AI/Events/sepration_cluster.py

import sys
import os
import dlib
import glob
import time
import uuid
from main_application import *

import click
@click.command()
@click.argument('eventid', default='')

def sep_clust(eventid):
    Gallery = 'C:\\Users\\Administrator\\Documents\\AI\\runtimecropimages\\front_face\\' + eventid + "\\"
    start = time.time()

    # if len(sys.argv) != 3:
    #     print("Please specify valid arguments. Call the program like this \npython face_clustering.py -specify input folder- -specify output path-")
    # exit()

    predictor_path = 'C:\\Users\\Administrator\\Documents\\AI\\runtimecropimages\\model\\shape_predictor_68_face_landmarks.dat'
    face_rec_model_path = 'C:\\Users\\Administrator\\Documents\\AI\\runtimecropimages\\model\\dlib_face_recognition_resnet_model_v1.dat'
    # faces_folder_path = sys.argv[1]
    output_folder = 'C:\\Users\\Administrator\\Documents\\AI\\runtimecropimages\\sepration_cluster\\' + eventid + "\\"
    import os
    import shutil

    files = output_folder

    for root, dirs, files in os.walk(files):
        for f in files:
            os.unlink(os.path.join(root, f))
        for d in dirs:
            shutil.rmtree(os.path.join(root, d))


    detector = dlib.get_frontal_face_detector()  # a detector to find the faces
    sp = dlib.shape_predictor(predictor_path)  # shape predictor to find face landmarks
    facerec = dlib.face_recognition_model_v1(face_rec_model_path)  # face recognition model

    descriptors = []
    images = []

    for root, dirs, files in os.walk(Gallery, topdown=False):

        for name in files:
            f = os.path.join(root, name)

            # Load the images from input folder
            # for f in glob.glob(os.path.join(faces_folder_path, "*")):
            print("Processing file: {}".format(f))
            img = dlib.load_rgb_image(f)

            # Ask the detector to find the bounding boxes of each face. The 1 in the second argument indicates that we should upsample the image 1 time. This will make everything bigger and allow us to detect more faces.
            dets = detector(img, 1)
            print("Number of faces detected: {}".format(len(dets)))

            # Now process each face we found.
            for k, d in enumerate(dets):
                # Get the landmarks/parts for the face in box d.
                shape = sp(img, d)

                # Compute the 128D vector that describes the face in img identified by shape.
                face_descriptor = facerec.compute_face_descriptor(img, shape)
                descriptors.append(face_descriptor)
                images.append((img, shape))

    # Cluster the faces.
    labels = dlib.chinese_whispers_clustering(descriptors, 0.40)
    num_classes = len(set(labels))  # Total number of clusters
    print("Number of clusters: {}".format(num_classes))

    for i in range(0, num_classes):
        indices = []
        class_length = len([label for label in labels if label == i])
        for j, label in enumerate(labels):
            if label == i:
                indices.append(j)
        print("Indices of images in the cluster {0} : {1}".format(str(i), str(indices)))
        print("Size of cluster {0} : {1}".format(str(i), str(class_length)))
        output_folder_path = output_folder + str(i)  # Output folder for each cluster
        os.path.normpath(output_folder_path)
        os.makedirs(output_folder_path)

        # Save each face to the respective cluster folder
        print("Saving faces to output folder...")
        for k, index in enumerate(indices):
            img, shape = images[index]
            x = img

            # file_path2=os.path.join("C:/Users/katku/Desktop/spyder/192.168.89.91_windows/final_crop_cluster_FaceRecognition/unique/",str(uuid.uuid4().hex[:15])+str(i))
            file_path = os.path.join(output_folder_path, str(uuid.uuid4().hex[:15]) + str(k) + str(i))
            # dlib.save_face_chip(img, shape, file_path2, size=150, padding=568.25)
            dlib.save_face_chip(img, shape, file_path, size=150, padding=0.25)

    print("--- %s seconds ---" % (time.time() - start))

sep_clust()