diff --git a/app.py b/app.py index a0b0c8903be8c0c5f7d0b1734ebe791988bc615b..dce2c806277d89a63cefc6d0fdbea5a46cc5b3ba 100644 --- a/app.py +++ b/app.py @@ -10,15 +10,15 @@ from sklearn.svm import SVC def calc_embeddings(all_files, names): detector = cv2.dnn.readNetFromCaffe( - "../deploy.prototxt.txt", "../res10_300x300_ssd_iter_140000.caffemodel" + "deploy.prototxt.txt", "res10_300x300_ssd_iter_140000.caffemodel" ) - embedder = cv2.dnn.readNetFromTorch("../openface.nn4.small2.v1.t7") + embedder = cv2.dnn.readNetFromTorch("openface.nn4.small2.v1.t7") knownNames = [] knownEmbeddings = [] total = 0 for file in all_files: name = names[total] - path = os.path.join(os.getcwd(), name, file) + path = os.path.join(os.getcwd(), 'celeb_dataset', name, file) # f = open(f"/celeb_dataset/'{name}'/{file}", "rb") f = open(path, "rb") file_bytes = np.asarray(bytearray(f.read()), dtype=np.uint8) @@ -57,9 +57,9 @@ def calc_embeddings(all_files, names): knownNames.append(name) knownEmbeddings.append(vec.flatten()) total += 1 - with open("../unknownEmbeddings.pkl", "rb") as fp: + with open("unknownEmbeddings.pkl", "rb") as fp: l = pickle.load(fp) - with open("../unknownNames.pkl", "rb") as fp: + with open("unknownNames.pkl", "rb") as fp: n = pickle.load(fp) for i in l: knownEmbeddings.append(i) @@ -79,27 +79,27 @@ def recognize(embeddings, names): def run_inference(myImage): - os.chdir("celeb_dataset") + # os.chdir("./celeb_dataset") celebs = [] scores = dict() - for celeb in os.listdir(): + for celeb in os.listdir("./celeb_dataset"): files = [] names = [] if celeb in celebs: continue name = celeb celebs.append(name) - for file in os.listdir(celeb): + for file in os.listdir(os.path.join(os.getcwd(), 'celeb_dataset', celeb)): files.append(file) names.append(name) embeddings, names = calc_embeddings(files, names) le, model = recognize(embeddings, names) detector = cv2.dnn.readNetFromCaffe( - "../deploy.prototxt.txt", - "../res10_300x300_ssd_iter_140000.caffemodel", + "deploy.prototxt.txt", + "res10_300x300_ssd_iter_140000.caffemodel", ) - embedder = cv2.dnn.readNetFromTorch("../openface.nn4.small2.v1.t7") + embedder = cv2.dnn.readNetFromTorch("openface.nn4.small2.v1.t7") (h, w) = myImage.shape[:2] imageBlob = cv2.dnn.blobFromImage( cv2.resize(myImage, (300, 300)), @@ -130,10 +130,11 @@ def run_inference(myImage): j = np.argmax(preds) proba = preds[j] name = le.classes_[j] - # text = "{}: {:.2f}%".format(name, proba * 100) - scores[name] = proba * 100 - result = sorted(scores.items(), key=lambda x: x[1], reverse=True) - return result + text = "{}: {:.2f}%".format(name, proba * 100) + scores[name] = proba + if len(scores)>1: + del scores["Unknown"] + return scores iface = gr.Interface( diff --git a/celeb_dataset/Angelina Jolie/006_9135205d.jpg b/celeb_dataset/Angelina Jolie/006_9135205d.jpg new file mode 100644 index 0000000000000000000000000000000000000000..a52edd0f3a08c10d0ad808aa6113d2fcaeec4bf6 --- 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