stock photos for readme

README.md

@@ -17,7 +17,7 @@ result = DeepFace.verify("img1.jpg", "img2.jpg")
 print("Is verified: ", result["verified"])
 ```
 
-<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/tests/dataset/test-case-6.jpg" width="95%" height="95%"></p>
+<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/stock-1.jpg" width="95%" height="95%"></p>
 
 Modern face recognition pipelines consist of 4 stages: detect, [align](https://sefiks.com/2020/02/23/face-alignment-for-face-recognition-in-python-within-opencv/), represent and verify. Deepface handles all these common stages in the background.
 
@@ -71,9 +71,9 @@ print("Emotion: ", demography["dominant_emotion"])
 print("Race: ", demography["dominant_race"])
 ```
 
-<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/tests/dataset/test-case-3.jpg" width="95%" height="95%"></p>
+<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/stock-2.jpg" width="95%" height="95%"></p>
 
-# Stream
+# Streaming and Real Time Analysis
 
 You can run deepface for real time videos as well. Calling stream function under the DeepFace interface will access your webcam and apply both face recognition and facial attribute analysis. Stream function expects a database folder including face images. VGG-Face is the default face recognition model and cosine similarity is the default distance metric similar to verify function. The function starts to analyze if it can focus a face sequantially 5 frames. Then, it shows results 5 seconds.
 
@@ -82,7 +82,7 @@ from deepface import DeepFace
 DeepFace.stream("/user/database")
 ```
 
-<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/deepface-stream-v4.jpg" width="90%" height="90%"></p>
+<p align="center"><img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/stock-3.jpg" width="90%" height="90%"></p>
 
 # Installation