yonigozlan HF staff commited on
Commit
8418755
•
1 Parent(s): 7a4aa57

remove set image size

Browse files
Files changed (1) hide show
  1. app.py +10 -22
app.py CHANGED
@@ -44,7 +44,6 @@ def process_video(
44
  input_video,
45
  confidence_threshold,
46
  classes,
47
- max_side,
48
  progress=gr.Progress(track_tqdm=True),
49
  ):
50
  classes = classes.strip(" ").split(",")
@@ -61,9 +60,7 @@ def process_video(
61
  frame = next(frame_generator)
62
  except StopIteration:
63
  break
64
- results, fps = query(
65
- frame, classes, confidence_threshold, max_side=max_side
66
- )
67
  all_fps.append(fps)
68
  detections = []
69
 
@@ -92,11 +89,8 @@ def process_video(
92
  )
93
 
94
 
95
- def query(frame, classes, confidence_threshold, max_side=360):
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- frame_resized = sv.resize_image(
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- image=frame, resolution_wh=(max_side, max_side), keep_aspect_ratio=True
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- )
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- image = Image.fromarray(frame_resized)
100
  inputs = processor(images=image, text=classes, return_tensors="pt").to(device)
101
  with torch.no_grad():
102
  start = time.time()
@@ -124,7 +118,7 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
124
  """
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  )
126
  gr.Markdown(
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- "Simply upload a video, and write the objects you want to detect! You can also play with confidence threshold, image size, or try the examples below. 👇"
128
  )
129
 
130
  with gr.Row():
@@ -147,29 +141,23 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
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  value=0.2,
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  step=0.05,
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  )
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- max_side = gr.Slider(
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- label="Image Size",
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- minimum=240,
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- maximum=1080,
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- value=640,
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- step=10,
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- )
157
  with gr.Row():
158
  submit = gr.Button(variant="primary")
159
 
160
  example = gr.Examples(
161
  examples=[
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- ["./football.mp4", 0.3, "person, ball, shoe", 640],
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- ["./cat.mp4", 0.2, "cat", 640],
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- ["./safari2.mp4", 0.3, "elephant, giraffe, springbok, zebra", 640],
165
  ],
166
- inputs=[input_video, conf, classes, max_side],
167
  outputs=output_video,
168
  )
169
 
170
  submit.click(
171
  fn=process_video,
172
- inputs=[input_video, conf, classes, max_side],
173
  outputs=[output_video, actual_fps],
174
  )
175
 
 
44
  input_video,
45
  confidence_threshold,
46
  classes,
 
47
  progress=gr.Progress(track_tqdm=True),
48
  ):
49
  classes = classes.strip(" ").split(",")
 
60
  frame = next(frame_generator)
61
  except StopIteration:
62
  break
63
+ results, fps = query(frame, classes, confidence_threshold)
 
 
64
  all_fps.append(fps)
65
  detections = []
66
 
 
89
  )
90
 
91
 
92
+ def query(frame, classes, confidence_threshold):
93
+ image = Image.fromarray(frame)
 
 
 
94
  inputs = processor(images=image, text=classes, return_tensors="pt").to(device)
95
  with torch.no_grad():
96
  start = time.time()
 
118
  """
119
  )
120
  gr.Markdown(
121
+ "Simply upload a video, and write the objects you want to detect! You can also play with confidence threshold or try the examples below. 👇"
122
  )
123
 
124
  with gr.Row():
 
141
  value=0.2,
142
  step=0.05,
143
  )
144
+
 
 
 
 
 
 
145
  with gr.Row():
146
  submit = gr.Button(variant="primary")
147
 
148
  example = gr.Examples(
149
  examples=[
150
+ ["./football.mp4", 0.3, "person, ball, shoe"],
151
+ ["./cat.mp4", 0.2, "cat"],
152
+ ["./safari2.mp4", 0.3, "elephant, giraffe, springbok, zebra"],
153
  ],
154
+ inputs=[input_video, conf, classes],
155
  outputs=output_video,
156
  )
157
 
158
  submit.click(
159
  fn=process_video,
160
+ inputs=[input_video, conf, classes],
161
  outputs=[output_video, actual_fps],
162
  )
163