Reset the main functions

app.py

@@ -12,111 +12,68 @@ from transformers.models.detr.feature_extraction_detr import rgb_to_id
 
 import os
 
-
-def detect_objects(model_name,url_input,image_input,threshold):
-    
-
-    if 'maskformer' in model_name:
-        if validators.url(url_input):
-            image = Image.open(requests.get(url_input, stream=True).raw)
-            tb_label = "Confidence Values URL"
-            
-        elif image_input:
-            image = image_input
-            tb_label = "Confidence Values Upload"
-
-        # NOTE: Pulling from the example on https://huggingface.co/facebook/maskformer-swin-large-coco
-        #     and https://huggingface.co/spaces/ajcdp/Image-Segmentation-Gradio/blob/main/app.py 
-
-        processor = MaskFormerImageProcessor.from_pretrained(model_name)
-        model = MaskFormerForInstanceSegmentation.from_pretrained(model_name)
-
-        target_size = (image.size[0], image.size[1])
-        inputs = processor(images=image, return_tensors="pt")
-        with torch.no_grad():
-            outputs = model(**inputs)
-        outputs.class_queries_logits = outputs.class_queries_logits.cpu()
-        outputs.masks_queries_logits = outputs.masks_queries_logits.cpu()
-        results = processor.post_process_segmentation(outputs=outputs, target_size=target_size)[0].cpu().detach()
-        results = torch.argmax(results, dim=0).numpy()
-        results = visualize_instance_seg_mask(results)
-        return results, "EMPTY"
-
-        # for result in results:
-        #     boxes = result.boxes.cpu().numpy()
-        #     for i, box in enumerate(boxes):
-        #         # r = box.xyxy[0].astype(int)
-        #         coordinates = box.xyxy[0].astype(int)
-        #         try:
-        #             label = YOLOV8_LABELS[int(box.cls)]
-        #         except:
-        #             label = "ERROR"
-        #         try:
-        #             confi = float(box.conf)
-        #         except:
-        #             confi = 0.0
-        #         # final_str_abv += str() + "__" + str(box.cls) + "__" + str(box.conf) + "__" + str(box) + "\n"
-        #         if confi >= threshold:
-        #             final_str_abv += f"Detected `{label}` with confidence `{confi}` at location `{coordinates}`\n"
-        #         else:
-        #             final_str_else += f"Detected `{label}` with confidence `{confi}` at location `{coordinates}`\n"
-
-        # final_str = "{:*^50}\n".format("ABOVE THRESHOLD OR EQUAL") + final_str_abv + "\n{:*^50}\n".format("BELOW THRESHOLD")+final_str_else
-
-        # return render, final_str
-    elif "detr" in model_name:
-        # NOTE: Using the example on https://huggingface.co/facebook/detr-resnet-50-panoptic
-        if validators.url(url_input):
-            image = Image.open(requests.get(url_input, stream=True).raw)
-            tb_label = "Confidence Values URL"
-            
-        elif image_input:
-            image = image_input
-            tb_label = "Confidence Values Upload"
-
-        feature_extractor = DetrFeatureExtractor.from_pretrained(model_name)
-        model = DetrForSegmentation.from_pretrained(model_name)
-        inputs = feature_extractor(images=image, return_tensors="pt")
-
-        outputs = model(**inputs)
-
-        # use the `post_process_panoptic` method of `DetrFeatureExtractor` to convert to COCO format
-        processed_sizes = torch.as_tensor(inputs["pixel_values"].shape[-2:]).unsqueeze(0)
-        result = feature_extractor.post_process_panoptic(outputs, processed_sizes)[0]
-        
-        # the segmentation is stored in a special-format png
-        panoptic_seg = Image.open(io.BytesIO(result["png_string"]))
-        panoptic_seg = numpy.array(panoptic_seg, dtype=numpy.uint8)
-        # retrieve the ids corresponding to each mask
-        panoptic_seg_id = rgb_to_id(panoptic_seg)
-
-        
-
-        return gr.Image.update(), "EMPTY"
-        
-       
-        
-        #Visualize prediction
-        viz_img = visualize_prediction(image, processed_outputs, threshold, model.config.id2label)
-        
-        # return [viz_img, processed_outputs]
-        # print(type(viz_img))
-    
-        final_str_abv = ""
-        final_str_else = ""
-        for score, label, box in sorted(zip(processed_outputs["scores"], processed_outputs["labels"], processed_outputs["boxes"]), key = lambda x: x[0].item(), reverse=True):
-            box = [round(i, 2) for i in box.tolist()]
-            if score.item() >= threshold:
-                final_str_abv += f"Detected `{model.config.id2label[label.item()]}` with confidence `{round(score.item(), 3)}` at location `{box}`\n"
-            else:
-                final_str_else += f"Detected `{model.config.id2label[label.item()]}` with confidence `{round(score.item(), 3)}` at location `{box}`\n"
-    
-        # https://docs.python.org/3/library/string.html#format-examples
-        final_str = "{:*^50}\n".format("ABOVE THRESHOLD OR EQUAL") + final_str_abv + "\n{:*^50}\n".format("BELOW THRESHOLD")+final_str_else
-            
-        return viz_img, final_str
+# colors for visualization
+COLORS = [
+    [0.000, 0.447, 0.741],
+    [0.850, 0.325, 0.098],
+    [0.929, 0.694, 0.125],
+    [0.494, 0.184, 0.556],
+    [0.466, 0.674, 0.188],
+    [0.301, 0.745, 0.933]
+]
+
+YOLOV8_LABELS = ['pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor']
+
+def make_prediction(img, feature_extractor, model):
+    inputs = feature_extractor(img, return_tensors="pt")
+    outputs = model(**inputs)
+    img_size = torch.tensor([tuple(reversed(img.size))])
+    processed_outputs = feature_extractor.post_process(outputs, img_size)
+    return processed_outputs
+
+def fig2img(fig):
+    buf = io.BytesIO()
+    fig.savefig(buf, bbox_inches="tight")
+    buf.seek(0)
+    img = Image.open(buf)
+    return img
+
+
+def visualize_prediction(pil_img, output_dict, threshold=0.7, id2label=None):
+    keep = output_dict["scores"] > threshold
+    boxes = output_dict["boxes"][keep].tolist()
+    scores = output_dict["scores"][keep].tolist()
+    labels = output_dict["labels"][keep].tolist()
+    if id2label is not None:
+        labels = [id2label[x] for x in labels]
+
+    # print("Labels " + str(labels))
+
+    plt.figure(figsize=(16, 10))
+    plt.imshow(pil_img)
+    ax = plt.gca()
+    colors = COLORS * 100
+    for score, (xmin, ymin, xmax, ymax), label, color in zip(scores, boxes, labels, colors):
+        ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, fill=False, color=color, linewidth=3))
+        ax.text(xmin, ymin, f"{label}: {score:0.2f}", fontsize=15, bbox=dict(facecolor="yellow", alpha=0.5))
+    plt.axis("off")
+    return fig2img(plt.gcf())
+
+def segment_images(model_name,url_input,image_input,threshold):
+    ####
+    # Get Image Object
+    if validators.url(url_input):
+        image = Image.open(requests.get(url_input, stream=True).raw)
+    elif image_input:
+        image = image_input
+    ####
+
+    if "detr" in model_name:
+        pass
+    elif "maskformer" in model_name.lower():
+        pass
     else:
-        raise NameError(f"Model name {model_name} not prepared")
+        raise NameError("Model is not implemented")
         
 def set_example_image(example: list) -> dict:
     return gr.Image.update(value=example[0])
@@ -197,13 +154,13 @@ with demo:
     options.change(fn=changing, inputs=[], outputs=[img_but, url_but])
 
     
-    url_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=[img_output_from_url, output_text1],queue=True)
-    img_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=[img_output_from_upload, output_text1],queue=True)
-    # url_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=[img_output_from_url, _],queue=True)
-    # img_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=[img_output_from_upload, _],queue=True)
+    url_but.click(segment_images,inputs=[options,url_input,img_input,slider_input],outputs=[img_output_from_url, output_text1],queue=True)
+    img_but.click(segment_images,inputs=[options,url_input,img_input,slider_input],outputs=[img_output_from_upload, output_text1],queue=True)
+    # url_but.click(segment_images,inputs=[options,url_input,img_input,slider_input],outputs=[img_output_from_url, _],queue=True)
+    # img_but.click(segment_images,inputs=[options,url_input,img_input,slider_input],outputs=[img_output_from_upload, _],queue=True)
     
-    # url_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=img_output_from_url,queue=True)
-    # img_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=img_output_from_upload,queue=True)
+    # url_but.click(segment_images,inputs=[options,url_input,img_input,slider_input],outputs=img_output_from_url,queue=True)
+    # img_but.click(segment_images,inputs=[options,url_input,img_input,slider_input],outputs=img_output_from_upload,queue=True)
 
     
     example_images.click(fn=set_example_image,inputs=[example_images],outputs=[img_input])