Update app.py

app.py

@@ -1,16 +1,16 @@
-# import os
-# import sys
-# import argparse
-# import time
-# from pathlib import Path
-# import pandas as pd
+import os
+import sys
+import argparse
+import time
+from pathlib import Path
+import pandas as pd
 
 import gradio as gr
-# import cv2
+import cv2
 from PIL import Image
-# import torch
-# import torch.backends.cudnn as cudnn
-# from numpy import random
+import torch
+import torch.backends.cudnn as cudnn
+from numpy import random
 import numpy as np
 
 # BASE_DIR = "/home/user/app"
@@ -18,200 +18,207 @@ import numpy as np
 # os.makedirs(f"{BASE_DIR}/input",exist_ok=True)
 # os.system(f"git clone https://github.com/WongKinYiu/yolov7.git {BASE_DIR}/yolov7")
 # sys.path.append(f'{BASE_DIR}/yolov7')
+os.system("pip install yolov7-package==0.0.12")
 
-# def detect(opt, save_img=False):    
+def detect(opt, save_img=False):    
 #     from models.experimental import attempt_load
 #     from utils.datasets import LoadStreams, LoadImages
 #     from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
 #         scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path
 #     from utils.plots import plot_one_box
 #     from utils.torch_utils import select_device, load_classifier, time_synchronized, TracedModel
+
+    from yolov7_package.models.experimental import attempt_load
+    from yolov7_package.utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
+        scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path
+    from yolov7_package.utils.torch_utils import select_device, load_classifier, time_synchronized, TracedModel
+    from yolov7_package.utils.datasets import LoadStreams, LoadImages
     
-#     bbox = {}
-#     source, weights, view_img, save_txt, imgsz, trace = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size, not opt.no_trace
-#     save_img = not opt.nosave and not source.endswith('.txt')  # save inference images
-#     webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
-#         ('rtsp://', 'rtmp://', 'http://', 'https://'))
-
-#     # Directories
-#     save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))  # increment run
-#     (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
-
-#     # Initialize
-#     set_logging()
-#     device = select_device(opt.device)
-#     half = device.type != 'cpu'  # half precision only supported on CUDA
-
-#     # Load model
-#     model = attempt_load(weights, map_location=device)  # load FP32 model
-#     stride = int(model.stride.max())  # model stride
-#     imgsz = check_img_size(imgsz, s=stride)  # check img_size
-
-#     if trace:
-#         model = TracedModel(model, device, opt.img_size)
-
-#     if half:
-#         model.half()  # to FP16
-
-#     # Second-stage classifier
-#     classify = False
-#     if classify:
-#         modelc = load_classifier(name='resnet101', n=2)  # initialize
-#         modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()
-
-#     # Set Dataloader
-#     vid_path, vid_writer = None, None
-#     if webcam:
-#         view_img = check_imshow()
-#         cudnn.benchmark = True  # set True to speed up constant image size inference
-#         dataset = LoadStreams(source, img_size=imgsz, stride=stride)
-#     else:
-#         dataset = LoadImages(source, img_size=imgsz, stride=stride)
-
-#     # Get names and colors
-#     names = model.module.names if hasattr(model, 'module') else model.names
-#     colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
-
-#     # Run inference
-#     if device.type != 'cpu':
-#         model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
-#     old_img_w = old_img_h = imgsz
-#     old_img_b = 1
-
-#     t0 = time.time()
-#     for path, img, im0s, vid_cap in dataset:
-#         img = torch.from_numpy(img).to(device)
-#         img = img.half() if half else img.float()  # uint8 to fp16/32
-#         img /= 255.0  # 0 - 255 to 0.0 - 1.0
-#         if img.ndimension() == 3:
-#             img = img.unsqueeze(0)
-
-#         # Warmup
-#         if device.type != 'cpu' and (old_img_b != img.shape[0] or old_img_h != img.shape[2] or old_img_w != img.shape[3]):
-#             old_img_b = img.shape[0]
-#             old_img_h = img.shape[2]
-#             old_img_w = img.shape[3]
-#             for i in range(3):
-#                 model(img, augment=opt.augment)[0]
-
-#         # Inference
-#         t1 = time_synchronized()
-#         with torch.no_grad():   # Calculating gradients would cause a GPU memory leak
-#             pred = model(img, augment=opt.augment)[0]
-#         t2 = time_synchronized()
-
-#         # Apply NMS
-#         pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
-#         t3 = time_synchronized()
-
-#         # Apply Classifier
-#         if classify:
-#             pred = apply_classifier(pred, modelc, img, im0s)
-
-#         # Process detections
-#         for i, det in enumerate(pred):  # detections per image
-#             if webcam:  # batch_size >= 1
-#                 p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
-#             else:
-#                 p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)
-
-#             p = Path(p)  # to Path
-#             save_path = str(save_dir / p.name)  # img.jpg
-#             txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # img.txt
-#             gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
-#             if len(det):
-#                 # Rescale boxes from img_size to im0 size
-#                 det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
-# #                 print(f"BOXES ---->>>> {det[:, :4]}")
-#                 bbox[f"{txt_path.split('/')[4]}"]=(det[:, :4]).numpy()
-
-#                # Print results
-#                 for c in det[:, -1].unique():
-#                     n = (det[:, -1] == c).sum()  # detections per class
-#                     s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string
-
-#                 # Write results
-#                 for *xyxy, conf, cls in reversed(det):
-#                     if save_txt:  # Write to file
-#                         xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
-#                         line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh)  # label format
-#                         with open(txt_path + '.txt', 'a') as f:
-#                             f.write(('%g ' * len(line)).rstrip() % line + '\n')
-
-#                     if save_img or view_img:  # Add bbox to image
-#                         label = f'{names[int(cls)]} {conf:.2f}'
-#                         plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=1)
-
-#             # Print time (inference + NMS)
-#             print(f'{s}Done. ({(1E3 * (t2 - t1)):.1f}ms) Inference, ({(1E3 * (t3 - t2)):.1f}ms) NMS')
-
-#             # Stream results
-#             # if view_img:
-#             #     cv2.imshow(str(p), im0)
-#             #     cv2.waitKey(1)  # 1 millisecond
-
-#             # Save results (image with detections)
-#             if save_img:
-#                 if dataset.mode == 'image':
-#                     # Image.fromarray(im0).show()
-#                     cv2.imwrite(save_path, im0) 
-#                     print(f" The image with the result is saved in: {save_path}")
-#                 # else:  # 'video' or 'stream'
-#                 #     if vid_path != save_path:  # new video
-#                 #         vid_path = save_path
-#                 #         if isinstance(vid_writer, cv2.VideoWriter):
-#                 #             vid_writer.release()  # release previous video writer
-#                 #         if vid_cap:  # video
-#                 #             fps = vid_cap.get(cv2.CAP_PROP_FPS)
-#                 #             w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-#                 #             h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-#                 #         else:  # stream
-#                 #             fps, w, h = 30, im0.shape[1], im0.shape[0]
-#                 #             save_path += '.mp4'
-#                 #         vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
-#                 #     vid_writer.write(im0)
-
-#     if save_txt or save_img:
-#         s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
-#         #print(f"Results saved to {save_dir}{s}")
-
-#     print(f'Done. ({time.time() - t0:.3f}s)')
-#     return bbox,save_path
-
-# class options:
-#     def __init__(self, weights, source, img_size=640, conf_thres=0.1, iou_thres=0.45, device='', 
-#                  view_img=False, save_txt=False, save_conf=False, nosave=False, classes=None, 
-#                  agnostic_nms=False, augment=False, update=False, project='runs/detect', name='exp', 
-#                  exist_ok=False, no_trace=False):
-#         self.weights=weights
-#         self.source=source
-#         self.img_size=img_size
-#         self.conf_thres=conf_thres
-#         self.iou_thres=iou_thres
-#         self.device=device
-#         self.view_img=view_img
-#         self.save_txt=save_txt
-#         self.save_conf=save_conf
-#         self.nosave=nosave
-#         self.classes=classes
-#         self.agnostic_nms=agnostic_nms
-#         self.augment=augment
-#         self.update=update
-#         self.project=project
-#         self.name=name
-#         self.exist_ok=exist_ok
-#         self.no_trace=no_trace
+    bbox = {}
+    source, weights, view_img, save_txt, imgsz, trace = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size, not opt.no_trace
+    save_img = not opt.nosave and not source.endswith('.txt')  # save inference images
+    webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
+        ('rtsp://', 'rtmp://', 'http://', 'https://'))
+
+    # Directories
+    save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))  # increment run
+    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
+
+    # Initialize
+    set_logging()
+    device = select_device(opt.device)
+    half = device.type != 'cpu'  # half precision only supported on CUDA
+
+    # Load model
+    model = attempt_load(weights, map_location=device)  # load FP32 model
+    stride = int(model.stride.max())  # model stride
+    imgsz = check_img_size(imgsz, s=stride)  # check img_size
+
+    if trace:
+        model = TracedModel(model, device, opt.img_size)
+
+    if half:
+        model.half()  # to FP16
+
+    # Second-stage classifier
+    classify = False
+    if classify:
+        modelc = load_classifier(name='resnet101', n=2)  # initialize
+        modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()
+
+    # Set Dataloader
+    vid_path, vid_writer = None, None
+    if webcam:
+        view_img = check_imshow()
+        cudnn.benchmark = True  # set True to speed up constant image size inference
+        dataset = LoadStreams(source, img_size=imgsz, stride=stride)
+    else:
+        dataset = LoadImages(source, img_size=imgsz, stride=stride)
+
+    # Get names and colors
+    names = model.module.names if hasattr(model, 'module') else model.names
+    colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
+
+    # Run inference
+    if device.type != 'cpu':
+        model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
+    old_img_w = old_img_h = imgsz
+    old_img_b = 1
+
+    t0 = time.time()
+    for path, img, im0s, vid_cap in dataset:
+        img = torch.from_numpy(img).to(device)
+        img = img.half() if half else img.float()  # uint8 to fp16/32
+        img /= 255.0  # 0 - 255 to 0.0 - 1.0
+        if img.ndimension() == 3:
+            img = img.unsqueeze(0)
+
+        # Warmup
+        if device.type != 'cpu' and (old_img_b != img.shape[0] or old_img_h != img.shape[2] or old_img_w != img.shape[3]):
+            old_img_b = img.shape[0]
+            old_img_h = img.shape[2]
+            old_img_w = img.shape[3]
+            for i in range(3):
+                model(img, augment=opt.augment)[0]
+
+        # Inference
+        t1 = time_synchronized()
+        with torch.no_grad():   # Calculating gradients would cause a GPU memory leak
+            pred = model(img, augment=opt.augment)[0]
+        t2 = time_synchronized()
+
+        # Apply NMS
+        pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
+        t3 = time_synchronized()
+
+        # Apply Classifier
+        if classify:
+            pred = apply_classifier(pred, modelc, img, im0s)
+
+        # Process detections
+        for i, det in enumerate(pred):  # detections per image
+            if webcam:  # batch_size >= 1
+                p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
+            else:
+                p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)
+
+            p = Path(p)  # to Path
+            save_path = str(save_dir / p.name)  # img.jpg
+            txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # img.txt
+            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
+            if len(det):
+                # Rescale boxes from img_size to im0 size
+                det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
+#                 print(f"BOXES ---->>>> {det[:, :4]}")
+                bbox[f"{txt_path.split('/')[4]}"]=(det[:, :4]).numpy()
+
+               # Print results
+                for c in det[:, -1].unique():
+                    n = (det[:, -1] == c).sum()  # detections per class
+                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string
+
+                # Write results
+                for *xyxy, conf, cls in reversed(det):
+                    if save_txt:  # Write to file
+                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
+                        line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh)  # label format
+                        with open(txt_path + '.txt', 'a') as f:
+                            f.write(('%g ' * len(line)).rstrip() % line + '\n')
+
+                    if save_img or view_img:  # Add bbox to image
+                        label = f'{names[int(cls)]} {conf:.2f}'
+                        plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=1)
+
+            # Print time (inference + NMS)
+            print(f'{s}Done. ({(1E3 * (t2 - t1)):.1f}ms) Inference, ({(1E3 * (t3 - t2)):.1f}ms) NMS')
+
+            # Stream results
+            # if view_img:
+            #     cv2.imshow(str(p), im0)
+            #     cv2.waitKey(1)  # 1 millisecond
+
+            # Save results (image with detections)
+            if save_img:
+                if dataset.mode == 'image':
+                    # Image.fromarray(im0).show()
+                    cv2.imwrite(save_path, im0) 
+                    print(f" The image with the result is saved in: {save_path}")
+                # else:  # 'video' or 'stream'
+                #     if vid_path != save_path:  # new video
+                #         vid_path = save_path
+                #         if isinstance(vid_writer, cv2.VideoWriter):
+                #             vid_writer.release()  # release previous video writer
+                #         if vid_cap:  # video
+                #             fps = vid_cap.get(cv2.CAP_PROP_FPS)
+                #             w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+                #             h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+                #         else:  # stream
+                #             fps, w, h = 30, im0.shape[1], im0.shape[0]
+                #             save_path += '.mp4'
+                #         vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
+                #     vid_writer.write(im0)
+
+    if save_txt or save_img:
+        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
+        #print(f"Results saved to {save_dir}{s}")
+
+    print(f'Done. ({time.time() - t0:.3f}s)')
+    return bbox,save_path
+
+class options:
+    def __init__(self, weights, source, img_size=640, conf_thres=0.1, iou_thres=0.45, device='', 
+                 view_img=False, save_txt=False, save_conf=False, nosave=False, classes=None, 
+                 agnostic_nms=False, augment=False, update=False, project='runs/detect', name='exp', 
+                 exist_ok=False, no_trace=False):
+        self.weights=weights
+        self.source=source
+        self.img_size=img_size
+        self.conf_thres=conf_thres
+        self.iou_thres=iou_thres
+        self.device=device
+        self.view_img=view_img
+        self.save_txt=save_txt
+        self.save_conf=save_conf
+        self.nosave=nosave
+        self.classes=classes
+        self.agnostic_nms=agnostic_nms
+        self.augment=augment
+        self.update=update
+        self.project=project
+        self.name=name
+        self.exist_ok=exist_ok
+        self.no_trace=no_trace
 
 def get_output(input_image):
-    # image.save(f"{BASE_DIR}/input/image.jpg")
-    # source = f"{BASE_DIR}/input"
-    # opt = options(weights='logo_detection.pt',source=source)
-    # bbox = None
-    # with torch.no_grad():
-    #     bbox,output_path = detect(opt)
-    # if os.path.exists(output_path):
-    #     return np.array(Image.open(output_path))
-    # else:
+    image.save(f"{BASE_DIR}/input/image.jpg")
+    source = f"{BASE_DIR}/input"
+    opt = options(weights='logo_detection.pt',source=source)
+    bbox = None
+    with torch.no_grad():
+        bbox,output_path = detect(opt)
+    if os.path.exists(output_path):
+        return np.array(Image.open(output_path))
+    else:
     return input_image