Update app.py

app.py

@@ -1,54 +1,104 @@
-from ultralytics import YOLO
+import gradio as gr
 import cv2
-import cvzone
-import math
-import imutils
-from imutils.video import VideoStream
-
-cap = cv2.VideoCapture(0)#'rtsp://admin:[email protected]:554/Streaming/Channels/1')
-cap.set(3, 640)
-cap.set(4, 128)
-
-
-
-model = YOLO("best20.pt")
-
-classNames = ['person', 'Gun' ]
-
-while True:
-    success, frame = cap.read()
-
-    result = model(frame, stream=True)
+import requests
+import os
 
-    for r in result:
-        boxes = r.boxes
-        for box in boxes:
-            # bounding box
-            x1, y1, x2, y2 = box.xyxy[0]
-            x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
-            w, h = x2 - x1, y2 - y1
-
-            #confidence
-
-            conf = math.ceil((box.conf[0] * 100)) / 100
-
-            #className
-
-            cls = int(box.cls[0])
-            currentClass = classNames[cls]
-
-            if currentClass == "Gun"  and conf > 0.4:
-                print(conf)
-
-                cvzone.cornerRect(frame, (x1, y1, w, h), l=9, rt=5, colorR=(255, 0, 0))
-                cvzone.putTextRect(frame, f'{conf}{currentClass}', (max(0, x1), max(0, y1)))
-                print(conf)
-
-    frame = imutils.resize(frame, width=1200)
-    cv2.imshow('AsimCodeCam', frame)
-    key = cv2.waitKey(1) & 0xFF
-    if key == ord('q'):
-        break
+from ultralytics import YOLO
 
-cv2.destroyAllWindows()
-video_stream.stop()
+file_urls = [
+    'https://www.dropbox.com/s/b5g97xo901zb3ds/pothole_example.jpg?dl=1',
+    'https://www.dropbox.com/s/86uxlxxlm1iaexa/pothole_screenshot.png?dl=1',
+    'https://www.dropbox.com/s/7sjfwncffg8xej2/video_7.mp4?dl=1'
+]
+
+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)
+
+for i, url in enumerate(file_urls):
+    if 'mp4' in file_urls[i]:
+        download_file(
+            file_urls[i],
+            f"video.mp4"
+        )
+    else:
+        download_file(
+            file_urls[i],
+            f"image_{i}.jpg"
+        )
+
+model = YOLO('best20.pt')
+path  = [['image_0.jpg'], ['image_1.jpg']]
+video_path = [['video.mp4']]
+
+def show_preds_image(image_path):
+    image = cv2.imread(image_path)
+    outputs = model.predict(source=image_path)
+    results = outputs[0].cpu().numpy()
+    for i, det in enumerate(results.boxes.xyxy):
+        cv2.rectangle(
+            image,
+            (int(det[0]), int(det[1])),
+            (int(det[2]), int(det[3])),
+            color=(0, 0, 255),
+            thickness=2,
+            lineType=cv2.LINE_AA
+        )
+    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()):
+        ret, frame = cap.read()
+        if ret:
+            frame_copy = frame.copy()
+            outputs = model.predict(source=frame)
+            results = outputs[0].cpu().numpy()
+            for i, det in enumerate(results.boxes.xyxy):
+                cv2.rectangle(
+                    frame_copy,
+                    (int(det[0]), int(det[1])),
+                    (int(det[2]), int(det[3])),
+                    color=(0, 0, 255),
+                    thickness=2,
+                    lineType=cv2.LINE_AA
+                )
+            yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
+
+inputs_video = [
+    gr.components.Video(type="filepath", label="Input Video"),
+
+]
+outputs_video = [
+    gr.components.Image(type="numpy", label="Output Image"),
+]
+interface_video = gr.Interface(
+    fn=show_preds_video,
+    inputs=inputs_video,
+    outputs=outputs_video,
+    title="Pothole detector",
+    examples=video_path,
+    cache_examples=False,
+)
+
+gr.TabbedInterface(
+    [interface_image, interface_video],
+    tab_names=['Image inference', 'Video inference']
+).queue().launch()