Merge branch 'alex_dev' of https://huggingface.co/spaces/piperod91/shark_detection into alex_dev

app.py

@@ -1,6 +1,8 @@
 
+
 import subprocess
 import os  
+import os  
 if os.getenv('SYSTEM') == 'spaces':
 
     subprocess.call('pip install -U openmim'.split())
@@ -14,6 +16,9 @@ if os.getenv('SYSTEM') == 'spaces':
 
 import gradio as gr
 
+import gradio as gr
+
+from huggingface_hub import snapshot_download
 from huggingface_hub import snapshot_download
 import cv2 
 import dotenv 
@@ -28,6 +33,10 @@ from inference import classes
 from inference import class_sizes_lower
 from metrics import process_results_for_plot
 from metrics import prediction_dashboard
+from inference import classes
+from inference import class_sizes_lower
+from metrics import process_results_for_plot
+from metrics import prediction_dashboard
 import os
 import pathlib
 import multiprocessing as mp

inference.py

@@ -15,6 +15,7 @@ from huggingface_hub import hf_hub_download
 from huggingface_hub import snapshot_download
 from time import time
 
+classes = ['Beach',
 classes = ['Beach',
  'Sea',
  'Wave',
@@ -24,13 +25,19 @@ classes = ['Beach',
  'Foam',
  'Algae',
  'Vegetation',
+ 'Algae',
+ 'Vegetation',
  'Watermark',
  'Bird',
  'Ship',
  'Boat',
  'Car',
+ 'Ship',
+ 'Boat',
+ 'Car',
  'Kayak',
  "Shark's line",
+ "Shark's line",
  'Dock',
  'Dog',
  'Unidentifiable shade',
@@ -46,6 +53,8 @@ classes = ['Beach',
  'Person',
  'ocean',
  'Surfer',
+ 'ocean',
+ 'Surfer',
  'Surfer',
  'Fish',
  'Killer whale',
@@ -67,6 +76,16 @@ classes = ['Beach',
  'Blacktip shark',
  'Tiger shark',
  'Bull shark']*3
+ 'Shark',
+ 'Nurse shark',
+ 'Silky shark',
+ 'Leopard shark',
+ 'Shortfin mako shark',
+ 'Hammerhead shark',
+ 'Oceanic whitetip shark',
+ 'Blacktip shark',
+ 'Tiger shark',
+ 'Bull shark']*3
 
 class_sizes = {'Beach': None,
                'Sea': None,
@@ -124,6 +143,11 @@ class_sizes = {'Beach': None,
 
 class_sizes_lower = {k.lower(): v for k, v in class_sizes.items()}
    
+classes_is_shark = [1 if 'shark' in x.lower() else 0 for x in classes]
+classes_is_human = [1 if 'person' or 'surfer' in x.lower() else 0 for x in classes]
+classes_is_unknown = [1 if 'unidentifiable' in x.lower() else 0 for x in classes]
+class_sizes_lower = {k.lower(): v for k, v in class_sizes.items()}
+   
 classes_is_shark = [1 if 'shark' in x.lower() else 0 for x in classes]
 classes_is_human = [1 if 'person' or 'surfer' in x.lower() else 0 for x in classes]
 classes_is_unknown = [1 if 'unidentifiable' in x.lower() else 0 for x in classes]
@@ -131,8 +155,15 @@ classes_is_unknown = [1 if 'unidentifiable' in x.lower() else 0 for x in classes
 classes_is_shark_id = [i for i, x in enumerate(classes_is_shark) if x == 1]
 classes_is_human_id = [i for i, x in enumerate(classes_is_human) if x == 1]
 classes_is_unknown_id = [i for i, x in enumerate(classes_is_unknown) if x == 1]
+classes_is_shark_id = [i for i, x in enumerate(classes_is_shark) if x == 1]
+classes_is_human_id = [i for i, x in enumerate(classes_is_human) if x == 1]
+classes_is_unknown_id = [i for i, x in enumerate(classes_is_unknown) if x == 1]
 
 
+if not os.path.exists('model'):
+    REPO_ID = "SharkSpace/maskformer_model"
+    FILENAME = "mask2former"
+    snapshot_download(repo_id=REPO_ID, token= os.environ.get('SHARK_MODEL'),local_dir='model/')
 if not os.path.exists('model'):
     REPO_ID = "SharkSpace/maskformer_model"
     FILENAME = "mask2former"
@@ -151,6 +182,7 @@ register_all_modules()
 # build the model from a config file and a checkpoint file
 model = init_detector(config_file, checkpoint_file, device='cuda:0')  # or device='cuda:0'
 model.dataset_meta['palette'] = model.dataset_meta['palette'] + model.dataset_meta['palette'][-23:]
+model.dataset_meta['palette'] = model.dataset_meta['palette'] + model.dataset_meta['palette'][-23:]
 model.dataset_meta['classes'] = classes
 print(model.cfg.visualizer)
 # init visualizer(run the block only once in jupyter notebook)
@@ -162,12 +194,20 @@ visualizer.dataset_meta = model.dataset_meta
 classes = visualizer.dataset_meta.get('classes', None)
 palette = visualizer.dataset_meta.get('palette', None)
 
+print(len(classes))
+print(len(palette))
+def inference_frame_serial(image, visualize = True):
+    #start = time()
+classes = visualizer.dataset_meta.get('classes', None)
+palette = visualizer.dataset_meta.get('palette', None)
+
 print(len(classes))
 print(len(palette))
 def inference_frame_serial(image, visualize = True):
     #start = time()
     result = inference_detector(model, image)
     #print(f'inference time: {time()-start}')
+    #print(f'inference time: {time()-start}')
     # show the results
     if visualize:
         visualizer.add_datasample(
@@ -181,6 +221,18 @@ def inference_frame_serial(image, visualize = True):
     else:
         frame = None
     return frame, result
+    if visualize:
+        visualizer.add_datasample(
+        'result',
+        image,
+        data_sample=result,
+        draw_gt = None,
+        show=False
+        )
+        frame = visualizer.get_image()
+    else:
+        frame = None
+    return frame, result
 
 def inference_frame(image):
     result = inference_detector(model, image)