Upload app.py.py

app.py.py

@@ -0,0 +1,149 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from six import BytesIO
+from PIL import Image
+import tensorflow as tf
+from object_detection.utils import label_map_util
+from object_detection.utils import visualization_utils as viz_utils
+from object_detection.utils import ops as utils_op
+import tarfile
+import wget 
+import gradio as gr
+from huggingface_hub import snapshot_download
+import os 
+
+import matplotlib.pyplot as plt
+from tqdm import tqdm
+import cv2
+
+PATH_TO_LABELS = 'data/label_map.pbtxt'   
+category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)
+
+def pil_image_as_numpy_array(pilimg):
+
+    img_array = tf.keras.utils.img_to_array(pilimg)
+    img_array = np.expand_dims(img_array, axis=0)
+    return img_array
+    
+def load_image_into_numpy_array(path):
+                                    
+    image = None
+    image_data = tf.io.gfile.GFile(path, 'rb').read()
+    image = Image.open(BytesIO(image_data))
+    return pil_image_as_numpy_array(image)            
+
+def load_model():
+    download_dir = snapshot_download(REPO_ID)
+    saved_model_dir = os.path.join(download_dir, "saved_model")
+    detection_model = tf.saved_model.load(saved_model_dir)
+    return detection_model
+
+def load_model2():
+    wget.download("https://nyp-aicourse.s3-ap-southeast-1.amazonaws.com/pretrained-models/balloon_model.tar.gz")
+    tarfile.open("balloon_model.tar.gz").extractall()
+    model_dir = 'saved_model'    
+    detection_model = tf.saved_model.load(str(model_dir))
+    return detection_model    
+
+threshold = 0.50
+
+def predict(pilimg,video_in_filepath,threshold):
+
+    image_np = pil_image_as_numpy_array(pilimg)
+    return predict2(image_np,threshold),None
+
+def predict2(image_np,threshold):
+
+    results = detection_model(image_np)
+
+    # different object detection models have additional results
+    result = {key:value.numpy() for key,value in results.items()}
+    
+    label_id_offset = 0
+    image_np_with_detections = image_np.copy()
+
+    viz_utils.visualize_boxes_and_labels_on_image_array(
+        image_np_with_detections[0],
+        result['detection_boxes'][0],
+        (result['detection_classes'][0] + label_id_offset).astype(int),
+        result['detection_scores'][0],
+        category_index,
+        use_normalized_coordinates=True,
+        max_boxes_to_draw=200,
+        min_score_thresh=float(threshold),
+        agnostic_mode=False,
+        line_thickness=2)
+
+    result_pil_img = tf.keras.utils.array_to_img(image_np_with_detections[0])
+    
+    return result_pil_img
+
+label_id_offset = 0
+samples_folder = 'test_samples'
+# image_path = 'test_samples/image489.png'
+
+def video_fn(video_reader):
+        #video_reader = cv2.VideoCapture(video_in_filepath)
+        
+        nb_frames = int(video_reader.get(cv2.CAP_PROP_FRAME_COUNT))
+        frame_h = int(video_reader.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        frame_w = int(video_reader.get(cv2.CAP_PROP_FRAME_WIDTH))
+        fps = video_reader.get(cv2.CAP_PROP_FPS)
+
+       
+        video_out_filepath = 'detected.mp4'
+        video_writer = cv2.VideoWriter(video_out_filepath,
+                                cv2.VideoWriter_fourcc(*'mp4v'), 
+                                fps, 
+                                (frame_w, frame_h))
+
+        for i in tqdm(range(nb_frames)):
+            ret, image_np = video_reader.read()
+            input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.uint8)
+            results = detection_model(input_tensor)
+            viz_utils.visualize_boxes_and_labels_on_image_array(
+                    image_np,
+                    results['detection_boxes'][0].numpy(),
+                    (results['detection_classes'][0].numpy()+ label_id_offset).astype(int),
+                    results['detection_scores'][0].numpy(),
+                    category_index,
+                    use_normalized_coordinates=True,
+                    max_boxes_to_draw=200,
+                    min_score_thresh=.50,
+                    agnostic_mode=False,
+                    line_thickness=2)
+
+            video_writer.write(np.uint8(image_np))
+                
+        # Release camera and close windows
+        video_reader.release()
+        video_writer.release() 
+        cv2.destroyAllWindows() 
+        cv2.waitKey(1)
+        return video_writer
+
+REPO_ID = "23b719w/assignment2_tfodmodel"
+detection_model = load_model()
+# pil_image = Image.open(image_path)
+# image_arr = pil_image_as_numpy_array(pil_image)
+
+# predicted_img = predict(image_arr)
+# predicted_img.save('predicted.jpg')
+
+# gr.Interface(fn=predict,
+#              inputs=[gr.Image(type="pil",label="Input Image"),gr.Video(label="Input Video"),gr.Textbox(placeholder="0.50",label="Set the confidence threshold (0.00-1.00)")],
+#              outputs=[gr.Image(type="pil",label="Output Image"),gr.Video(label="Output Video")], 
+#              title="Facemask & Glasses",
+#              description="Model: ssd_mobilenet_v2_320x320",
+#              theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky"),
+#              examples=[["test_samples/image489.png","",0.55], ["test_samples/image825.png","",0.55], ["test_samples/image833.png","",0.55], ["test_samples/image846.png","",0.55]]
+#              ).launch(share=True)
+
+gr.Interface(fn=video_fn,
+             inputs=gr.Video(label="Input Video"),
+             outputs=gr.Video(label="Output Video"), 
+             title="Facemask & Glasses",
+             description="Model: ssd_mobilenet_v2_320x320",
+             theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky"),
+             examples="test_samples/test_video.mp4"
+             ).launch(share=True)