update various

README.md

@@ -1,5 +1,5 @@
 # deepstack-ui
-UI for working with [Deepstack](https://python.deepstack.cc/). Allows uploading an image and performing object detection with Deepstack. The effect of various parameters can be explored, including filtering the classes of object detected, filtering by minimum confidence, and spatial filtering using a region of interest (ROI).
+UI for working with [Deepstack](https://python.deepstack.cc/). Allows uploading an image and performing object detection or face recognition with Deepstack. The effect of various parameters can be explored, including filtering the classes of object detected, filtering by minimum confidence, and spatial filtering using a region of interest (ROI).
 
 <p align="center">
 <img src="https://github.com/robmarkcole/deepstack-ui/blob/master/usage.png" width="1000">

app/deepstack-ui.py

@@ -13,7 +13,7 @@
 MIN_CONFIDENCE_THRESHOLD = 0.1
 MAX_CONFIDENCE_THRESHOLD = 1.0
 OBJECT_TEST_IMAGE = "street.jpg"
-FACE_TEST_IMAGE = "idris.jpg"
+FACE_TEST_IMAGE = "faces.jpg"
 FACE = "Face"
 OBJECT = "Object"
 
@@ -45,6 +45,7 @@ def process_image_object(pil_image, dsobject):
     return predictions
 
 
+@st.cache
 def process_image_face(pil_image, dsface):
     image_bytes = utils.pil_image_to_byte_array(pil_image)
     predictions = dsface.recognize(image_bytes)
@@ -54,7 +55,7 @@ def process_image_face(pil_image, dsface):
 deepstack_mode = st.selectbox("Select Deepstack mode:", [OBJECT, FACE])
 
 if deepstack_mode == FACE:
-    st.title("Deepstack Face detection & recogntion")
+    st.title("Deepstack Face recogntion")
 
     img_file_buffer = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg"])
     ## Process image
@@ -64,10 +65,6 @@ def process_image_face(pil_image, dsface):
     else:
         pil_image = Image.open(FACE_TEST_IMAGE)
 
-    st.image(
-        np.array(pil_image), caption=f"Processed image", use_column_width=True,
-    )
-
     dsface = ds.DeepstackFace(
         ip=DEEPSTACK_IP,
         port=DEEPSTACK_PORT,
@@ -76,8 +73,29 @@ def process_image_face(pil_image, dsface):
         min_confidence=MIN_CONFIDENCE_THRESHOLD,
     )
     predictions = process_image_face(pil_image, dsface)
+    faces = utils.get_faces(predictions, pil_image.width, pil_image.height)
+
+    # Draw object boxes
+    draw = ImageDraw.Draw(pil_image)
+    for face in faces:
+        name = face["name"]
+        confidence = face["confidence"]
+        box = face["bounding_box"]
+        box_label = f"{name}"
+
+        utils.draw_box(
+            draw,
+            (box["y_min"], box["x_min"], box["y_max"], box["x_max"]),
+            pil_image.width,
+            pil_image.height,
+            text=box_label,
+            color=const.YELLOW,
+        )
+    st.image(
+        np.array(pil_image), caption=f"Processed image", use_column_width=True,
+    )
     st.subheader("All faces")
-    st.write(utils.get_faces(predictions))
+    st.write(faces)
 
 
 elif deepstack_mode == OBJECT:

app/utils.py

@@ -63,15 +63,26 @@ def get_objects(predictions: list, img_width: int, img_height: int):
     return objects
 
 
-def get_faces(predictions: list):
+def get_faces(predictions: list, img_width: int, img_height: int):
     """Return faces info."""
     faces = []
+    decimal_places = 3
     for pred in predictions:
+        box_width = pred["x_max"] - pred["x_min"]
+        box_height = pred["y_max"] - pred["y_min"]
         name = pred["userid"]
         confidence = pred["confidence"]
+        box = {
+            "height": round(box_height / img_height, decimal_places),
+            "width": round(box_width / img_width, decimal_places),
+            "y_min": round(pred["y_min"] / img_height, decimal_places),
+            "x_min": round(pred["x_min"] / img_width, decimal_places),
+            "y_max": round(pred["y_max"] / img_height, decimal_places),
+            "x_max": round(pred["x_max"] / img_width, decimal_places),
+        }
 
         faces.append(
-            {"name": name, "confidence": confidence,}
+            {"name": name, "confidence": confidence, "bounding_box": box,}
         )
     return faces