computer vision 1

app.py

@@ -50,42 +50,42 @@ interface_image = gr.Interface(
     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)
+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"),
+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,
-# )
+]
+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()
+gr.TabbedInterface(
+    [interface_image, interface_video],
+    tab_names=['Image inference', 'Video inference']
+).queue().launch()

requirements.txt

@@ -1,47 +1,55 @@
-# Ultralytics requirements
-# Usage: pip install -r requirements.txt
-
-# Base ----------------------------------------
-hydra-core>=1.2.0
-matplotlib>=3.2.2
-numpy>=1.18.5
-opencv-python>=4.1.1
-Pillow>=7.1.2
-PyYAML>=5.3.1
-requests>=2.23.0
-scipy>=1.4.1
-torch>=1.7.0
-torchvision>=0.8.1
-tqdm>=4.64.0
+opencv-python
+matplotlib
+requests
+hydra-core
 ultralytics
+gradio
 
-# 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
-# onnx-simplifier>=0.4.1  # ONNX simplifier
-# nvidia-pyindex  # TensorRT export
-# nvidia-tensorrt  # TensorRT export
-# scikit-learn==0.19.2  # CoreML quantization
-# 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
-thop>=0.1.1  # FLOPs computation
-# albumentations>=1.0.3
-# pycocotools>=2.0.6  # COCO mAP
-# roboflow
-
-# HUB -----------------------------------------
-GitPython>=3.1.24
+
+# # Ultralytics requirements
+# # Usage: pip install -r requirements.txt
+
+# # Base ----------------------------------------
+# hydra-core>=1.2.0
+# matplotlib>=3.2.2
+# numpy>=1.18.5
+# opencv-python>=4.1.1
+# Pillow>=7.1.2
+# PyYAML>=5.3.1
+# requests>=2.23.0
+# scipy>=1.4.1
+# 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
+# # onnx-simplifier>=0.4.1  # ONNX simplifier
+# # nvidia-pyindex  # TensorRT export
+# # nvidia-tensorrt  # TensorRT export
+# # scikit-learn==0.19.2  # CoreML quantization
+# # 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
+# thop>=0.1.1  # FLOPs computation
+# # albumentations>=1.0.3
+# # pycocotools>=2.0.6  # COCO mAP
+# # roboflow
+
+# # HUB -----------------------------------------
+# GitPython>=3.1.24