From 8446139d79c366fc2c44d72c2ddb963817a8ce7d Mon Sep 17 00:00:00 2001
From: bsw215583320 <baoshiwei121@163.com>
Date: 星期四, 16 一月 2025 14:38:34 +0800
Subject: [PATCH] 优化上料识别与药材识别
---
model/loading.onnx | 0
test_herb_identify.py | 32 +++
herb_identify.py | 269 +++++++++++++++++++++++++++++
cam_detect.py | 15
model/herb_identify.onnx | 0
camera_onnx.py | 60 +++---
laplacian.py | 124 +++++++++++++
7 files changed, 466 insertions(+), 34 deletions(-)
diff --git a/cam_detect.py b/cam_detect.py
index af30f97..922816f 100644
--- a/cam_detect.py
+++ b/cam_detect.py
@@ -1,6 +1,6 @@
+# 鎽勫儚澶寸敾闈㈡祴璇曠▼搴�
import os
-
import cv2
import time
import numpy as np
@@ -8,14 +8,14 @@
import win32com.client
if __name__ == '__main__':
-# 鎽勫儚澶寸储寮曞彿锛岄�氬父涓�0琛ㄧず绗竴涓憚鍍忓ご
- camera_index = 1
+ # 鎽勫儚澶寸储寮曞彿锛岄�氬父涓�0琛ㄧず绗竴涓憚鍍忓ご
+ camera_index = 3
# 鎵撳紑鎽勫儚澶�
cap = cv2.VideoCapture(camera_index, cv2.CAP_DSHOW)
# 璁剧疆鍒嗚鲸鐜�
- cap.set(cv2.CAP_PROP_FRAME_WIDTH, 3800) # 瀹藉害
- cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 2100) # 楂樺害
+ cap.set(cv2.CAP_PROP_FRAME_WIDTH, 2048) # 瀹藉害
+ cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 1540) # 楂樺害
# 妫�鏌ユ憚鍍忓ご鏄惁鎴愬姛鎵撳紑
if not cap.isOpened():
print("鏃犳硶鎵撳紑鎽勫儚澶�")
@@ -62,8 +62,9 @@
# 灏咶PS缁樺埗鍦ㄥ浘鍍忎笂
cv2.putText(resized_frame, f"FPS: {fps:.2f}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2,
cv2.LINE_AA)
+ resizeframe = cv2.resize(frame, (target_width, target_height))
# 鏄剧ず鐢婚潰
- cv2.imshow("Camera", resized_frame)
+ cv2.imshow("Camera", resizeframe)
# 妫�娴嬫寜閿紝濡傛灉鎸変笅q閿垯閫�鍑哄惊鐜�
if cv2.waitKey(1) & 0xFF == ord('q'):
@@ -73,4 +74,4 @@
cap.release()
# 鍏抽棴鎵�鏈夌獥鍙�
- cv2.destroyAllWindows()
\ No newline at end of file
+ cv2.destroyAllWindows()
diff --git a/camera_onnx.py b/camera_onnx.py
index cba18f3..5db1439 100644
--- a/camera_onnx.py
+++ b/camera_onnx.py
@@ -1,5 +1,7 @@
-import os
+# 涓婃枡璇嗗埆锛岃幏鍙栦笂鏂欏浘鐗囷紝淇濆瓨鍒癲ata/images鏂囦欢澶逛笅
+
+import os
import cv2
import time
import numpy as np
@@ -17,26 +19,26 @@
for device in webcams:
name = getattr(device, 'Name', None)
pnp_class = getattr(device, 'PNPClass', None)
- if name is not None and 'PC Camera' in name:
+ if name is not None and cam1 in name:
# 灏嗚澶囧悕瀛楀拰绱㈠紩娣诲姞鍒板瓧鍏镐腑
- webcam_dict['PC Camera'] = index
+ webcam_dict[cam1] = index
index += 1
- elif name is not None and "USB Camera" in name:
+ elif name is not None and cam2 in name:
# 灏嗚澶囧悕瀛楀拰绱㈠紩娣诲姞鍒板瓧鍏镐腑
- webcam_dict['USB Camera'] = index
+ webcam_dict[cam2] = index
index += 1
return webcam_dict
# 璋冪敤鍙︿竴涓暱鐒﹂暅澶达紝鎷嶆憚娓呮櫚鐨勫眬閮ㄨ嵂鏉愬浘鐗�
def get_image():
- camera2_index = webcams.get('PC Camera')
+ camera2_index = webcams.get(cam2)
print("绗簩涓憚鍍忓ご绱㈠紩锛�" + str(camera2_index))
# 鎵撳紑鎽勫儚澶�
capture = cv2.VideoCapture(camera2_index, cv2.CAP_DSHOW)
# 璁剧疆鍒嗚鲸鐜�
- capture.set(cv2.CAP_PROP_FRAME_WIDTH, 3800) # 瀹藉害
- capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 2150) # 楂樺害
+ capture.set(cv2.CAP_PROP_FRAME_WIDTH, 1920) # 瀹藉害
+ capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 1080) # 楂樺害
# 妫�鏌ユ憚鍍忓ご鏄惁鎴愬姛鎵撳紑
if not capture.isOpened():
print("鏃犳硶鎵撳紑鎽勫儚澶�2")
@@ -46,45 +48,49 @@
print("鎽勫儚澶�2鍒嗚鲸鐜�:", width2, "x", height2)
# 寰幆璇诲彇鎽勫儚澶寸敾闈�
# Shadows name 'width' from outer scope
+ count = 0
while True:
ret2, frame2 = capture.read()
if not ret2:
print("鏃犳硶璇诲彇鎽勫儚澶寸敾闈�")
break
- # 鏄剧ず鐢婚潰
- # cv2.imshow('Output2', frame2)
+ count += 1
# 1920*1080鐨勫浘鍍忥紝涓績瑁佸壀640*480鐨勫尯鍩�
- a2 = int(height2 / 2 - target_height / 2)
- b2 = int(height2 / 2 + target_height / 2)
- c2 = int(width2 / 2 - target_width / 2)
- d2 = int(width2 / 2 + target_width / 2)
- cropped_frame2 = frame2[a2:b2, c2:d2]
- # 璋冩暣鍥惧儚灏哄
- resized_frame2 = cv2.resize(cropped_frame2, (target_width, target_height))
- # 鐢熸垚淇濆瓨鏂囦欢鍚嶏紝浠ュ綋鍓嶆椂闂村懡鍚�
- save_name = time.strftime("%Y%m%d%H%M%S", time.localtime()) + ".jpg"
- # 淇濆瓨璋冩暣灏哄鍚庣殑鍥剧墖
- cv2.imwrite(save_path + "2/" + save_name, resized_frame2)
- # cv2.imshow("Camera", resized_frame2)
- print("淇濆瓨鍥剧墖:", save_name)
- break
+ # a2 = int(height2 / 2 - target_height / 2)
+ # b2 = int(height2 / 2 + target_height / 2)
+ # c2 = int(width2 / 2 - target_width / 2)
+ # d2 = int(width2 / 2 + target_width / 2)
+ # cropped_frame2 = frame2[a2:b2, c2:d2]
+ if count == 2:
+ # 璋冩暣鍥惧儚灏哄
+ resized_frame2 = cv2.resize(frame2, (target_width, target_height))
+ # 鏄剧ず鐢婚潰
+ # cv2.imshow('Output2', resized_frame2)
+ # 鐢熸垚淇濆瓨鏂囦欢鍚嶏紝浠ュ綋鍓嶆椂闂村懡鍚�
+ save_name2 = time.strftime("%Y%m%d%H%M%S", time.localtime()) + ".jpg"
+ # 淇濆瓨璋冩暣灏哄鍚庣殑鍥剧墖
+ cv2.imwrite(save_path + "2/" + save_name2, resized_frame2)
+ # cv2.imshow("Camera", resized_frame2)
+ print("淇濆瓨鍥剧墖:", save_name2)
+ break
# 缁撴潫绾跨▼
capture.release()
- return frame2
if __name__ == '__main__':
+ cam1 = "USB Camera"
+ cam2 = "PC Camera"
webcams = list_webcams()
print(webcams)
target_width = 1024
target_height = 768
save_path = "data/images/"
# 鍔犺浇ONNX妯″瀷
- session = onnxruntime.InferenceSession("model/best.onnx")
+ session = onnxruntime.InferenceSession("model/loading.onnx")
# 鎽勫儚澶寸储寮曞彿锛岄�氬父涓�0琛ㄧず绗竴涓憚鍍忓ご
- camera_index = webcams.get('USB Camera')
+ camera_index = webcams.get(cam1)
print("绗竴涓憚鍍忓ご绱㈠紩锛�" + str(camera_index))
modelmeta = session.get_modelmeta()
metadata_map = modelmeta.custom_metadata_map
diff --git a/herb_identify.py b/herb_identify.py
new file mode 100644
index 0000000..cf049dd
--- /dev/null
+++ b/herb_identify.py
@@ -0,0 +1,269 @@
+# 涓婃枡璇嗗埆锛岃幏鍙栦笂鏂欏浘鐗囷紝璋冪敤鑽潗璇嗗埆妯″瀷锛岃緭鍑鸿瘑鍒粨鏋�
+
+
+import os
+import cv2
+import time
+import numpy as np
+import onnxruntime
+import win32com.client
+
+
+
+# 璁＄畻鎷夋櫘鎷夋柉鍝嶅簲鐨勬柟宸�,鍒ゆ柇鍥惧儚鐨勬竻鏅板害
+def variance_of_laplacian(image):
+ # 璁＄畻杈撳叆鍥惧儚鐨勬媺鏅媺鏂搷搴旂殑鏂瑰樊
+ return cv2.Laplacian(image, cv2.CV_64F).var()
+# 鑾峰彇鎽勫儚澶村垪琛�
+def list_webcams():
+ # 鍒涘缓涓�涓猈MI瀹㈡埛绔疄渚�
+ wmi = win32com.client.GetObject("winmgmts:")
+ webcams = wmi.InstancesOf("Win32_PnPEntity")
+ # 鍒涘缓鎽勫儚澶村拰绱㈠紩瀛楀吀
+ webcam_dict = {}
+ index = 0
+ for device in webcams:
+ name = getattr(device, 'Name', None)
+ pnp_class = getattr(device, 'PNPClass', None)
+ if name is not None and cam1 in name:
+ # 灏嗚澶囧悕瀛楀拰绱㈠紩娣诲姞鍒板瓧鍏镐腑
+ webcam_dict[cam1] = index
+ index += 1
+ elif name is not None and cam2 in name:
+ # 灏嗚澶囧悕瀛楀拰绱㈠紩娣诲姞鍒板瓧鍏镐腑
+ webcam_dict[cam2] = index
+ index += 1
+ return webcam_dict
+
+
+# 璋冪敤鍙︿竴涓暱鐒﹂暅澶达紝鎷嶆憚娓呮櫚鐨勫眬閮ㄨ嵂鏉愬浘鐗�
+def get_image():
+ camera2_index = webcams.get(cam2)
+ print("绗簩涓憚鍍忓ご绱㈠紩锛�" + str(camera2_index))
+ # 鎵撳紑鎽勫儚澶�
+ capture = cv2.VideoCapture(camera2_index, cv2.CAP_DSHOW)
+ # 璁剧疆鍒嗚鲸鐜�
+ capture.set(cv2.CAP_PROP_FRAME_WIDTH, 2048) # 瀹藉害
+ capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 1540) # 楂樺害
+ # 妫�鏌ユ憚鍍忓ご鏄惁鎴愬姛鎵撳紑
+ if not capture.isOpened():
+ print("鏃犳硶鎵撳紑鎽勫儚澶�2")
+ exit()
+ width2 = capture.get(cv2.CAP_PROP_FRAME_WIDTH)
+ height2 = capture.get(cv2.CAP_PROP_FRAME_HEIGHT)
+ print("鎽勫儚澶�2鍒嗚鲸鐜�:", width2, "x", height2)
+ # 寰幆璇诲彇鎽勫儚澶寸敾闈�
+ # Shadows name 'width' from outer scope
+ count = 0
+ while True:
+ ret2, frame2 = capture.read()
+ if not ret2:
+ print("鏃犳硶璇诲彇鎽勫儚澶寸敾闈�")
+ break
+
+ count += 1
+ # 1920*1080鐨勫浘鍍忥紝涓績瑁佸壀640*480鐨勫尯鍩�
+ # a2 = int(height2 / 2 - target_height / 2)
+ # b2 = int(height2 / 2 + target_height / 2)
+ # c2 = int(width2 / 2 - target_width / 2)
+ # d2 = int(width2 / 2 + target_width / 2)
+ # cropped_frame2 = frame2[a2:b2, c2:d2]
+ if count == 2:
+ # 璋冩暣鍥惧儚灏哄
+ resized_frame2 = cv2.resize(frame2, (target_width, target_height))
+
+ # 棰勫鐞�
+ herb_blob = cv2.dnn.blobFromImage(resized_frame2, 1 / 255.0, (640, 640), swapRB=True, crop=False)
+
+ # 妯″瀷鎺ㄧ悊
+ herb_outputs = identify_session.run(None, {identify_session.get_inputs()[0].name: herb_blob})
+
+ herb_probabilities = herb_outputs[0]
+
+ top_five_classes = np.argsort(herb_probabilities, axis=1)[0][-5:][::-1]
+ name = ""
+ for i, class_id in enumerate(top_five_classes):
+ # 淇濈暀涓や綅灏忔暟
+ probability = round(herb_probabilities[0][class_id], 2)
+
+ herb_class = herbs[class_id]
+ name = name + herb_class + "=" + str(probability)
+ # 鏄剧ず鐢婚潰
+ # cv2.imshow('Output2', resized_frame2)
+
+ # 璁＄畻鎷夋櫘鎷夋柉鍝嶅簲鐨勬柟宸�
+ laplacian = variance_of_laplacian(resized_frame2)
+ # 鐢熸垚淇濆瓨鏂囦欢鍚嶏紝浠ュ綋鍓嶆椂闂村懡鍚�
+ save_name2 = name +"_["+ str(round(laplacian, 2)) +"]_"+ time.strftime("%Y%m%d%H%M%S", time.localtime()) + ".jpg"
+ # 鍒ゆ柇鍥惧儚鐨勬竻鏅板害
+ # 淇濆瓨璋冩暣灏哄鍚庣殑鍥剧墖
+ if laplacian > 1500:
+ c_ = save_path + "2/c/"
+ # 鍒ゆ柇鏂囦欢鏄惁瀛樺湪锛屼笉瀛樺湪鍒欏垱寤�
+ if not os.path.exists(c_):
+ os.makedirs(c_)
+ cv2.imwrite(c_ + save_name2, resized_frame2)
+ else:
+ n_ = save_path + "2/n/"
+ # 鍒ゆ柇鏂囦欢鏄惁瀛樺湪锛屼笉瀛樺湪鍒欏垱寤�
+ if not os.path.exists(n_):
+ os.makedirs(n_)
+ cv2.imwrite(n_ + save_name2, resized_frame2)
+ # cv2.imshow("Camera", resized_frame2)
+ print("淇濆瓨鍥剧墖:", save_name2)
+ break
+ # 缁撴潫绾跨▼
+ capture.release()
+
+
+if __name__ == '__main__':
+
+ cam1 = "USB Camera"
+ cam2 = "PC Camera"
+ webcams = list_webcams()
+ print(webcams)
+ target_width = 1024
+ target_height = 768
+ save_path = "data/images/"
+ # 鍔犺浇ONNX妯″瀷
+ session = onnxruntime.InferenceSession("model/loading.onnx")
+ identify_session = onnxruntime.InferenceSession("model/herb_identify.onnx")
+ herbs = eval(identify_session.get_modelmeta().custom_metadata_map['names'])
+ # 鎽勫儚澶寸储寮曞彿锛岄�氬父涓�0琛ㄧず绗竴涓憚鍍忓ご
+ camera_index = webcams.get(cam1)
+ print("绗竴涓憚鍍忓ご绱㈠紩锛�" + str(camera_index))
+ modelmeta = session.get_modelmeta()
+ metadata_map = modelmeta.custom_metadata_map
+ classes = eval(metadata_map['names'])
+ # 鎵撳紑鎽勫儚澶�
+ cap = cv2.VideoCapture(camera_index, cv2.CAP_DSHOW)
+ # 璁剧疆鍒嗚鲸鐜�
+ # cap.set(cv2.CAP_PROP_FRAME_WIDTH, 3840) # 瀹藉害
+ # cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 2160) # 楂樺害
+ # 妫�鏌ユ憚鍍忓ご鏄惁鎴愬姛鎵撳紑
+ if not cap.isOpened():
+ print("鏃犳硶鎵撳紑鎽勫儚澶�")
+ exit()
+
+ width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
+ height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
+ print("鎽勫儚澶村垎杈ㄧ巼:", width, "x", height)
+
+ # 鐩爣鍥惧儚灏哄
+
+ # 璁℃椂鍣�
+ frame_count = 0
+ start_time = time.time()
+ stime = time.time()
+
+ if not os.path.exists(save_path):
+ os.makedirs(save_path)
+ class_old = "1"
+ count = 0
+ status = "娌℃湁涓婃枡"
+ # 寰幆璇诲彇鎽勫儚澶寸敾闈�
+ while True:
+ # 鐫＄湢100姣
+ time.sleep(0.1)
+
+ ret, frame = cap.read()
+
+ if not ret:
+ print("鏃犳硶璇诲彇鎽勫儚澶寸敾闈�")
+ break
+
+ resized_frame = frame
+ if height > target_height and width > target_width:
+ # 1920*1080鐨勫浘鍍忥紝涓績瑁佸壀640*480鐨勫尯鍩�
+ a = int(height / 2 - target_height / 2)
+ b = int(height / 2 + target_height / 2)
+ c = int(width / 2 - target_width / 2)
+ d = int(width / 2 + target_width / 2)
+ print(a, b, c, d)
+ cropped_frame = frame[a:b, c:d]
+ # 璋冩暣鍥惧儚灏哄
+ resized_frame = cv2.resize(cropped_frame, (target_width, target_height))
+
+ # 鑾峰彇褰撳墠鏃堕棿
+ current_time = time.time()
+
+ # 濡傛灉璺濈涓婁竴娆′繚瀛樺凡缁忚繃鍘�1绉掞紝鍒欎繚瀛樺綋鍓嶇敾闈�
+ # if current_time - start_time >= 3.0:
+ # # 鐢熸垚淇濆瓨鏂囦欢鍚嶏紝浠ュ綋鍓嶆椂闂村懡鍚�
+ # save_name = time.strftime("%Y%m%d%H%M%S", time.localtime()) + ".jpg"
+ # # 淇濆瓨璋冩暣灏哄鍚庣殑鍥剧墖
+ # cv2.imwrite(save_path + save_name, frame)
+ # print("淇濆瓨鍥剧墖:", save_name)
+ # # 閲嶇疆璁℃椂鍣�
+ # start_time = time.time()
+
+ # 棰勫鐞�
+ blob = cv2.dnn.blobFromImage(resized_frame, 1 / 255.0, (640, 640), swapRB=True, crop=False)
+
+ # 妯″瀷鎺ㄧ悊
+ outputs = session.run(None, {session.get_inputs()[0].name: blob})
+ #
+ # print(outputs)
+ # 搴旂敤softmax鍑芥暟
+ probabilities = outputs[0]
+
+ # 鎵惧埌鏈�澶ф鐜囩殑绫诲埆
+ predicted_class = np.argmax(probabilities, axis=1)[0]
+ max_probability = np.max(probabilities, axis=1)[0]
+ class_ = classes[predicted_class]
+
+ # 璁＄畻绫诲瀷閲嶅鐨勬鏁帮紝绫诲埆鏇存崲涔嬪悗閲嶆柊璁℃暟
+ if class_ != class_old:
+ count = 0
+ else:
+ count += 1
+ class_old = class_
+ print(f"{class_}:{count}: {max_probability}")
+ # 鍒ゆ柇鏄惁涓婃枡骞朵笖涓婃枡娆℃暟澶т簬10娆�
+ if class_ == "shangliao" and count > 10:
+ status = "姝ｅ湪涓婃枡"
+ # 姣忛殧3绉掑彇涓�甯у浘鍍�
+ # 濡傛灉璺濈涓婁竴娆′繚瀛樺凡缁忚繃鍘�1绉掞紝鍒欎繚瀛樺綋鍓嶇敾闈�
+ if current_time - stime >= 10.0:
+ save_name = time.strftime("%Y%m%d%H%M%S", time.localtime()) + ".jpg"
+ # 淇濆瓨璋冩暣灏哄鍚庣殑鍥剧墖
+ path_ = save_path + "1/"
+ if not os.path.exists(path_):
+ os.makedirs(path_)
+ cv2.imwrite(path_ + save_name, resized_frame)
+ # 閲嶇疆璁℃椂鍣�
+ stime = time.time()
+ get_image()
+ else:
+ status = "娌℃湁涓婃枡"
+
+ print(status)
+ # # 鎵惧埌姒傜巼杈冮珮鐨勫墠鍗佷釜绫诲埆
+ # top_ten_classes = np.argsort(probabilities, axis=1)[0][-1:]
+ #
+ # # 杈撳嚭鍓嶅崄涓被鍒�
+ # print("Top 5 Classes:")
+ # for i in top_ten_classes:
+ # print(f"{classes[i]}: {probabilities[0][i]}")
+
+ # 璁＄畻甯ч�熺巼
+ frame_count += 1
+ end_time = time.time()
+ elapsed_time = end_time - start_time
+ fps = frame_count / elapsed_time
+ # print(f"FPS: {fps:.2f}")
+ # 灏咶PS缁樺埗鍦ㄥ浘鍍忎笂
+ cv2.putText(resized_frame, f"FPS: {fps:.2f}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2,
+ cv2.LINE_AA)
+ # 鏄剧ず鐢婚潰
+ cv2.imshow("Camera", resized_frame)
+
+ # 妫�娴嬫寜閿紝濡傛灉鎸変笅q閿垯閫�鍑哄惊鐜�
+ if cv2.waitKey(1) & 0xFF == ord('q'):
+ break
+
+ # 鍏抽棴鎽勫儚澶�
+ cap.release()
+
+ # 鍏抽棴鎵�鏈夌獥鍙�
+ cv2.destroyAllWindows()
diff --git a/laplacian.py b/laplacian.py
new file mode 100644
index 0000000..1555d1e
--- /dev/null
+++ b/laplacian.py
@@ -0,0 +1,124 @@
+import os
+
+import cv2
+from scipy.fftpack import fftshift, fft2
+import numpy as np
+
+
+def variance_of_laplacian(image):
+ # 璁＄畻杈撳叆鍥惧儚鐨勬媺鏅媺鏂搷搴旂殑鏂瑰樊
+ return cv2.Laplacian(image, cv2.CV_64F).var()
+
+def frequency_domain_analysis(gray):
+
+ # 璁＄畻鍌呴噷鍙跺彉鎹㈠苟绉诲姩鍒颁腑蹇�
+ f_transform = fftshift(fft2(gray))
+ # 璁＄畻棰戣氨骞呭害
+ magnitude_spectrum = 20 * np.log(np.abs(f_transform))
+ # 璁＄畻楂橀鎴愬垎鐨勬瘮渚�
+ high_frequency_magnitude = np.sum(magnitude_spectrum > np.mean(magnitude_spectrum))
+ total_magnitude = magnitude_spectrum.size
+ ratio = high_frequency_magnitude / total_magnitude
+ return ratio
+def edge_gradient(gray):
+
+ sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=3)
+ sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=3)
+ magnitude = np.sqrt(sobelx**2 + sobely**2)
+ return np.mean(magnitude)
+
+def testOne():
+ # 鍔犺浇鍥惧儚骞惰浆鎹负鐏板害鍥�
+ image = cv2.imread('H:/images/1/20250114193113.jpg')
+ gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+ # 璁＄畻鍥惧儚鐨勬竻鏅板害鍒嗘暟
+ score = variance_of_laplacian(gray)
+ ratio = frequency_domain_analysis(gray)
+ edge_gradient_score = edge_gradient(gray)
+
+ print("Image clarity score: ", score)
+ print("High frequency ratio: ", ratio)
+ print("Edge gradient score: ", edge_gradient_score)
+
+ # 鏍规嵁缁忛獙璁剧疆闃堝�兼潵鍒ゆ柇鍥惧儚鏄惁妯＄硦
+ threshold = 100 # 杩欎釜闃堝�煎彲浠ユ牴鎹疄闄呮儏鍐佃皟鏁�
+ if score < threshold:
+ print("The image is considered blurry.")
+ else:
+ print("The image is considered clear.")
+
+
+def batch_score():
+ global name, join
+ # 璇诲彇鏂囦欢澶逛笅鐨勬墍鏈夊浘鍍忓垎鍒绠楀垎鏁�
+ folder_path = 'H:/images/2'
+ for filename in os.listdir(folder_path):
+ if filename.endswith('.jpg') or filename.endswith('.png'):
+ image_path = os.path.join(folder_path, filename)
+ image = cv2.imread(image_path)
+ gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+ score = variance_of_laplacian(gray)
+ ratio = frequency_domain_analysis(gray)
+ edge_gradient_score = edge_gradient(gray)
+ # 鎵�鏈夌粨鏋滀繚鐣欎袱浣嶅皬鏁�
+ score = round(score, 2)
+ ratio = round(ratio, 2)
+ edge_gradient_score = round(edge_gradient_score, 2)
+ name = "score=" + str(score) + "ratio=" + str(ratio) + "edge_gradient_score=" + str(
+ edge_gradient_score) + ".jpg"
+ path_ = folder_path + "/22/"
+ # 鍒ゆ柇鏂囦欢澶规槸鍚﹀瓨鍦紝涓嶅瓨鍦ㄥ垯鏂板缓
+ if not os.path.exists(path_):
+ os.makedirs(path_)
+ join = path_ + name
+ print(join)
+ cv2.imwrite(join, image)
+
+
+def cam_score():
+ # 璋冪敤鎽勫儚澶磋绠楅噰闆嗗埌鍥惧儚鐨勫緱鍒�
+ camera_index = 2
+ print("绗簩涓憚鍍忓ご绱㈠紩锛�" + str(camera_index))
+ # 鎵撳紑鎽勫儚澶�
+ capture = cv2.VideoCapture(camera_index, cv2.CAP_DSHOW)
+ # 璁剧疆鍒嗚鲸鐜�
+ capture.set(cv2.CAP_PROP_FRAME_WIDTH, 1921)
+ capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 1081)
+ # 妫�鏌ユ憚鍍忓ご鏄惁鎴愬姛鎵撳紑
+ if not capture.isOpened():
+ print("鏃犳硶鎵撳紑鎽勫儚澶�2")
+ exit()
+ # 寰幆璇诲彇鐢婚潰
+ while True:
+ ret, frame = capture.read()
+ if not ret:
+ print("鏃犳硶璇诲彇鎽勫儚澶寸敾闈�")
+ break
+ gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+ score = variance_of_laplacian(gray)
+ # ratio = frequency_domain_analysis(gray)
+ # edge_gradient_score = edge_gradient(gray)
+ # 灏咶PS缁樺埗鍦ㄥ浘鍍忎笂
+ cv2.putText(frame, f"score={str(score)}"
+ , (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)
+ # cv2.putText(frame, f"ratio={str(ratio)}"
+ # , (10, 70), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)
+ # cv2.putText(frame, f"edge_gradient_score={str(edge_gradient_score)}"
+ # , (10, 110), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)
+ # 鏄剧ず鐢婚潰
+ cv2.imshow("Camera", frame)
+ # 妫�娴嬫寜閿紝濡傛灉鎸変笅q閿垯閫�鍑哄惊鐜�
+ if cv2.waitKey(1) & 0xFF == ord('q'):
+ break
+ # 鍏抽棴鎽勫儚澶�
+ capture.release()
+ # 鍏抽棴鎵�鏈夌獥鍙�
+ cv2.destroyAllWindows()
+
+
+if __name__ == '__main__':
+ testOne()
+ # batch_score()
+ # cam_score()
\ No newline at end of file
diff --git a/model/herb_identify.onnx b/model/herb_identify.onnx
new file mode 100644
index 0000000..4f61333
--- /dev/null
+++ b/model/herb_identify.onnx
Binary files differ
diff --git a/model/loading.onnx b/model/loading.onnx
new file mode 100644
index 0000000..9eb4848
--- /dev/null
+++ b/model/loading.onnx
Binary files differ
diff --git a/test_herb_identify.py b/test_herb_identify.py
new file mode 100644
index 0000000..7a5a342
--- /dev/null
+++ b/test_herb_identify.py
@@ -0,0 +1,32 @@
+
+import os
+import cv2
+import numpy as np
+import onnxruntime
+
+if __name__ == '__main__':
+ img = cv2.imread("data/images/2/20250114200010.jpg")
+ identify_session = onnxruntime.InferenceSession("model/herb_identify.onnx")
+ herbs = eval(identify_session.get_modelmeta().custom_metadata_map['names'])
+ # 棰勫鐞�
+ herb_blob = cv2.dnn.blobFromImage(img, 1 / 255.0, (640, 640), swapRB=True, crop=False)
+ # 妯″瀷鎺ㄧ悊
+ herb_outputs = identify_session.run(None, {identify_session.get_inputs()[0].name: herb_blob})
+ herb_probabilities = herb_outputs[0]
+
+ top_five_classes = np.argsort(herb_probabilities, axis=1)[0][-5:][::-1]
+ name = ""
+ for i, class_id in enumerate(top_five_classes):
+ # 淇濈暀涓や綅灏忔暟
+ probability = round(herb_probabilities[0][class_id], 2)
+
+ herb_class = herbs[class_id]
+ name = name + " " + herb_class + ":" + str(probability)
+ # 灏嗚瘑鍒粨鏋滄墦鍗板埌鍥剧墖涓�
+ cv2.putText(img, herb_class + ":" + str(probability), (10, (i+1)*40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
+
+ # 鏄剧ず鍥剧墖
+ cv2.imshow("Output", img)
+ print(name)
+ # 绛夊緟
+ cv2.waitKey(0)
--
Gitblit v1.9.3