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app.py

@@ -1,176 +1,176 @@
-import torch
-from models.common import DetectMultiBackend
-from utils.general import (check_img_size, cv2,
-                            non_max_suppression, scale_boxes)
-from utils.plots import Annotator, colors
-import numpy as np
-import gradio as gr
-import time
-data = 'data/coco128.yaml' 
-
-
-def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleup=True, stride=32):
-    # Resize and pad image while meeting stride-multiple constraints
-    shape = im.shape[:2]  # current shape [height, width]
-    if isinstance(new_shape, int):
-        new_shape = (new_shape, new_shape)
-
-    # Scale ratio (new / old)
-    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
-    if not scaleup:  # only scale down, do not scale up (for better val mAP)
-        r = min(r, 1.0)
-
-    # Compute padding
-    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
-    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
-
-    if auto:  # minimum rectangle
-        dw, dh = np.mod(dw, stride), np.mod(dh, stride)  # wh padding
-
-    dw /= 2  # divide padding into 2 sides
-    dh /= 2
-
-    if shape[::-1] != new_unpad:  # resize
-        im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
-    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
-    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
-    im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
-    return im, r, (dw, dh)
-
-names = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 
-         'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 
-         'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 
-         'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 
-         'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 
-         'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 
-         'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 
-         'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 
-         'hair drier', 'toothbrush']
-
-
-
-
-def detect(im,model,device,iou_threshold=0.45,confidence_threshold=0.25):
-    im = np.array(im)
-    imgsz=(640, 640)  # inference size (pixels)
-    data = 'data/coco128.yaml'  # data.yaml path
-    # Load model
-    stride, names, pt = model.stride, model.names, model.pt
-    imgsz = check_img_size(imgsz, s=stride)  # check image size
-
-    # Run inference
-    # model.warmup(imgsz=(1))  # warmup
-    
-    imgs = im.copy()  # for NMS
-
-    image, ratio, dwdh = letterbox(im, auto=False)
-    print(image.shape)
-    image = image.transpose((2, 0, 1))
-    img = torch.from_numpy(image).to(device)
-    img = img.float()  # uint8 to fp16/32
-    img /= 255.0  # 0 - 255 to 0.0 - 1.0
-    if img.ndimension() == 3:
-        img = img.unsqueeze(0)
-
-# Inference
-    start = time.time()
-    pred = model(img, augment=False)
-    fps_inference = 1/(time.time()-start)
-# NMS
-    pred = non_max_suppression(pred, confidence_threshold, iou_threshold, None, False, max_det=10)
-
-
-    for i, det in enumerate(pred):  # detections per image
-        if len(det):
-            # Rescale boxes from img_size to im0 size
-            det[:, :4] = scale_boxes(img.shape[2:], det[:, :4], imgs.shape).round()
-
-            annotator = Annotator(imgs, line_width=3, example=str(names))
-            hide_labels = False
-            hide_conf = False
-            # Write results
-            for *xyxy, conf, cls in reversed(det):
-                c = int(cls)  # integer class
-                label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
-                print(xyxy,label)
-                annotator.box_label(xyxy, label, color=colors(c, True))
-
-    return imgs,fps_inference
-
-
-def inference(img,model_link,iou_threshold,confidence_threshold):
-    print(model_link)
-    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
-    # Load model
-    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
-    model = DetectMultiBackend('weights/'+str(model_link)+'.pt', device=device, dnn=False, data=data, fp16=False)  
-    return detect(img,model,device,iou_threshold,confidence_threshold)
-
-
-def inference2(video,model_link,iou_threshold,confidence_threshold):
-    print(model_link)
-    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
-    # Load model
-    model = DetectMultiBackend('weights/'+str(model_link)+'.pt', device=device, dnn=False, data=data, fp16=False)  
-    frames = cv2.VideoCapture(video)
-    fps = frames.get(cv2.CAP_PROP_FPS)
-    image_size = (int(frames.get(cv2.CAP_PROP_FRAME_WIDTH)),int(frames.get(cv2.CAP_PROP_FRAME_HEIGHT)))
-    finalVideo = cv2.VideoWriter('output.mp4',cv2.VideoWriter_fourcc(*'VP90'), fps, image_size)
-    fps_video = []
-    while frames.isOpened():
-        ret,frame = frames.read()
-        if not ret:
-            break
-        frame,fps = detect(frame,model,device,iou_threshold,confidence_threshold)
-        fps_video.append(fps)
-        finalVideo.write(frame)
-    frames.release()
-    finalVideo.release()
-    return 'output.mp4',np.mean(fps_video)
-
-
-
-examples_images = ['data/images/bus.jpg',
-                    'data/images/zidane.jpg',]
-examples_videos = ['data/video/input_0.mp4',
-                    'data/video/input_1.mp4'] 
-
-models = ['yolov5s','yolov5n','yolov5m','yolov5l','yolov5x']
-
-with gr.Blocks() as demo:
-    gr.Markdown("## YOLOv5 Inference")
-    with gr.Tab("Image"):
-        gr.Markdown("## YOLOv5 Inference on Image")
-        with gr.Row():
-            image_input = gr.Image(type='pil', label="Input Image", source="upload")
-            image_output = gr.Image(type='pil', label="Output Image", source="upload")
-        fps_image = gr.Number(value=0,label='FPS')
-        image_drop = gr.Dropdown(choices=models,value=models[0])
-        image_iou_threshold = gr.Slider(label="IOU Threshold",interactive=True, minimum=0.0, maximum=1.0, value=0.45)
-        image_conf_threshold = gr.Slider(label="Confidence Threshold",interactive=True, minimum=0.0, maximum=1.0, value=0.25)
-        gr.Examples(examples=examples_images,inputs=image_input,outputs=image_output)
-        text_button = gr.Button("Detect")
-    with gr.Tab("Video"):
-        gr.Markdown("## YOLOv5 Inference on Video")
-        with gr.Row():
-            video_input = gr.Video(type='pil', label="Input Image", source="upload")
-            video_output = gr.Video(type="pil", label="Output Image",format="mp4")
-        fps_video = gr.Number(value=0,label='FPS')
-        video_drop = gr.Dropdown(choices=models,value=models[0])
-        video_iou_threshold = gr.Slider(label="IOU Threshold",interactive=True, minimum=0.0, maximum=1.0, value=0.45)
-        video_conf_threshold = gr.Slider(label="Confidence Threshold",interactive=True, minimum=0.0, maximum=1.0, value=0.25)
-        gr.Examples(examples=examples_videos,inputs=video_input,outputs=video_output)
-        video_button = gr.Button("Detect")
-    
-    with gr.Tab("Webcam Video"):
-        gr.Markdown("## YOLOv5 Inference on Webcam Video")
-        gr.Markdown("Coming Soon")
-
-    text_button.click(inference, inputs=[image_input,image_drop,
-                                         image_iou_threshold,image_conf_threshold],
-                                        outputs=[image_output,fps_image])
-    video_button.click(inference2, inputs=[video_input,video_drop,
-                                           video_iou_threshold,video_conf_threshold],            
-                                        outputs=[video_output,fps_video])
-
+import torch
+from models.common import DetectMultiBackend
+from utils.general import (check_img_size, cv2,
+                            non_max_suppression, scale_boxes)
+from utils.plots import Annotator, colors
+import numpy as np
+import gradio as gr
+import time
+data = 'data/coco128.yaml' 
+
+
+def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleup=True, stride=32):
+    # Resize and pad image while meeting stride-multiple constraints
+    shape = im.shape[:2]  # current shape [height, width]
+    if isinstance(new_shape, int):
+        new_shape = (new_shape, new_shape)
+
+    # Scale ratio (new / old)
+    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
+    if not scaleup:  # only scale down, do not scale up (for better val mAP)
+        r = min(r, 1.0)
+
+    # Compute padding
+    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
+    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
+
+    if auto:  # minimum rectangle
+        dw, dh = np.mod(dw, stride), np.mod(dh, stride)  # wh padding
+
+    dw /= 2  # divide padding into 2 sides
+    dh /= 2
+
+    if shape[::-1] != new_unpad:  # resize
+        im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
+    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
+    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
+    im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
+    return im, r, (dw, dh)
+
+names = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 
+         'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 
+         'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 
+         'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 
+         'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 
+         'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 
+         'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 
+         'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 
+         'hair drier', 'toothbrush']
+
+
+
+
+def detect(im,model,device,iou_threshold=0.45,confidence_threshold=0.25):
+    im = np.array(im)
+    imgsz=(640, 640)  # inference size (pixels)
+    data = 'data/coco128.yaml'  # data.yaml path
+    # Load model
+    stride, names, pt = model.stride, model.names, model.pt
+    imgsz = check_img_size(imgsz, s=stride)  # check image size
+
+    # Run inference
+    # model.warmup(imgsz=(1))  # warmup
+    
+    imgs = im.copy()  # for NMS
+
+    image, ratio, dwdh = letterbox(im, auto=False)
+    print(image.shape)
+    image = image.transpose((2, 0, 1))
+    img = torch.from_numpy(image).to(device)
+    img = img.float()  # uint8 to fp16/32
+    img /= 255.0  # 0 - 255 to 0.0 - 1.0
+    if img.ndimension() == 3:
+        img = img.unsqueeze(0)
+
+# Inference
+    start = time.time()
+    pred = model(img, augment=False)
+    fps_inference = 1/(time.time()-start)
+# NMS
+    pred = non_max_suppression(pred, confidence_threshold, iou_threshold, None, False, max_det=10)
+
+
+    for i, det in enumerate(pred):  # detections per image
+        if len(det):
+            # Rescale boxes from img_size to im0 size
+            det[:, :4] = scale_boxes(img.shape[2:], det[:, :4], imgs.shape).round()
+
+            annotator = Annotator(imgs, line_width=3, example=str(names))
+            hide_labels = False
+            hide_conf = False
+            # Write results
+            for *xyxy, conf, cls in reversed(det):
+                c = int(cls)  # integer class
+                label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
+                print(xyxy,label)
+                annotator.box_label(xyxy, label, color=colors(c, True))
+
+    return imgs,fps_inference
+
+
+def inference(img,model_link,iou_threshold,confidence_threshold):
+    print(model_link)
+    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+    # Load model
+    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+    model = DetectMultiBackend('weights/'+str(model_link)+'.pt', device=device, dnn=False, data=data, fp16=False)  
+    return detect(img,model,device,iou_threshold,confidence_threshold)
+
+
+def inference2(video,model_link,iou_threshold,confidence_threshold):
+    print(model_link)
+    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+    # Load model
+    model = DetectMultiBackend('weights/'+str(model_link)+'.pt', device=device, dnn=False, data=data, fp16=False)  
+    frames = cv2.VideoCapture(video)
+    fps = frames.get(cv2.CAP_PROP_FPS)
+    image_size = (int(frames.get(cv2.CAP_PROP_FRAME_WIDTH)),int(frames.get(cv2.CAP_PROP_FRAME_HEIGHT)))
+    finalVideo = cv2.VideoWriter('output.mp4',cv2.VideoWriter_fourcc(*'VP90'), fps, image_size)
+    fps_video = []
+    while frames.isOpened():
+        ret,frame = frames.read()
+        if not ret:
+            break
+        frame,fps = detect(frame,model,device,iou_threshold,confidence_threshold)
+        fps_video.append(fps)
+        finalVideo.write(frame)
+    frames.release()
+    finalVideo.release()
+    return 'output.mp4',np.mean(fps_video)
+
+
+
+examples_images = ['data/images/bus.jpg',
+                    'data/images/zidane.jpg',]
+examples_videos = ['data/video/input_0.mp4',
+                    'data/video/input_1.mp4'] 
+
+models = ['yolov5s','yolov5n','yolov5m','yolov5l','yolov5x']
+
+with gr.Blocks() as demo:
+    gr.Markdown("## YOLOv5 Inference")
+    with gr.Tab("Image"):
+        gr.Markdown("## YOLOv5 Inference on Image")
+        with gr.Row():
+            image_input = gr.Image(type='pil', label="Input Image", sources="upload")
+            image_output = gr.Image(type='pil', label="Output Image", sources="upload")
+        fps_image = gr.Number(value=0,label='FPS')
+        image_drop = gr.Dropdown(choices=models,value=models[0])
+        image_iou_threshold = gr.Slider(label="IOU Threshold",interactive=True, minimum=0.0, maximum=1.0, value=0.45)
+        image_conf_threshold = gr.Slider(label="Confidence Threshold",interactive=True, minimum=0.0, maximum=1.0, value=0.25)
+        gr.Examples(examples=examples_images,inputs=image_input,outputs=image_output)
+        text_button = gr.Button("Detect")
+    with gr.Tab("Video"):
+        gr.Markdown("## YOLOv5 Inference on Video")
+        with gr.Row():
+            video_input = gr.Video(label="Input Image", sources="upload")
+            video_output = gr.Video(label="Output Image",format="mp4")
+        fps_video = gr.Number(value=0,label='FPS')
+        video_drop = gr.Dropdown(choices=models,value=models[0])
+        video_iou_threshold = gr.Slider(label="IOU Threshold",interactive=True, minimum=0.0, maximum=1.0, value=0.45)
+        video_conf_threshold = gr.Slider(label="Confidence Threshold",interactive=True, minimum=0.0, maximum=1.0, value=0.25)
+        gr.Examples(examples=examples_videos,inputs=video_input,outputs=video_output)
+        video_button = gr.Button("Detect")
+    
+    with gr.Tab("Webcam Video"):
+        gr.Markdown("## YOLOv5 Inference on Webcam Video")
+        gr.Markdown("Coming Soon")
+
+    text_button.click(inference, inputs=[image_input,image_drop,
+                                         image_iou_threshold,image_conf_threshold],
+                                        outputs=[image_output,fps_image])
+    video_button.click(inference2, inputs=[video_input,video_drop,
+                                           video_iou_threshold,video_conf_threshold],            
+                                        outputs=[video_output,fps_video])
+
 demo.launch()

requirements.txt

@@ -1,12 +1,12 @@
-ipython
-numpy>=1.18.5
-opencv-python>=4.1.1
-Pillow>=7.1.2
-scipy>=1.4.1
-torch>=1.7.0
-torchvision>=0.8.1
-tqdm>=4.64.0
-seaborn>=0.11.0
-gradio==3.44.1
-psutil
-pandas
+ipython==8.30.0
+numpy==1.24.3
+opencv-python==4.10.0
+Pillow==11.0.0
+scipy==1.14.1
+torch==2.3.0
+torchvision==0.18.0
+tqdm==4.67.1
+seaborn==0.13.2
+gradio==5.9.1
+psutil==5.9.8
+pandas==2.2.3