is not None: | |
| img = np.array(img) | |
| img = cv2.resize(img, (224, 224), interpolation=cv2.INTER_AREA) | |
| plt.imshow(img) | |
| plt.show() | |
| x = np.expand_dims(img, axis=0) | |
| embedding = model.predict(x)[0, :] | |
| print(embedding) | |
| return embedding | |
| database = {} | |
| database["dr adnan"] = img_to_encoding( | |
| "86055fdb-7441-422e-b501-ffac2221dae0.jpg", model) | |
| database["wasay"] = img_to_encoding("Wasay (1).jpg", model) | |
| database["fatima"] = img_to_encoding("IMG_20220826_095746.jpg", model) | |
| database["omer"] = img_to_encoding("Omer_1.jpg", model) | |
| database["saad"] = img_to_encoding("IMG20220825113812.jpg", model) | |
| database["waleed"] = img_to_encoding("IMG_20220825_113352.jpg", model) | |
| database["talha"] = img_to_encoding("IMG20220825113526.jpg", model) | |
| database["asfand"] = img_to_encoding("imgAsfand.jpg", model) | |
| database["afrasiyab"] = img_to_encoding("imgAfra.jpg", model) | |
| def who_is_it(image): | |
| # START CODE HERE | |
| # Step 1: Compute the target "encoding" for the image. Use img_to_encoding() see example above. ## (≈ 1 line) | |
| if image is not None: | |
| img = cv2.resize(image, (224, 224), interpolation=cv2.INTER_AREA) | |
| x = np.expand_dims(img, axis=0) | |
| encoding = model.predict(x)[0, :] | |
| ## Step 2: Find the closest encoding ## | |
| # Initialize "min_dist" to a large value, say 100 (≈1 line) | |
| min_dist = 10000000 | |
| identity = "Not in database" | |
| # Loop over the database dictionary's names and encodings. | |
| for (name, db_enc) in database.items(): | |
| # Compute L2 distance between the target "encoding" and the current db_enc from the database. (≈ 1 line) | |
| dist = cosine(db_enc, encoding) | |
| print(dist) | |
| # If this distance is less than the min_dist, then set min_dist to dist, and identity to name. (≈ 3 lines) | |
| if dist < min_dist: | |
| min_dist = dist | |
| identity = name | |
| # END CODE HERE | |
| if min_dist < 0.3: | |
| return identity | |
| else: | |
| return ("Not in database") | |
| image = gr.inputs.Image(shape=(250, 250)) | |
| label = gr.outputs.Label() | |
| faceModel = MTCNN() | |
| faces = faceModel.detect_faces(image) | |
| for face in faces: | |
| x, y, w, h = face["box"] | |
| img = image[y:y+h, x:x+w] | |
| if img is not None: | |
| img = cv2.resize(img, (224, 224)) | |
| # prepare the image | |
| img = np.expand_dims(img, axis=0) | |
| intf = gr.Interface(fn=who_is_it, inputs=img, outputs=label) | |
| intf.launch(inline=False) | |