update

.gitignore

@@ -3,4 +3,4 @@ flagged/
 *.jpg
 *.mp4
 *.mkv
-gradio_cached_examples/
+# gradio_cached_examples/

app.py

@@ -2,6 +2,7 @@ import gradio as gr
 import cv2
 import requests
 import os
+import numpy as np
 
 from ultralytics import YOLO
 
@@ -12,7 +13,6 @@ file_urls = [
 ]
 
 def download_file(url, save_name):
-    url = url
     if not os.path.exists(save_name):
         file = requests.get(url)
         open(save_name, 'wb').write(file.content)
@@ -30,8 +30,8 @@ for i, url in enumerate(file_urls):
         )
 
 model = YOLO('best.pt')
-path  = [['image_0.jpg'], ['image_1.jpg']]
-video_path = [['video.mp4']]
+path  = [['1.jpeg'], ['2.jpeg']]
+video_path = [['contoh.mp4']]
 
 def show_preds_image(image_path):
     image = cv2.imread(image_path)
@@ -48,21 +48,6 @@ def show_preds_image(image_path):
         )
     return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
 
-inputs_image = [
-    gr.components.Image(type="filepath", label="Input Image"),
-]
-outputs_image = [
-    gr.components.Image(type="numpy", label="Output Image"),
-]
-interface_image = gr.Interface(
-    fn=show_preds_image,
-    inputs=inputs_image,
-    outputs=outputs_image,
-    title="Pothole detector app",
-    examples=path,
-    cache_examples=False,
-)
-
 def show_preds_video(video_path):
     cap = cv2.VideoCapture(video_path)
     while(cap.isOpened()):
@@ -81,14 +66,37 @@ def show_preds_video(video_path):
                     lineType=cv2.LINE_AA
                 )
             yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
+        else:
+            break
+
+def show_preds_webcam(frame):
+    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    outputs = model.predict(source=frame)
+    results = outputs[0].cpu().numpy()
+    for i, det in enumerate(results.boxes.xyxy):
+        cv2.rectangle(
+            frame,
+            (int(det[0]), int(det[1])),
+            (int(det[2]), int(det[3])),
+            color=(0, 0, 255),
+            thickness=2,
+            lineType=cv2.LINE_AA
+        )
+    return frame
 
-inputs_video = [
-    gr.components.Video(type="filepath", label="Input Video"),
+inputs_image = gr.Image(label="Input Image")
+outputs_image = gr.Image(label="Output Image")
+interface_image = gr.Interface(
+    fn=show_preds_image,
+    inputs=inputs_image,
+    outputs=outputs_image,
+    title="Pothole detector",
+    examples=path,
+    cache_examples=False,
+)
 
-]
-outputs_video = [
-    gr.components.Image(type="numpy", label="Output Image"),
-]
+inputs_video = gr.Video(label="Input Video")
+outputs_video = gr.Image(label="Output Image")
 interface_video = gr.Interface(
     fn=show_preds_video,
     inputs=inputs_video,
@@ -98,7 +106,16 @@ interface_video = gr.Interface(
     cache_examples=False,
 )
 
+inputs_webcam = gr.Image(sources="webcam", streaming=True)
+outputs_webcam = gr.Image(label="Output Image")
+interface_webcam = gr.Interface(
+    fn=show_preds_webcam,
+    inputs=inputs_webcam,
+    outputs=outputs_webcam,
+    title="Webcam Object Detection"
+)
+
 gr.TabbedInterface(
-    [interface_image, interface_video],
-    tab_names=['Image inference', 'Video inference']
-).queue().launch()
+    [interface_image, interface_video, interface_webcam],
+    tab_names=['Image Inference', 'Video Inference', 'Webcam Inference']
+).queue().launch()

requirements.txt

@@ -1,6 +1,6 @@
 # Ultralytics requirements
 # Usage: pip install -r requirements.txt
-
+ 
 # Base ----------------------------------------
 hydra-core>=1.2.0
 matplotlib>=3.2.2
@@ -14,16 +14,16 @@ torch>=1.7.0
 torchvision>=0.8.1
 tqdm>=4.64.0
 ultralytics
-
+ 
 # Logging -------------------------------------
 tensorboard>=2.4.1
 # clearml
 # comet
-
+ 
 # Plotting ------------------------------------
 pandas>=1.1.4
 seaborn>=0.11.0
-
+ 
 # Export --------------------------------------
 # coremltools>=6.0  # CoreML export
 # onnx>=1.12.0  # ONNX export
@@ -34,7 +34,7 @@ seaborn>=0.11.0
 # tensorflow>=2.4.1  # TF exports (-cpu, -aarch64, -macos)
 # tensorflowjs>=3.9.0  # TF.js export
 # openvino-dev  # OpenVINO export
-
+ 
 # Extras --------------------------------------
 ipython  # interactive notebook
 psutil  # system utilization
@@ -42,6 +42,6 @@ thop>=0.1.1  # FLOPs computation
 # albumentations>=1.0.3
 # pycocotools>=2.0.6  # COCO mAP
 # roboflow
-
+ 
 # HUB -----------------------------------------
-GitPython>=3.1.24
+GitPython>=3.1.24