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emotion for stream

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Şefik Serangil 2020-03-31 20:17:25 +03:00
parent 1af71925a2
commit e38c251051
1 changed files with 73 additions and 8 deletions
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81
deepface/commons/realtime.py
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@ -66,8 +66,8 @@ def analysis(db_path, model_name, distance_metric, enable_face_analysis = True):
tic = time.time() tic = time.time()
#emotion_model = Emotion.loadModel() emotion_model = Emotion.loadModel()
#print("Emotion model loaded") print("Emotion model loaded")
age_model = Age.loadModel() age_model = Age.loadModel()
print("Age model loaded") print("Age model loaded")
@ -188,7 +188,7 @@ def analysis(db_path, model_name, distance_metric, enable_face_analysis = True):
for detected_face in detected_faces_final: for detected_face in detected_faces_final:
x = detected_face[0]; y = detected_face[1] x = detected_face[0]; y = detected_face[1]
w = detected_face[2]; h = detected_face[3] w = detected_face[2]; h = detected_face[3]
cv2.rectangle(freeze_img, (x,y), (x+w,y+h), (67,67,67), 1) #draw rectangle to main image cv2.rectangle(freeze_img, (x,y), (x+w,y+h), (67,67,67), 1) #draw rectangle to main image
#------------------------------- #-------------------------------
@ -202,7 +202,6 @@ def analysis(db_path, model_name, distance_metric, enable_face_analysis = True):
if enable_face_analysis == True: if enable_face_analysis == True:
"""
gray_img = functions.detectFace(custom_face, (48, 48), True) gray_img = functions.detectFace(custom_face, (48, 48), True)
emotion_labels = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral'] emotion_labels = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']
emotion_predictions = emotion_model.predict(gray_img)[0,:] emotion_predictions = emotion_model.predict(gray_img)[0,:]
@ -218,10 +217,56 @@ def analysis(db_path, model_name, distance_metric, enable_face_analysis = True):
mood_items.append(mood_item) mood_items.append(mood_item)
emotion_df = pd.DataFrame(mood_items, columns = ["emotion", "score"]) emotion_df = pd.DataFrame(mood_items, columns = ["emotion", "score"])
emotion_df = emotion_df.sort_values(by = ["score"], ascending=False) emotion_df = emotion_df.sort_values(by = ["score"], ascending=False).reset_index(drop=True)
dominant_emotion = emotion_df.iloc[0].emotion
emotion_score = emotion_df.iloc[0].score #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+pivot_img_size, y+180)
, (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, y+180)
, (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:
cv2.putText(freeze_img, emotion_label, (x+w, y + 20 + (index+1) * 20), 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:
cv2.putText(freeze_img, emotion_label, (x-pivot_img_size, y + 20 + (index+1) * 20), 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)
#------------------------------- #-------------------------------
@ -323,6 +368,11 @@ def analysis(db_path, model_name, distance_metric, enable_face_analysis = True):
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:
#top right #top right
freeze_img[y - pivot_img_size:y, x+w:x+w+pivot_img_size] = display_img freeze_img[y - pivot_img_size:y, x+w:x+w+pivot_img_size] = display_img
overlay = freeze_img.copy(); opacity = 0.4
cv2.rectangle(freeze_img,(x+w,y),(x+w+pivot_img_size, y+20),(46,200,255),cv2.FILLED)
cv2.addWeighted(overlay, opacity, freeze_img, 1 - opacity, 0, freeze_img)
cv2.putText(freeze_img, label, (x+w, y+10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, text_color, 1) cv2.putText(freeze_img, label, (x+w, y+10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, text_color, 1)
#connect face and text #connect face and text
@ -332,6 +382,11 @@ def analysis(db_path, model_name, distance_metric, enable_face_analysis = True):
elif y + h + pivot_img_size < resolution_y and x - pivot_img_size > 0: elif y + h + pivot_img_size < resolution_y and x - pivot_img_size > 0:
#bottom left #bottom left
freeze_img[y+h:y+h+pivot_img_size, x-pivot_img_size:x] = display_img freeze_img[y+h:y+h+pivot_img_size, x-pivot_img_size:x] = display_img
overlay = freeze_img.copy(); opacity = 0.4
cv2.rectangle(freeze_img,(x-pivot_img_size,y+h-20),(x, y+h),(46,200,255),cv2.FILLED)
cv2.addWeighted(overlay, opacity, freeze_img, 1 - opacity, 0, freeze_img)
cv2.putText(freeze_img, label, (x - pivot_img_size, y+h-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, text_color, 1) cv2.putText(freeze_img, label, (x - pivot_img_size, y+h-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, text_color, 1)
#connect face and text #connect face and text
@ -341,6 +396,11 @@ def analysis(db_path, model_name, distance_metric, enable_face_analysis = True):
elif y - pivot_img_size > 0 and x - pivot_img_size > 0: elif y - pivot_img_size > 0 and x - pivot_img_size > 0:
#top left #top left
freeze_img[y-pivot_img_size:y, x-pivot_img_size:x] = display_img freeze_img[y-pivot_img_size:y, x-pivot_img_size:x] = display_img
overlay = freeze_img.copy(); opacity = 0.4
cv2.rectangle(freeze_img,(x- pivot_img_size,y),(x, y+20),(46,200,255),cv2.FILLED)
cv2.addWeighted(overlay, opacity, freeze_img, 1 - opacity, 0, freeze_img)
cv2.putText(freeze_img, label, (x - pivot_img_size, y+10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, text_color, 1) cv2.putText(freeze_img, label, (x - pivot_img_size, y+10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, text_color, 1)
#connect face and text #connect face and text
@ -350,6 +410,11 @@ def analysis(db_path, model_name, distance_metric, enable_face_analysis = True):
elif x+w+pivot_img_size < resolution_x and y + h + pivot_img_size < resolution_y: elif x+w+pivot_img_size < resolution_x and y + h + pivot_img_size < resolution_y:
#bottom righ #bottom righ
freeze_img[y+h:y+h+pivot_img_size, x+w:x+w+pivot_img_size] = display_img freeze_img[y+h:y+h+pivot_img_size, x+w:x+w+pivot_img_size] = display_img
overlay = freeze_img.copy(); opacity = 0.4
cv2.rectangle(freeze_img,(x+w,y+h-20),(x+w+pivot_img_size, y+h),(46,200,255),cv2.FILLED)
cv2.addWeighted(overlay, opacity, freeze_img, 1 - opacity, 0, freeze_img)
cv2.putText(freeze_img, label, (x+w, y+h-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, text_color, 1) cv2.putText(freeze_img, label, (x+w, y+h-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, text_color, 1)
#connect face and text #connect face and text