mirror of
https://github.com/serengil/deepface.git
synced 2025-07-29 14:21:39 +00:00
add
This commit is contained in:
Xu-jianwen
parent
46590a0550
commit
3234414aff
@ -14,7 +14,7 @@ from deepface.extendedmodels import Age
|
||||
from deepface.commons import functions, realtime, distance as dst
|
||||
from deepface.detectors import FaceDetector
|
||||
|
||||
def analysis(db_path, model_name = 'VGG-Face', detector_backend = 'opencv', distance_metric = 'cosine', enable_face_analysis = True, source = 0, time_threshold = 5, frame_threshold = 5):
|
||||
def analysis(db_path, model_name = 'VGG-Face', detector_backend = 'opencv', distance_metric = 'cosine', source = 0, time_threshold = 5, frame_threshold = 5):
|
||||
|
||||
#------------------------
|
||||
|
||||
@ -56,26 +56,6 @@ def analysis(db_path, model_name = 'VGG-Face', detector_backend = 'opencv', dist
|
||||
#tuned thresholds for model and metric pair
|
||||
threshold = dst.findThreshold(model_name, distance_metric)
|
||||
|
||||
#------------------------
|
||||
#facial attribute analysis models
|
||||
|
||||
if enable_face_analysis == True:
|
||||
|
||||
tic = time.time()
|
||||
|
||||
emotion_model = DeepFace.build_model('Emotion')
|
||||
print("Emotion model loaded")
|
||||
|
||||
age_model = DeepFace.build_model('Age')
|
||||
print("Age model loaded")
|
||||
|
||||
gender_model = DeepFace.build_model('Gender')
|
||||
print("Gender model loaded")
|
||||
|
||||
toc = time.time()
|
||||
|
||||
print("Facial attibute analysis models loaded in ",toc-tic," seconds")
|
||||
|
||||
#------------------------
|
||||
|
||||
#find embeddings for employee list
|
||||
@ -197,141 +177,6 @@ def analysis(db_path, model_name = 'VGG-Face', detector_backend = 'opencv', dist
|
||||
|
||||
custom_face = base_img[y:y+h, x:x+w]
|
||||
|
||||
#-------------------------------
|
||||
#facial attribute analysis
|
||||
|
||||
if enable_face_analysis == True:
|
||||
|
||||
gray_img = functions.preprocess_face(img = custom_face, target_size = (48, 48), grayscale = True, enforce_detection = False, detector_backend = 'opencv')
|
||||
emotion_labels = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']
|
||||
emotion_predictions = emotion_model.predict(gray_img)[0,:]
|
||||
sum_of_predictions = emotion_predictions.sum()
|
||||
|
||||
mood_items = []
|
||||
for i in range(0, len(emotion_labels)):
|
||||
mood_item = []
|
||||
emotion_label = emotion_labels[i]
|
||||
emotion_prediction = 100 * emotion_predictions[i] / sum_of_predictions
|
||||
mood_item.append(emotion_label)
|
||||
mood_item.append(emotion_prediction)
|
||||
mood_items.append(mood_item)
|
||||
|
||||
emotion_df = pd.DataFrame(mood_items, columns = ["emotion", "score"])
|
||||
emotion_df = emotion_df.sort_values(by = ["score"], ascending=False).reset_index(drop=True)
|
||||
|
||||
#background of mood box
|
||||
|
||||
#transparency
|
||||
overlay = freeze_img.copy()
|
||||
opacity = 0.4
|
||||
|
||||
if x+w+pivot_img_size < resolution_x:
|
||||
#right
|
||||
cv2.rectangle(freeze_img
|
||||
#, (x+w,y+20)
|
||||
, (x+w,y)
|
||||
, (x+w+pivot_img_size, y+h)
|
||||
, (64,64,64),cv2.FILLED)
|
||||
|
||||
cv2.addWeighted(overlay, opacity, freeze_img, 1 - opacity, 0, freeze_img)
|
||||
|
||||
elif x-pivot_img_size > 0:
|
||||
#left
|
||||
cv2.rectangle(freeze_img
|
||||
#, (x-pivot_img_size,y+20)
|
||||
, (x-pivot_img_size,y)
|
||||
, (x, y+h)
|
||||
, (64,64,64),cv2.FILLED)
|
||||
|
||||
cv2.addWeighted(overlay, opacity, freeze_img, 1 - opacity, 0, freeze_img)
|
||||
|
||||
for index, instance in emotion_df.iterrows():
|
||||
emotion_label = "%s " % (instance['emotion'])
|
||||
emotion_score = instance['score']/100
|
||||
|
||||
bar_x = 35 #this is the size if an emotion is 100%
|
||||
bar_x = int(bar_x * emotion_score)
|
||||
|
||||
if x+w+pivot_img_size < resolution_x:
|
||||
|
||||
text_location_y = y + 20 + (index+1) * 20
|
||||
text_location_x = x+w
|
||||
|
||||
if text_location_y < y + h:
|
||||
cv2.putText(freeze_img, emotion_label, (text_location_x, text_location_y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
|
||||
|
||||
cv2.rectangle(freeze_img
|
||||
, (x+w+70, y + 13 + (index+1) * 20)
|
||||
, (x+w+70+bar_x, y + 13 + (index+1) * 20 + 5)
|
||||
, (255,255,255), cv2.FILLED)
|
||||
|
||||
elif x-pivot_img_size > 0:
|
||||
|
||||
text_location_y = y + 20 + (index+1) * 20
|
||||
text_location_x = x-pivot_img_size
|
||||
|
||||
if text_location_y <= y+h:
|
||||
cv2.putText(freeze_img, emotion_label, (text_location_x, text_location_y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
|
||||
|
||||
cv2.rectangle(freeze_img
|
||||
, (x-pivot_img_size+70, y + 13 + (index+1) * 20)
|
||||
, (x-pivot_img_size+70+bar_x, y + 13 + (index+1) * 20 + 5)
|
||||
, (255,255,255), cv2.FILLED)
|
||||
|
||||
#-------------------------------
|
||||
|
||||
face_224 = functions.preprocess_face(img = custom_face, target_size = (224, 224), grayscale = False, enforce_detection = False, detector_backend = 'opencv')
|
||||
|
||||
age_predictions = age_model.predict(face_224)[0,:]
|
||||
apparent_age = Age.findApparentAge(age_predictions)
|
||||
|
||||
#-------------------------------
|
||||
|
||||
gender_prediction = gender_model.predict(face_224)[0,:]
|
||||
|
||||
if np.argmax(gender_prediction) == 0:
|
||||
gender = "W"
|
||||
elif np.argmax(gender_prediction) == 1:
|
||||
gender = "M"
|
||||
|
||||
#print(str(int(apparent_age))," years old ", dominant_emotion, " ", gender)
|
||||
|
||||
analysis_report = str(int(apparent_age))+" "+gender
|
||||
|
||||
#-------------------------------
|
||||
|
||||
info_box_color = (46,200,255)
|
||||
|
||||
#top
|
||||
if y - pivot_img_size + int(pivot_img_size/5) > 0:
|
||||
|
||||
triangle_coordinates = np.array( [
|
||||
(x+int(w/2), y)
|
||||
, (x+int(w/2)-int(w/10), y-int(pivot_img_size/3))
|
||||
, (x+int(w/2)+int(w/10), y-int(pivot_img_size/3))
|
||||
] )
|
||||
|
||||
cv2.drawContours(freeze_img, [triangle_coordinates], 0, info_box_color, -1)
|
||||
|
||||
cv2.rectangle(freeze_img, (x+int(w/5), y-pivot_img_size+int(pivot_img_size/5)), (x+w-int(w/5), y-int(pivot_img_size/3)), info_box_color, cv2.FILLED)
|
||||
|
||||
cv2.putText(freeze_img, analysis_report, (x+int(w/3.5), y - int(pivot_img_size/2.1)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 111, 255), 2)
|
||||
|
||||
#bottom
|
||||
elif y + h + pivot_img_size - int(pivot_img_size/5) < resolution_y:
|
||||
|
||||
triangle_coordinates = np.array( [
|
||||
(x+int(w/2), y+h)
|
||||
, (x+int(w/2)-int(w/10), y+h+int(pivot_img_size/3))
|
||||
, (x+int(w/2)+int(w/10), y+h+int(pivot_img_size/3))
|
||||
] )
|
||||
|
||||
cv2.drawContours(freeze_img, [triangle_coordinates], 0, info_box_color, -1)
|
||||
|
||||
cv2.rectangle(freeze_img, (x+int(w/5), y + h + int(pivot_img_size/3)), (x+w-int(w/5), y+h+pivot_img_size-int(pivot_img_size/5)), info_box_color, cv2.FILLED)
|
||||
|
||||
cv2.putText(freeze_img, analysis_report, (x+int(w/3.5), y + h + int(pivot_img_size/1.5)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 111, 255), 2)
|
||||
|
||||
#-------------------------------
|
||||
#face recognition
|
||||
|
||||
@ -463,3 +308,7 @@ def analysis(db_path, model_name = 'VGG-Face', detector_backend = 'opencv', dist
|
||||
#kill open cv things
|
||||
cap.release()
|
||||
cv2.destroyAllWindows()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
analysis("D:/face/320_database/", detector_backend = 'ssd')
|
||||
Loading…
x
Reference in New Issue
Block a user