Added Uploading

app.py

@@ -2,6 +2,8 @@ import os
 import faiss
 import gradio as gr
 from helpers import *
+import shutil
+from PIL import Image
 
 detector = load_detector()
 model = load_model()
@@ -20,8 +22,9 @@ for r, _, f in os.walk(os.getcwd() + "/images"):
 source_faces = []
 for img in source_imgs:
   try:
-    faces, id = extract_faces(detector, img)
-    source_faces.append(faces[id])
+    # faces, id = extract_faces(detector, img)
+    # source_faces.append(faces[id])
+    source_faces.append(Image.open(img))
   except Exception as e:
     print(f"Skipping {img}, {e}")
 
@@ -45,7 +48,7 @@ def find_names(image):
   recognition(imgs, ids, names, source_faces, d, source_imgs)
   return ",".join(names), "Recognition.jpg"
 
-demo = gr.Interface(
+detect = gr.Interface(
     find_names,
     gr.Image(type="filepath"),
     ["text", gr.Image(type = "filepath")],
@@ -55,5 +58,68 @@ demo = gr.Interface(
     ]
 )
 
+
+def upload_files(files):
+    if not os.path.exists(os.path.join(os.getcwd(), "temp")):
+        os.mkdir(os.path.join(os.getcwd(), "temp"))
+    for file in files:
+        shutil.move(file.name, os.path.join(os.getcwd(), "temp", file.name.split('\\')[-1]))
+    return None, "Uploaded!"
+
+with gr.Blocks() as upload:
+    gr.Markdown("# Select Images to Upload and click Upload")
+    with gr.Row():
+        input = gr.Files(file_types=[".jpg", ".jpeg", ".png"], label="Upload images")
+        # input = gr.Image(type="filepath")
+        output = gr.Textbox()
+    upload_btn = gr.Button(label="Upload")
+    upload_btn.click(upload_files, inputs=[input], outputs=[input, output])
+
+def load_image():
+    global i, imgs
+    images = os.listdir(os.path.join(os.getcwd(), "temp"))
+    imgs = []
+    for image in images:
+        faces, id = extract_faces(detector, os.path.join(os.getcwd(), "temp", image))
+        # imgs.append(Image.open(os.path.join(os.getcwd(), "temp", image)))
+        imgs.append(faces[id])
+    return imgs[0], "Loaded!"
+
+def save_img(label):
+    global i, imgs
+    imgs[i].save(os.path.join(os.getcwd(), "images", f"{label}.jpg"))
+    i+=1
+    if i < len(imgs):
+        return imgs[i], "Saved!"
+    else:
+        clear()
+        return None, "Finished!"
+
+def clear():
+    global i, imgs
+    i = 0
+    imgs = None
+    shutil.rmtree(os.path.join(os.getcwd(), "temp"))
+    return None, None
+
+i = 0
+imgs = None
+
+with gr.Blocks() as annotate:
+    output = gr.Image(type="pil", label="Image")
+    input = gr.Textbox(label = "Enter Label")
+    with gr.Row():
+        next_btn = gr.Button(label="Next")
+        next_btn.click(load_image, inputs=[], outputs=[output, input])
+        save_btn = gr.Button(label="Save")
+        save_btn.click(save_img, inputs=[input], outputs=[output, input])
+        clear_btn = gr.Button(label="Clear")
+        clear_btn.click(clear, inputs=[], outputs=[output, input])
+
+tabbed_interface = gr.TabbedInterface(
+    [upload, annotate, detect],
+    ["Upload", "Annotate", "Attendance"],
+)
 if __name__ == "__main__":
-    demo.launch()
+    tabbed_interface.launch()
+

app_old.py

@@ -0,0 +1,59 @@
+import os
+import faiss
+import gradio as gr
+from helpers import *
+
+detector = load_detector()
+model = load_model()
+
+source_imgs = []
+for r, _, f in os.walk(os.getcwd() + "/images"):
+    for file in f:
+        if (
+            (".jpg" in file.lower())
+            or (".jpeg" in file.lower())
+            or (".png" in file.lower())
+        ):
+            exact_path = r + "/" + file
+            source_imgs.append(exact_path)
+
+source_faces = []
+for img in source_imgs:
+  try:
+    faces, id = extract_faces(detector, img)
+    source_faces.append(faces[id])
+  except Exception as e:
+    print(f"Skipping {img}, {e}")
+
+source_embeddings = get_embeddings(model, source_faces)
+
+def find_names(image):
+  imgs, _ = extract_faces(detector, image)
+  for i, face in enumerate(imgs):
+    if(face.size[0] * face.size[1] < 1000):
+      del imgs[i]
+
+  embeds = get_embeddings(model, imgs)
+  d = np.zeros((len(source_embeddings), len(embeds)))
+  for i, s in enumerate(source_embeddings):
+    for j, t in enumerate(embeds):
+      d[i][j] = findCosineDistance(s, t)
+  ids = np.argmin(d, axis = 0)
+  names = []
+  for i in ids:
+    names.append(source_imgs[i].split("/")[-1].split(".")[0])
+  recognition(imgs, ids, names, source_faces, d, source_imgs)
+  return ",".join(names), "Recognition.jpg"
+
+demo = gr.Interface(
+    find_names,
+    gr.Image(type="filepath"),
+    ["text", gr.Image(type = "filepath")],
+    examples = [
+        os.path.join(os.path.dirname(__file__), "examples/group1.jpg"),
+        os.path.join(os.path.dirname(__file__), "examples/group2.jpg")
+    ]
+)
+
+if __name__ == "__main__":
+    demo.launch()