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walking enabled in db images

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Şefik Serangil 2020-04-03 23:37:11 +03:00
parent f90887c231
commit 9eaf86b27d
1 changed files with 11 additions and 7 deletions
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18
deepface/commons/realtime.py
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@ -4,6 +4,7 @@ import numpy as np
import pandas as pd import pandas as pd
import cv2 import cv2
import time import time
import re
import os import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
@ -27,7 +28,11 @@ def analysis(db_path, model_name, distance_metric, enable_face_analysis = True):
for r, d, f in os.walk(db_path): # r=root, d=directories, f = files for r, d, f in os.walk(db_path): # r=root, d=directories, f = files
for file in f: for file in f:
if ('.jpg' in file): if ('.jpg' in file):
employees.append(file) #exact_path = os.path.join(r, file)
exact_path = r + "/" + file
#print(exact_path)
employees.append(exact_path)
#------------------------ #------------------------
@ -91,9 +96,9 @@ def analysis(db_path, model_name, distance_metric, enable_face_analysis = True):
#for employee in employees: #for employee in employees:
for index in pbar: for index in pbar:
employee = employees[index] employee = employees[index]
pbar.set_description("Finding embedding for %s" % (employee)) pbar.set_description("Finding embedding for %s" % (employee.split("/")[-1]))
embedding = [] embedding = []
img = functions.detectFace(db_path+"/"+employee, input_shape) img = functions.detectFace(employee, input_shape)
img_representation = model.predict(img)[0,:] img_representation = model.predict(img)[0,:]
embedding.append(employee) embedding.append(employee)
@ -365,16 +370,15 @@ def analysis(db_path, model_name, distance_metric, enable_face_analysis = True):
candidate = df.iloc[0] candidate = df.iloc[0]
employee_name = candidate['employee'] employee_name = candidate['employee']
best_distance = candidate['distance'] best_distance = candidate['distance']
#employee_name = employee_name.replace("_", "")
if best_distance <= threshold: if best_distance <= threshold:
#print(employee_name) #print(employee_name)
display_img = cv2.imread("%s/%s" % (db_path, employee_name)) display_img = cv2.imread(employee_name)
display_img = cv2.resize(display_img, (pivot_img_size, pivot_img_size)) display_img = cv2.resize(display_img, (pivot_img_size, pivot_img_size))
label = employee_name.replace("_", "").replace(".jpg", "")+" ("+"{0:.2f}".format(best_distance)+")" label = employee_name.split("/")[-1].replace(".jpg", "")
#label = employee_name.replace("_", "").replace(".jpg", "") label = re.sub('[0-9]', '', label)
try: try:
if y - pivot_img_size > 0 and x + w + pivot_img_size < resolution_x: if y - pivot_img_size > 0 and x + w + pivot_img_size < resolution_x: