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alignment fix for emotion

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This commit is contained in:
Şefik Serangil 2020-02-25 22:08:18 +03:00
parent ec3f64dbd8
commit 905e1b082b
2 changed files with 14 additions and 10 deletions
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15
deepface/DeepFace.py
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@ -9,13 +9,13 @@ import pandas as pd
from tqdm import tqdm
import json
#from basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
#from extendedmodels import Age, Gender, Race, Emotion
#from commons import functions, distance as dst
from basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
from extendedmodels import Age, Gender, Race, Emotion
from commons import functions, distance as dst
from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
from deepface.extendedmodels import Age, Gender, Race, Emotion
from deepface.commons import functions, distance as dst
#from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace
#from deepface.extendedmodels import Age, Gender, Race, Emotion
#from deepface.commons import functions, distance as dst
def verify(img1_path, img2_path
, model_name ='VGG-Face', distance_metric = 'cosine', plot = False):
@ -129,6 +129,9 @@ def verify(img1_path, img2_path
def analyze(img_path, actions= []):
if os.path.isfile(img_path) != True:
raise ValueError("Confirm that ",img_path," exists")
resp_obj = "{"
#if a specific target is not passed, then find them all

7
deepface/commons/functions.py
View File

@ -128,10 +128,7 @@ def detectFace(image_path, target_size=(224, 224), grayscale = False):
face_detector = cv2.CascadeClassifier(face_detector_path)
eye_detector = cv2.CascadeClassifier(eye_detector_path)
if grayscale != True:
img = cv2.imread(image_path)
else: #gray scale
img = cv2.imread(image_path, 0)
img_raw = img.copy()
@ -229,6 +226,10 @@ def detectFace(image_path, target_size=(224, 224), grayscale = False):
#face alignment block end
#---------------------------
#face alignment block needs colorful images. that's why, converting to gray scale logic moved to here.
if grayscale == True:
detected_face = cv2.cvtColor(detected_face, cv2.COLOR_BGR2GRAY)
detected_face = cv2.resize(detected_face, target_size)
img_pixels = image.img_to_array(detected_face)