streaming code refactored

tests/overlay.py

@@ -0,0 +1,55 @@
+import cv2
+import matplotlib.pyplot as plt
+from deepface.modules import streaming
+from deepface import DeepFace
+
+img_path = "dataset/img1.jpg"
+img = cv2.imread(img_path)
+
+overlay_img_path = "dataset/img6.jpg"
+face_objs = DeepFace.extract_faces(overlay_img_path, target_size=(112, 112))
+overlay_img = face_objs[0]["face"][:, :, ::-1] * 255
+
+raw_img = img.copy()
+
+demographies = DeepFace.analyze(img_path=img_path, actions=("age", "gender", "emotion"))
+demography = demographies[0]
+
+x = demography["region"]["x"]
+y = demography["region"]["y"]
+w = demography["region"]["w"]
+h = demography["region"]["h"]
+
+img = streaming.highlight_facial_areas(img=img, faces_coordinates=[(x, y, w, h)])
+
+img = streaming.overlay_emotion(
+    img=img,
+    emotion_probas=demography["emotion"],
+    x=x,
+    y=y,
+    w=w,
+    h=h,
+)
+
+img = streaming.overlay_age_gender(
+    img=img,
+    apparent_age=demography["age"],
+    gender=demography["dominant_gender"][0:1],
+    x=x,
+    y=y,
+    w=w,
+    h=h,
+)
+
+img = streaming.overlay_identified_face(
+    img=img,
+    target_img=overlay_img,
+    label="angelina",
+    x=x,
+    y=y,
+    w=w,
+    h=h,
+)
+
+plt.imshow(img[:, :, ::-1])
+plt.show()