Spaces:
Sleeping
Sleeping
Update app.py
Browse files
app.py
CHANGED
@@ -69,20 +69,23 @@ def predict2(image_np):
|
|
69 |
return result_pil_img
|
70 |
|
71 |
def detect_video(video):
|
72 |
-
|
73 |
-
|
74 |
-
|
75 |
-
|
76 |
-
|
77 |
-
|
78 |
-
fps = cap.get(cv2.CAP_PROP_FPS)
|
79 |
|
80 |
-
|
|
|
|
|
|
|
|
|
81 |
for i in tqdm(range(nb_frames)):
|
82 |
-
ret, image_np =
|
83 |
input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.uint8)
|
84 |
results = detection_model(input_tensor)
|
85 |
-
|
86 |
image_np,
|
87 |
results['detection_boxes'][0].numpy(),
|
88 |
(results['detection_classes'][0].numpy()+ label_id_offset).astype(int),
|
@@ -94,21 +97,14 @@ def detect_video(video):
|
|
94 |
agnostic_mode=False,
|
95 |
line_thickness=2)
|
96 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
97 |
|
98 |
-
# Yield the processed frame
|
99 |
-
yield image_np_with_detections
|
100 |
-
|
101 |
-
# Release resources
|
102 |
-
cap.release()
|
103 |
-
|
104 |
-
|
105 |
-
inputs_video = [
|
106 |
-
gr.components.Video( label="Input Video"),
|
107 |
-
|
108 |
-
]
|
109 |
-
outputs_video = [
|
110 |
-
gr.components.Image( label="Output Image"),
|
111 |
-
]
|
112 |
|
113 |
label_id_offset = 0
|
114 |
REPO_ID = "apailang/mytfodmodel"
|
@@ -142,25 +138,19 @@ tts_demo = gr.Interface(
|
|
142 |
cache_examples=True
|
143 |
)#.launch(share=True)
|
144 |
|
|
|
145 |
|
146 |
a = os.path.join(os.path.dirname(__file__), "data/a.mp4") # Video
|
147 |
b = os.path.join(os.path.dirname(__file__), "data/b.mp4") # Video
|
148 |
c = os.path.join(os.path.dirname(__file__), "data/c.mp4") # Video
|
149 |
|
150 |
|
151 |
-
|
152 |
-
# fn=show_preds_video,
|
153 |
-
# inputs=inputs_video,
|
154 |
-
# outputs=outputs_video,
|
155 |
-
# title="Pothole detector",
|
156 |
-
# examples=video_path,
|
157 |
-
# cache_examples=False,
|
158 |
-
# )
|
159 |
|
160 |
stt_demo = gr.Interface(
|
161 |
fn=detect_video,
|
162 |
inputs=inputs_video,
|
163 |
-
outputs=
|
164 |
examples=[
|
165 |
[a],
|
166 |
[b],
|
|
|
69 |
return result_pil_img
|
70 |
|
71 |
def detect_video(video):
|
72 |
+
video_reader = cv2.VideoCapture(video)
|
73 |
+
|
74 |
+
nb_frames = int(video_reader.get(cv2.CAP_PROP_FRAME_COUNT))
|
75 |
+
frame_h = int(video_reader.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
76 |
+
frame_w = int(video_reader.get(cv2.CAP_PROP_FRAME_WIDTH))
|
77 |
+
fps = video_reader.get(cv2.CAP_PROP_FPS)
|
|
|
78 |
|
79 |
+
video_writer = cv2.VideoWriter(video_out_filepath,
|
80 |
+
cv2.VideoWriter_fourcc(*'mp4v'),
|
81 |
+
fps,
|
82 |
+
(frame_w, frame_h))
|
83 |
+
|
84 |
for i in tqdm(range(nb_frames)):
|
85 |
+
ret, image_np = video_reader.read()
|
86 |
input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.uint8)
|
87 |
results = detection_model(input_tensor)
|
88 |
+
viz_utils.visualize_boxes_and_labels_on_image_array(
|
89 |
image_np,
|
90 |
results['detection_boxes'][0].numpy(),
|
91 |
(results['detection_classes'][0].numpy()+ label_id_offset).astype(int),
|
|
|
97 |
agnostic_mode=False,
|
98 |
line_thickness=2)
|
99 |
|
100 |
+
video_writer.write(np.uint8(image_np))
|
101 |
+
|
102 |
+
# Release camera and close windows
|
103 |
+
video_reader.release()
|
104 |
+
video_writer.release()
|
105 |
+
cv2.destroyAllWindows()
|
106 |
+
cv2.waitKey(1)
|
107 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
108 |
|
109 |
label_id_offset = 0
|
110 |
REPO_ID = "apailang/mytfodmodel"
|
|
|
138 |
cache_examples=True
|
139 |
)#.launch(share=True)
|
140 |
|
141 |
+
samples_folder = 'data'
|
142 |
|
143 |
a = os.path.join(os.path.dirname(__file__), "data/a.mp4") # Video
|
144 |
b = os.path.join(os.path.dirname(__file__), "data/b.mp4") # Video
|
145 |
c = os.path.join(os.path.dirname(__file__), "data/c.mp4") # Video
|
146 |
|
147 |
|
148 |
+
video_out_file = os.path.join(samples_folder,'detected' + '.mp4')
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
149 |
|
150 |
stt_demo = gr.Interface(
|
151 |
fn=detect_video,
|
152 |
inputs=inputs_video,
|
153 |
+
outputs="data/detected.mp4",
|
154 |
examples=[
|
155 |
[a],
|
156 |
[b],
|