Update app.py

app.py

@@ -1,146 +1,124 @@
-from fastai.vision.all import *
-from io import BytesIO
-import requests
-import streamlit as st
+import os
+import platform
+import subprocess
+import time
+from pathlib import Path
 
-import numpy as np
+import requests
 import torch
-import time
-import cv2
-from numpy import random
-from models.experimental import attempt_load
-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
-
-def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):
-    # Resize and pad image while meeting stride-multiple constraints
-    shape = img.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 test mAP)
-        r = min(r, 1.0)
-
-    # Compute padding
-    ratio = r, r  # width, height ratios
-    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
-    elif scaleFill:  # stretch
-        dw, dh = 0.0, 0.0
-        new_unpad = (new_shape[1], new_shape[0])
-        ratio = new_shape[1] / shape[1], new_shape[0] / shape[0]  # width, height ratios
-
-    dw /= 2  # divide padding into 2 sides
-    dh /= 2
-
-    if shape[::-1] != new_unpad:  # resize
-        img = cv2.resize(img, 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))
-    img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
-    return img, ratio, (dw, dh)
-
-def detect_modify(img0, model, conf=0.4, imgsz=640, conf_thres = 0.25, iou_thres=0.45):
-    st.image(img0, caption="Your image", use_column_width=True)
-
-    stride = int(model.stride.max())  # model stride
-    imgsz = check_img_size(imgsz, s=stride)  # check img_size
-
-    # Padded resize
-    img0 = cv2.cvtColor(np.asarray(img0), cv2.COLOR_RGB2BGR)
-    img = letterbox(img0, imgsz, stride=stride)[0]
-    # Convert
-    img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
-    img = np.ascontiguousarray(img)
-
-    
-    # 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
-    old_img_w = old_img_h = imgsz
-    old_img_b = 1
-
-    t0 = time.time()
-    img = torch.from_numpy(img).to(device)
-    # img /= 255.0  # 0 - 255 to 0.0 - 1.0
-    img = img/255.0
-    if img.ndimension() == 3:
-        img = img.unsqueeze(0)
-
-    # Inference
-    # t1 = time_synchronized()
-    with torch.no_grad():   # Calculating gradients would cause a GPU memory leak
-        pred = model(img)[0]
-    # t2 = time_synchronized()
-
-    # Apply NMS
-    pred = non_max_suppression(pred, conf_thres, iou_thres)
-    # t3 = time_synchronized()
-
-    # Process detections
-    # for i, det in enumerate(pred):  # detections per image
-        
-    gn = torch.tensor(img0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
-
-    det = pred[0]
-    if len(det):
-        # Rescale boxes from img_size to im0 size
-        det[:, :4] = scale_coords(img.shape[2:], det[:, :4], img0.shape).round()
-
-        # Print results
-        s = ''
-        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):
-            label = f'{names[int(cls)]} {conf:.2f}'
-            plot_one_box(xyxy, img0, label=label, color=colors[int(cls)], line_thickness=1)
-
-    f"""
-    ### Prediction result:
-    """
-    img0 = cv2.cvtColor(np.asarray(img0), cv2.COLOR_BGR2RGB)
-    st.image(img0, caption="Prediction Result", use_column_width=True)
-
-#set paramters
-weight_path = './best,pt'
-imgsz = 640
-conf = 0.4
-conf_thres = 0.25
-iou_thres=0.45
-device = torch.device("cpu")
-path = "./"
-
-# Load model
-model = attempt_load(weight_path, map_location=torch.device('cpu'))  # load FP32 model
-
-"""
-# YOLOv7
-This is a object detection model for [Objects].
-"""
-option = st.radio("", ["Upload Image", "Image URL"])
-
-if option == "Upload Image":
-    uploaded_file = st.file_uploader("Please upload an image.")
-
-    if uploaded_file is not None:
-        img = PILImage.create(uploaded_file)
-        detect_modify(img, model, conf=conf, imgsz=imgsz, conf_thres=conf_thres, iou_thres=iou_thres)
-else:
-    url = st.text_input("Please input a url.")
-    if url != "":
+
+
+def gsutil_getsize(url=''):
+    # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du
+    s = subprocess.check_output(f'gsutil du {url}', shell=True).decode('utf-8')
+    return eval(s.split(' ')[0]) if len(s) else 0  # bytes
+
+
+def attempt_download(file, repo='WongKinYiu/yolov7'):
+    # Attempt file download if does not exist
+    file = Path(str(file).strip().replace("'", '').lower())
+
+    if not file.exists():
         try:
-            response = requests.get(url)
-            pil_img = PILImage.create(BytesIO(response.content))
-            detect_modify(pil_img, model, conf=conf, imgsz=imgsz, conf_thres=conf_thres, iou_thres=iou_thres)
-        except:
-            st.text("Problem reading image from", url)
+            response = requests.get(f'https://api.github.com/repos/{repo}/releases/latest').json()  # github api
+            assets = [x['name'] for x in response['assets']]  # release assets
+            tag = response['tag_name']  # i.e. 'v1.0'
+        except:  # fallback plan
+            assets = ['yolov7.pt', 'yolov7-tiny.pt', 'yolov7x.pt', 'yolov7-d6.pt', 'yolov7-e6.pt', 
+                      'yolov7-e6e.pt', 'yolov7-w6.pt']
+            try:
+                tag = subprocess.check_output('git tag', shell=True).decode().split()[-1]
+            except IndexError:
+                tag = 'default'
+
+        name = file.name
+        if name in assets:
+            msg = f'{file} missing, try downloading from https://github.com/{repo}/releases/'
+            redundant = False  # second download option
+            try:  # GitHub
+                url = f'https://github.com/{repo}/releases/download/{tag}/{name}'
+                print(f'Downloading {url} to {file}...')
+                torch.hub.download_url_to_file(url, file)
+                assert file.exists() and file.stat().st_size > 1E6  # check
+            except Exception as e:  # GCP
+                print(f'Download error: {e}')
+                assert redundant, 'No secondary mirror'
+                url = f'https://storage.googleapis.com/{repo}/ckpt/{name}'
+                print(f'Downloading {url} to {file}...')
+                os.system(f'curl -L {url} -o {file}')  # torch.hub.download_url_to_file(url, weights)
+            finally:
+                if not file.exists() or file.stat().st_size < 1E6:  # check
+                    file.unlink(missing_ok=True)  # remove partial downloads
+                    print(f'ERROR: Download failure: {msg}')
+                print('')
+                return
+
+
+def gdrive_download(id='', file='tmp.zip'):
+    # Downloads a file from Google Drive. from yolov7.utils.google_utils import *; gdrive_download()
+    t = time.time()
+    file = Path(file)
+    cookie = Path('cookie')  # gdrive cookie
+    print(f'Downloading https://drive.google.com/uc?export=download&id={id} as {file}... ', end='')
+    file.unlink(missing_ok=True)  # remove existing file
+    cookie.unlink(missing_ok=True)  # remove existing cookie
+
+    # Attempt file download
+    out = "NUL" if platform.system() == "Windows" else "/dev/null"
+    os.system(f'curl -c ./cookie -s -L "drive.google.com/uc?export=download&id={id}" > {out}')
+    if os.path.exists('cookie'):  # large file
+        s = f'curl -Lb ./cookie "drive.google.com/uc?export=download&confirm={get_token()}&id={id}" -o {file}'
+    else:  # small file
+        s = f'curl -s -L -o {file} "drive.google.com/uc?export=download&id={id}"'
+    r = os.system(s)  # execute, capture return
+    cookie.unlink(missing_ok=True)  # remove existing cookie
+
+    # Error check
+    if r != 0:
+        file.unlink(missing_ok=True)  # remove partial
+        print('Download error ')  # raise Exception('Download error')
+        return r
+
+    # Unzip if archive
+    if file.suffix == '.zip':
+        print('unzipping... ', end='')
+        os.system(f'unzip -q {file}')  # unzip
+        file.unlink()  # remove zip to free space
+
+    print(f'Done ({time.time() - t:.1f}s)')
+    return r
+
+
+def get_token(cookie="./cookie"):
+    with open(cookie) as f:
+        for line in f:
+            if "download" in line:
+                return line.split()[-1]
+    return ""
+
+# def upload_blob(bucket_name, source_file_name, destination_blob_name):
+#     # Uploads a file to a bucket
+#     # https://cloud.google.com/storage/docs/uploading-objects#storage-upload-object-python
+#
+#     storage_client = storage.Client()
+#     bucket = storage_client.get_bucket(bucket_name)
+#     blob = bucket.blob(destination_blob_name)
+#
+#     blob.upload_from_filename(source_file_name)
+#
+#     print('File {} uploaded to {}.'.format(
+#         source_file_name,
+#         destination_blob_name))
+#
+#
+# def download_blob(bucket_name, source_blob_name, destination_file_name):
+#     # Uploads a blob from a bucket
+#     storage_client = storage.Client()
+#     bucket = storage_client.get_bucket(bucket_name)
+#     blob = bucket.blob(source_blob_name)
+#
+#     blob.download_to_filename(destination_file_name)
+#
+#     print('Blob {} downloaded to {}.'.format(
+#         source_blob_name,
+#         destination_file_name))