Update app.py

app.py

@@ -1,10 +1,21 @@
 import gradio as gr
-import torch
+#import torch
 import yolov7
+import subprocess
+import tempfile
+import time
+from pathlib import Path
+import uuid
+import cv2
+import gradio as gr
 
 
 
-def yolov7_inference(
+# Images
+#torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg')
+#torch.hub.download_url_to_file('https://raw.githubusercontent.com/obss/sahi/main/tests/data/small-vehicles1.jpeg', 'small-vehicles1.jpeg')
+    
+def image_fn(
     image: gr.inputs.Image = None,
     model_path: gr.inputs.Dropdown = None,
     image_size: gr.inputs.Slider = 640,
@@ -28,30 +39,105 @@ def yolov7_inference(
     model.iou = iou_threshold
     results = model([image], size=image_size)
     return results.render()[0]
+  
+  
         
+def video_fn(model_path, video_file, conf_thres, iou_thres, start_sec, duration):
+    model = yolov7.load(model_path, device="cpu", hf_model=True, trace=False)
+    start_timestamp = time.strftime("%H:%M:%S", time.gmtime(start_sec))
+    end_timestamp = time.strftime("%H:%M:%S", time.gmtime(start_sec + duration))
+
+    suffix = Path(video_file).suffix
+
+    clip_temp_file = tempfile.NamedTemporaryFile(suffix=suffix)
+    subprocess.call(
+        f"ffmpeg -y -ss {start_timestamp} -i {video_file} -to {end_timestamp} -c copy {clip_temp_file.name}".split()
+    )
+
+    # Reader of clip file
+    cap = cv2.VideoCapture(clip_temp_file.name)
+
+    # This is an intermediary temp file where we'll write the video to
+    # Unfortunately, gradio doesn't play too nice with videos rn so we have to do some hackiness
+    # with ffmpeg at the end of the function here.
+    with tempfile.NamedTemporaryFile(suffix=".mp4") as temp_file:
+        out = cv2.VideoWriter(temp_file.name, cv2.VideoWriter_fourcc(*"MP4V"), 30, (1280, 720))
 
-inputs = [
+        num_frames = 0
+        max_frames = duration * 30
+        while cap.isOpened():
+            try:
+                ret, frame = cap.read()
+                if not ret:
+                    break
+            except Exception as e:
+                print(e)
+                continue
+            print("FRAME DTYPE", type(frame))
+            out.write(model([frame], conf_thres, iou_thres))
+            num_frames += 1
+            print("Processed {} frames".format(num_frames))
+            if num_frames == max_frames:
+                break
+
+        out.release()
+
+        # Aforementioned hackiness
+        out_file = tempfile.NamedTemporaryFile(suffix="out.mp4", delete=False)
+        subprocess.run(f"ffmpeg -y -loglevel quiet -stats -i {temp_file.name} -c:v libx264 {out_file.name}".split())
+
+    return out_file.name
+
+image_interface = gr.Interface(
+    fn=image_fn,
+    inputs=[
     gr.inputs.Image(type="pil", label="Input Image"),
     gr.inputs.Dropdown(
         choices=[
             "StarAtNyte1/yolov7_custom",
+            #"kadirnar/yolov7-v0.1",
+        ],
+        default="StarAtNyte1/yolov7_custom",
+        label="Model",
+    )
+    #gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size")
+    #gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
+    #gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold")
+],
+    outputs=gr.outputs.Image(type="filepath", label="Output Image"),
+    title="Smart Environmental Eye (SEE)",
+    examples=[['image1.jpg', 'alshimaa/SEE_model_yolo7', 640, 0.25, 0.45], ['image2.jpg', 'alshimaa/SEE_model_yolo7', 640, 0.25, 0.45], ['image3.jpg', 'alshimaa/SEE_model_yolo7', 640, 0.25, 0.45]],
+    cache_examples=True,
+    theme='huggingface',
+)
+
+
+video_interface = gr.Interface(
+    fn=video_fn,
+    inputs=[
+        gr.inputs.Video(source = "upload", type = "mp4", label = "Input Video"),
+        gr.inputs.Dropdown(
+        choices=[
+            "StarAtNyte1/yolov7_custom",
+            #"kadirnar/yolov7-v0.1",
         ],
         default="StarAtNyte1/yolov7_custom",
         label="Model",
     ),
-    gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
-    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
-    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
-]
-
-outputs = gr.outputs.Image(type="filepath", label="Output Image")
-#title = "Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors"
-
-demo_app = gr.Interface(
-    fn=yolov7_inference,
-    inputs=inputs,
-    outputs=outputs,
+    ],
+    outputs=gr.outputs.Video(type = "mp4", label = "Output Video"),
+    # examples=[
+    #     ["video.mp4", 0.25, 0.45, 0, 2],
+       
+    # ],
+    title="Smart Environmental Eye (SEE)",
     cache_examples=True,
     theme='huggingface',
+   
 )
-demo_app.launch(debug=True, enable_queue=True)
+
+if __name__ == "__main__":
+    gr.TabbedInterface(
+        [image_interface, video_interface],
+        ["Run on Images", "Run on Videos"],
+    ).launch()