fix

main.py

@@ -0,0 +1,119 @@
+import requests
+from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
+import torch
+from PIL import Image
+
+model1 = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+feature_extractor1 = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+tokenizer1 = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+
+device1 = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model1.to(device1)
+
+
+
+max_length = 16
+num_beams = 4
+gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
+
+def image_to_text_model_1(image_url):
+    raw_image = Image.open(requests.get(image_url, stream=True).raw).convert('RGB')
+
+    pixel_values = feature_extractor1(images=[raw_image], return_tensors="pt").pixel_values
+    pixel_values = pixel_values.to(device1)
+
+    output_ids = model1.generate(pixel_values, **gen_kwargs)
+
+    preds = tokenizer1.batch_decode(output_ids, skip_special_tokens=True)
+    preds = [pred.strip() for pred in preds]
+    return preds
+
+def bytes_to_text_model_1(bts):
+    pixel_values = feature_extractor1(images=[bts], return_tensors="pt").pixel_values
+    pixel_values = pixel_values.to(device1)
+
+    output_ids = model1.generate(pixel_values, **gen_kwargs)
+
+    preds = tokenizer1.batch_decode(output_ids, skip_special_tokens=True)
+    preds = [pred.strip() for pred in preds]
+    print(preds[0])
+
+
+import requests
+from PIL import Image
+from transformers import BlipProcessor, BlipForConditionalGeneration
+import torch
+
+device2 = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+processor2 = BlipProcessor.from_pretrained("noamrot/FuseCap")
+model2 = BlipForConditionalGeneration.from_pretrained("noamrot/FuseCap").to(device2)
+
+
+def image_to_text_model_2(img_url):
+    raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
+    text = "a picture of "
+    inputs = processor2(raw_image, text, return_tensors="pt").to(device2)
+
+    out = model2.generate(**inputs, num_beams = 3)
+    print(processor2.decode(out[0], skip_special_tokens=True))
+
+def bytes_to_text_model_2(byts):
+    text = "a picture of "
+    inputs = processor2(byts, text, return_tensors="pt").to(device2)
+
+    out = model2.generate(**inputs, num_beams = 3)
+    print(processor2.decode(out[0], skip_special_tokens=True))
+
+
+
+import requests
+from PIL import Image
+from transformers import BlipProcessor, BlipForConditionalGeneration
+
+processor3 = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
+model3 = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")
+
+def image_to_text_model_3(img_url):
+    raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
+    text = "a picture of"
+    inputs = processor3(raw_image, text, return_tensors="pt")
+    inputs = processor3(raw_image, return_tensors="pt")
+
+    out = model3.generate(**inputs)
+    print(processor3.decode(out[0], skip_special_tokens=True))
+
+def bytes_to_text_model_3(byts):
+    text = "a picture of"
+    inputs = processor3(byts, text, return_tensors="pt")
+    inputs = processor3(byts, return_tensors="pt")
+
+    out = model3.generate(**inputs)
+    print(processor3.decode(out[0], skip_special_tokens=True))
+
+
+import cv2 
+  
+def FrameCapture(path): 
+    vidObj = cv2.VideoCapture(path) 
+    count = 0
+    success = 1
+  
+    while success: 
+        success, image = vidObj.read() 
+
+        if count % 20 == 0:
+        
+            print("NEW FRAME")
+            print("MODEL 1")
+            bytes_to_text_model_1(image)
+            print("MODEL 2")
+            bytes_to_text_model_2(image)
+            print("MODEL 3")
+            bytes_to_text_model_3(image)
+
+            print("\n\n")
+  
+        count += 1
+
+
+FrameCapture("animation.mp4")