Update app.py

app.py

@@ -132,49 +132,69 @@
 # if __name__ == "__main__":
 #     demo.launch()
 
+# import gradio as gr
+# import numpy as np
+# import cv2
+# from ultralytics import YOLO
+
+# model = YOLO('Model_IV.pt')
+
+# def transform_cv2(frame, transform):
+#     if transform == "cartoon":
+#         # prepare color
+#         img_color = cv2.pyrDown(cv2.pyrDown(frame))
+#         for _ in range(6):
+#             img_color = cv2.bilateralFilter(img_color, 9, 9, 7)
+#         img_color = cv2.pyrUp(cv2.pyrUp(img_color))
+
+#         # prepare edges
+#         img_edges = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
+#         img_edges = cv2.adaptiveThreshold(
+#             cv2.medianBlur(img_edges, 7),
+#             255,
+#             cv2.ADAPTIVE_THRESH_MEAN_C,
+#             cv2.THRESH_BINARY,
+#             9,
+#             2,
+#         )
+#         img_edges = cv2.cvtColor(img_edges, cv2.COLOR_GRAY2RGB)
+#         # combine color and edges
+#         img = cv2.bitwise_and(img_color, img_edges)
+#         return img
+#     elif transform == "edges":
+#         # perform edge detection
+#         img = cv2.cvtColor(cv2.Canny(frame, 100, 200), cv2.COLOR_GRAY2BGR)
+#         return img
+#     else:
+#         return np.flipud(frame)
+
+# with gr.Blocks() as demo:
+#     with gr.Row():
+#         with gr.Column():
+#             transform = gr.Dropdown(choices=["cartoon", "edges", "flip"],
+#                                     value="flip", label="Transformation")
+#             input_img = gr.Image(sources=["webcam"], type="numpy")
+#         with gr.Column():
+#             output_img = gr.Image(streaming=True)
+#         dep = input_img.stream(transform_cv2, [input_img, transform], [output_img],
+#                                 time_limit=30, stream_every=0.1, concurrency_limit=30)
+
+# if __name__ == "__main__":
+#     demo.launch()
+
+###
+
 import gradio as gr
-import numpy as np
+import torch
 import cv2
 
-def transform_cv2(frame, transform):
-    if transform == "cartoon":
-        # prepare color
-        img_color = cv2.pyrDown(cv2.pyrDown(frame))
-        for _ in range(6):
-            img_color = cv2.bilateralFilter(img_color, 9, 9, 7)
-        img_color = cv2.pyrUp(cv2.pyrUp(img_color))
-
-        # prepare edges
-        img_edges = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
-        img_edges = cv2.adaptiveThreshold(
-            cv2.medianBlur(img_edges, 7),
-            255,
-            cv2.ADAPTIVE_THRESH_MEAN_C,
-            cv2.THRESH_BINARY,
-            9,
-            2,
-        )
-        img_edges = cv2.cvtColor(img_edges, cv2.COLOR_GRAY2RGB)
-        # combine color and edges
-        img = cv2.bitwise_and(img_color, img_edges)
-        return img
-    elif transform == "edges":
-        # perform edge detection
-        img = cv2.cvtColor(cv2.Canny(frame, 100, 200), cv2.COLOR_GRAY2BGR)
-        return img
-    else:
-        return np.flipud(frame)
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        with gr.Column():
-            transform = gr.Dropdown(choices=["cartoon", "edges", "flip"],
-                                    value="flip", label="Transformation")
-            input_img = gr.Image(sources=["webcam"], type="numpy")
-        with gr.Column():
-            output_img = gr.Image(streaming=True)
-        dep = input_img.stream(transform_cv2, [input_img, transform], [output_img],
-                                time_limit=30, stream_every=0.1, concurrency_limit=30)
-
-if __name__ == "__main__":
-    demo.launch()
+# Load the YOLOv8 model
+model = torch.hub.load('ultralytics/yolov8', 'Model_IV')
+
+def inference(img):
+    results = model(img)
+    annotated_img = results.render()[0]
+    return annotated_img
+
+iface = gr.Interface(fn=inference, inputs="webcam", outputs="image")
+iface.launch()